From 36cd329c9bd4ab41e5d8c16afcefda930dd0dbf7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EA=B3=BD=EC=83=81=EC=9A=B1?= <37833373+Trigger21@users.noreply.github.com> Date: Wed, 2 May 2018 10:45:46 +0900 Subject: [PATCH 01/36] Add files via upload --- x_ysw/pipe.py | 136 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 136 insertions(+) create mode 100644 x_ysw/pipe.py diff --git a/x_ysw/pipe.py b/x_ysw/pipe.py new file mode 100644 index 0000000..72d9737 --- /dev/null +++ b/x_ysw/pipe.py @@ -0,0 +1,136 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Sun Apr 29 05:53:22 2018 +@author: itwill03 +""" + + +import numpy as np +import os +import glob +import tensorflow as tf +import pandas as pd +from sklearn.model_selection import train_test_split + +tf.set_random_seed(777) + +#train_info = pd.read_csv("/home/itwill03/sound/train.csv",delimiter=',') +#train_data = np.genfromtxt("/home/itwill03/sound/yy/feature_train.csv", delimiter=',') +train_info = pd.read_csv("C:\data\sound/train.csv",delimiter=',') +train_data = np.genfromtxt("C:\data\sound\mel_train2.csv", delimiter=',') +train_data.shape #Out[16]: (50, 16384) + +#label set +labels = train_info['label'] +df_label = pd.DataFrame(labels) +l = train_info['label'].unique() + +for i in range(len(l)): + df_label[df_label==l[i]] = i + +#train data set +train_data = pd.DataFrame(train_data) +train_data['label']=df_label +train_data = train_data.astype(np.float32) +train_data.shape #Out[20]: (50, 16385) +#np.savetxt("c:/data/sound/train_data.csv",train_data, delimiter=",") + +#훈련세트, validation세트 나누기 +from sklearn.model_selection import train_test_split +train_set, validate_set = train_test_split(train_data, test_size = 0.3) +trainData = train_set.values[:,0:16384] +trainLabel = train_set.values[:,-1] +validateData = validate_set.values[:,0:16384] +validataLabel = validate_set.values[:,-1] + +#print (trainData.shape,trainLabel.shape,validateData.shape,validataLabel.shape) + +tf.reset_default_graph() + +# 텐서플로우 모델 생성 +n_dim = 16384 +n_classes = 41 +training_epochs = 10 +learning_rate = 0.001 +batch_size = 100 +steps_for_validate = 5 +keep_prob = tf.placeholder(tf.float32) + +X = tf.placeholder(tf.float32, [None, n_dim]) +#X_img = tf.reshape(X, [-1, 128, 128, 1]) +Y = tf.placeholder(tf.int32, [None, 1]) +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name='p_keep_conv') +p_keep_hidden = tf.placeholder(tf.float32, name='p_keep_hidden') + + + +# img shape = (?, 128, 128, 1) +c1 = tf.layers.conv2d(tf.reshape(X, [-1, 128, 128, 1]), 32, kernel_size=[4, 4], strides=(1, 1), + padding='same', activation=tf.nn.elu, name="c1") +p1 = tf.layers.max_pooling2d(inputs=c1, pool_size=[5, 2], strides=(5,2)) +p1 = tf.nn.dropout(p1, p_keep_conv) + +# img shape = (?, 64, 64, 32) +c2 = tf.layers.conv2d(tf.reshape(p1, [-1, 25, 64, 32]), 64, kernel_size=[4, 4], strides=(1, 1), + padding='same', activation=tf.nn.elu, name="c2") +p2 = tf.layers.max_pooling2d(inputs=c2, pool_size=[2, 2], strides=2) #shape = [?, 1, 48, 100] +p2 = tf.nn.dropout(p2, p_keep_conv) + +# img shape = (?, 32, 32, 64) +c3 = tf.layers.conv2d(tf.reshape(p2, [-1, 12, 32, 64]), 128, kernel_size=[4, 4], strides=(1, 1), + padding='same', activation=tf.nn.elu, name="c3") +p3 = tf.layers.max_pooling2d(inputs=c3, pool_size=[2, 2], strides=2) #shape = [?, 1, 24, 200] +p3 = tf.nn.dropout(p3, p_keep_conv) + +L4_flat = tf.reshape(p3, shape=[-1, 6*16*128]) +W1 = tf.get_variable("W1", shape=[6*16*128, 64], initializer=tf.contrib.layers.xavier_initializer()) +L5 = tf.nn.relu(tf.matmul(L4_flat, W1)) +L5 = tf.nn.dropout(L5, p_keep_hidden) + +W2 = tf.get_variable("W2", shape=[64,41],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L5, W2) + b + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +saver = tf.train.Saver() + + +# train my model + +file_list = glob.glob("C:\data\sound\mel/train_*.csv") +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + for n in range(len(file_list)): + data = file_list[n] + train_data = pd.DataFrame(np.genfromtxt(data, delimiter=',')) + train_set, validate_set = train_test_split(train_data, test_size = 0.3) + trainData = train_set.values[:,0:16384] + trainLabel = train_set.values[:,-1] + validateData = validate_set.values[:,0:16384] + validataLabel = validate_set.values[:,-1] + total_batch = int(len(trainData) / batch_size) + for i in range(total_batch): + batch_xs = trainData[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .7, p_keep_hidden: .5} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + if epoch % steps_for_validate == steps_for_validate-1: + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + print('Accuracy:', sess.run(accuracy, feed_dict={ X: validateData, Y: validataLabel.reshape(-1, 1), + p_keep_conv: 1, p_keep_hidden: 1})) +print('Finished!') + + From 39991995f3704576de128900ca95e3bcdaa9e0d9 Mon Sep 17 00:00:00 2001 From: smallerhand Date: Wed, 2 May 2018 10:48:43 +0900 Subject: [PATCH 02/36] all --- x_ksh/SA_cnn_ps3.py | 24 ++++++++-------- x_ksh/mfcc_processing.py | 61 ++++++++++++++++++++-------------------- x_ksh/stft_processing.py | 2 +- 3 files changed, 44 insertions(+), 43 deletions(-) diff --git a/x_ksh/SA_cnn_ps3.py b/x_ksh/SA_cnn_ps3.py index 39e211b..f9d6324 100644 --- a/x_ksh/SA_cnn_ps3.py +++ b/x_ksh/SA_cnn_ps3.py @@ -12,14 +12,14 @@ tf.set_random_seed(777) trainData = np.genfromtxt('/home/paperspace/Downloads/trainData5.csv', delimiter=',') -trainData = trainData.reshape(-1, 20, 430) +trainData = trainData.reshape(-1, 20, 200) testData = np.genfromtxt('/home/paperspace/Downloads/testData5.csv', delimiter=',') -testData = testData.reshape(-1, 20, 430) +testData = testData.reshape(-1, 20, 200) trainLabel = np.genfromtxt('/home/paperspace/Downloads/trainLabel5.csv', delimiter=',') testLabel = np.genfromtxt('/home/paperspace/Downloads/testLabel5.csv', delimiter=',') print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape) -# (6631, 20, 430) (2842, 20, 430) (6631,) (2842,) +# (6631, 20, 200) (2842, 20, 200) (6631,) (2842,) #다시 돌릴때는 여기부터 tf.reset_default_graph() #그래프 초기화 @@ -31,40 +31,40 @@ steps_for_validate = 5 #placeholder -X = tf.placeholder(tf.float32, [None, 20, 430], name="X") -X_sound = tf.reshape(X, [-1, 20, 430, 1]) # 20*100*1 (frequency, time, amplitude) +X = tf.placeholder(tf.float32, [None, 20, 200], name="X") +X_sound = tf.reshape(X, [-1, 20, 200, 1]) Y = tf.placeholder(tf.int32, [None, 1], name="Y") Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv") p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") # L1 SoundIn shape=(?, 20, 430, 1) -W1 = tf.get_variable("W1", shape=[2, 43, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +W1 = tf.get_variable("W1", shape=[2, 20, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME') L1 = tf.nn.elu(L1) -L1 = tf.nn.max_pool(L1, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') +L1 = tf.nn.max_pool(L1, ksize=[1, 2, 20, 1],strides=[1, 2, 20, 1], padding='SAME') L1 = tf.nn.dropout(L1, p_keep_conv) # L2 Input shape=(?,10,21,32) -W2 = tf.get_variable("W2", shape=[3, 60, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) +W2 = tf.get_variable("W2", shape=[3, 3, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') L2 = tf.nn.elu(L2) L2 = tf.nn.max_pool(L2, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') L2 = tf.nn.dropout(L2, p_keep_conv) # L3 Input shape=(?,3,12,64) -W3 = tf.get_variable("W3", shape=[3, 48, 64, 128],initializer=tf.contrib.layers.xavier_initializer()) +W3 = tf.get_variable("W3", shape=[3, 3, 64, 128],initializer=tf.contrib.layers.xavier_initializer()) L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') L3 = tf.nn.elu(L3) L3 = tf.nn.max_pool(L3, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') L3 = tf.nn.dropout(L3, p_keep_conv) -L3_flat= tf.reshape(L3, shape=[-1, 16*128]) +L3_flat= tf.reshape(L3, shape=[-1, 2*2*128]) # Final FC 2*3*128 inputs -> 41 outputs -W4 = tf.get_variable("W4", shape=[16*128, 625],initializer=tf.contrib.layers.xavier_initializer()) +W4 = tf.get_variable("W4", shape=[2*2*128, 512],initializer=tf.contrib.layers.xavier_initializer()) L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) L4 = tf.nn.dropout(L4, p_keep_hidden) -W_o = tf.get_variable("W_o", shape=[625,41],initializer=tf.contrib.layers.xavier_initializer()) +W_o = tf.get_variable("W_o", shape=[512,41],initializer=tf.contrib.layers.xavier_initializer()) b = tf.Variable(tf.random_normal([41])) logits = tf.matmul(L4, W_o) + b diff --git a/x_ksh/mfcc_processing.py b/x_ksh/mfcc_processing.py index 55a7957..e1a93bb 100644 --- a/x_ksh/mfcc_processing.py +++ b/x_ksh/mfcc_processing.py @@ -26,7 +26,7 @@ testLabel = test_set.values[:,1] #data load and extract mfcc (scaling indluded) -path = '/Users/kimseunghyuck/desktop/audio_train/' +path = '/Users/kimseunghyuck/desktop/' #path = '/home/paperspace/Downloads/audio_train/' def see_how_long(file): @@ -54,38 +54,39 @@ def see_how_long(file): plt.plot(mfcc[2,]) plt.plot(np.abs(mfcc[3,])) -#5 seconds(430 segments) extract -def five_sec_extract(file): - #zero padding to file.shape[0] X 40 X 3000 +#2 seconds(200 segments) extract +def two_sec_extract(file): + #zero padding to file.shape[0] X 20 X 200 n=file.shape[0] - array = np.repeat(0, n * 20 * 430).reshape(n, 20, 430) - k=0 - for filename in file: - y, sr = sf.read(path+filename, dtype='float32') + array = np.repeat(0., n * 20 * 200).reshape(n, 20, 200) + k=0 + see = [] + for filename in file: + y, sr = librosa.core.load(path+'audio_train/'+filename, + mono=True, res_type="kaiser_fast") mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) length=mfcc.shape[1] - abs_mfcc=np.abs(mfcc) - if length == 430: + if length == 200: array[k, :, :]=mfcc - elif length < 430: + elif length < 200: array[k, :, :length]=mfcc - elif length > 430: - argmax=np.argmax(abs_mfcc, axis=1) - sample=[] - for i in range(np.max(argmax)): - sample.append(np.sum((argmax>=i) & (argmax 200: + sample = np.repeat(0., (length - 200)*20).reshape(20,length - 200) + for j in range(length - 200): + for i in range(20): + sample[i,j]=np.var(mfcc[i,j:j+200]) + A=np.argmax(sample, axis=1) + start=np.argmax(np.bincount(A)) + array[k, :, :]=mfcc[:, start:start+200] + see.append(start) k+=1 - array=array.reshape(-1,20*430) - array=((array-np.mean(array))/np.std(array)).reshape(-1,20,430) - return(array) + return(array, see) -trainData=five_sec_extract(trainfile) -testData=five_sec_extract(testfile) +trainData, see1=two_sec_extract(trainfile) +testData, see2=two_sec_extract(testfile) print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape) -# (6631, 20, 430) (2842, 20, 430) (6631,) (2842,) +# (6631, 20, 200) (2842, 20, 200) (6631,) (2842,) #how many kinds of label? print(len(np.unique(trainLabel))) #41 @@ -107,14 +108,14 @@ def Labeling(label): #0 40 0 40 #csv downdload totally about 600MB -trainData2D=trainData.reshape(-1, 20*430) -testData2D=testData.reshape(-1, 20*430) -np.savetxt('/Users/kimseunghyuck/desktop/trainData5.csv', +trainData2D=trainData.reshape(-1, 20*200) +testData2D=testData.reshape(-1, 20*200) +np.savetxt(path+'trainData6.csv', trainData2D, delimiter=",") -np.savetxt('/Users/kimseunghyuck/desktop/testData5.csv', +np.savetxt(path+'testData6.csv', testData2D, delimiter=",") -np.savetxt('/Users/kimseunghyuck/desktop/trainLabel5.csv', +np.savetxt(path+'trainLabel6.csv', trainLabel, delimiter=",") -np.savetxt('/Users/kimseunghyuck/desktop/testLabel5.csv', +np.savetxt(path+'testLabel6.csv', testLabel, delimiter=",") diff --git a/x_ksh/stft_processing.py b/x_ksh/stft_processing.py index 9500b35..2495f51 100644 --- a/x_ksh/stft_processing.py +++ b/x_ksh/stft_processing.py @@ -59,7 +59,7 @@ def two_sec_extract(file): array = np.repeat(0., n * 513 * 100).reshape(n, 513, 100) k=0 for filename in file: - y, sr = librosa.core.load(path+filename, mono=True, res_type="kaiser_fast") + y, sr = librosa.core.load(path+filename, mono=True, res_type="kaaiser_fast") stft=librosa.core.stft(y,1024,512) length=stft.shape[1] if length == 100: From 2483153baba31ecaef2bd4db3ac4ba2eb9e7234e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EA=B3=BD=EC=83=81=EC=9A=B1?= <37833373+Trigger21@users.noreply.github.com> Date: Wed, 2 May 2018 10:49:45 +0900 Subject: [PATCH 03/36] Create cound_cnn_ver_kaggle2.py --- x_ksw/New Project/cound_cnn_ver_kaggle2.py | 314 +++++++++++++++++++++ 1 file changed, 314 insertions(+) create mode 100644 x_ksw/New Project/cound_cnn_ver_kaggle2.py diff --git a/x_ksw/New Project/cound_cnn_ver_kaggle2.py b/x_ksw/New Project/cound_cnn_ver_kaggle2.py new file mode 100644 index 0000000..9ba752f --- /dev/null +++ b/x_ksw/New Project/cound_cnn_ver_kaggle2.py @@ -0,0 +1,314 @@ +import numpy as np +np.random.seed(1001) +tf.set_random_seed(1001) +import tensorflow as tf +import os +import shutil +import IPython +import matplotlib +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns +import glob +from sklearn.cross_validation import StratifiedKFold + +%matplotlib inline +matplotlib.style.use('ggplot') + +173*2 +# Preparing data + +class Config(object): + def __init__(self, + sampling_rate=16000, audio_duration=2, n_classes=41, + use_mfcc=False, n_folds=10, learning_rate=0.0001, + max_epochs=50, n_mfcc=20): + self.sampling_rate = sampling_rate + self.audio_duration = audio_duration + self.n_classes = n_classes + self.use_mfcc = use_mfcc + self.n_mfcc = n_mfcc + self.n_folds = n_folds + self.learning_rate = learning_rate + self.max_epochs = max_epochs + + self.audio_length = self.sampling_rate * self.audio_duration*2+4 + if self.use_mfcc: + self.dim = (self.n_mfcc, 6 + int(np.floor(self.audio_length/512)), 1) + else: + self.dim = (self.audio_length, 1) + +config = Config(sampling_rate=44100, audio_duration=2, n_folds=10, + learning_rate=0.001, use_mfcc=True, n_mfcc=40) + +def prepare_data(df, config, data_dir): + X = np.empty(shape=(df.shape[0], config.dim[0], config.dim[1], 1)) + input_length = config.audio_length + for i, fname in enumerate(df['fname']): + print(fname) + file_path = data_dir + fname + data, _ = librosa.core.load(file_path, sr=config.sampling_rate, res_type="kaiser_fast") + + # Random offset / Padding + if len(data) > input_length: + max_offset = len(data) - input_length + offset = np.random.randint(max_offset) + data = data[offset:(input_length+offset)] + else: + if input_length > len(data): + max_offset = input_length - len(data) + offset = np.random.randint(max_offset) + else: + offset = 0 + data = np.pad(data, (offset, input_length - len(data) - offset), "constant") + + data = librosa.feature.mfcc(data, sr=config.sampling_rate, n_mfcc=config.n_mfcc) + data = np.expand_dims(data, axis=-1) + X[i,] = data + return X + +#file = "/home/itwill03/sound/audio_train/*.wav" +file = "c:/sound/audio_train/*.wav" +train_list=glob.glob(file) + +#file = "/home/itwill03/sound/audio_test/*.wav" +#test_list=glob.glob(file) + +train = pd.read_csv("c:/sound/train.csv") +#train = pd.read_csv("/home/itwill03/sound/train.csv") + +labels = train['label'] +l = train['label'].unique() + +df_label = pd.DataFrame(labels) + +for i in range(len(l)): + df_label[df_label==l[i]] = int(i) +df_label.values + +df = pd.concat([train, df_label], axis=1) + +from sklearn.model_selection import train_test_split +train_set, test_set = train_test_split(df, test_size = 0.3) +trainfile = train_set['fname'] +testfile = test_set['fname'] +trainLabel = train_set.values[:,-1] +testLabel = test_set.values[:,-1] + +train.shape[0] +config.dim[0] +config.dim[1] +#X = np.empty(shape=(9473, 40, 350, 1)) # 엔트리를 초기화 하지 않고 값을 반환 + +train.index +train.index[1] + +config.audio_length + + +X_train = prepare_data(train_set, config, 'c:/sound/audio_train/') +X_test = prepare_data(test_set, config, 'c:/sound/audio_train/') + +#X_train = prepare_data(train_set, config, '/home/itwill03/sound/audio_train/') +#X_test = prepare_data(test_set, config, '/home/itwill03/sound/audio_train/') + +#Normalization + +mean = np.mean(X_train, axis=0) +std = np.std(X_train, axis=0) + +X_train = (X_train - mean)/std +X_test = (X_test - mean)/std + +X_train.shape # (6631, 40, 350, 1) +X_test.shape # (2842, 40, 350, 1) + +trainData = X_train.reshape(-1, 40, 173) +testData = X_test.reshape(-1, 40, 173) + +trainData.shape # (6631, 40, 350) +testData.shape # (2842, 40, 350) +trainLabel.shape # (6631,) +testLabel.shape # (2842,) + +# start +tf.reset_default_graph() #그래프 초기화 + +# hyper parameters +learning_rate = 0.0001 +training_epochs = 700 +batch_size = 100 +steps_for_validate = 5 + +#placeholder +X = tf.placeholder(tf.float32, [None, 40, 350,1], name="X") +#X_sound = tf.reshape(X, [-1, 40, 173, 1]) # 40*350*1 (frequency, time, amplitude) +Y = tf.placeholder(tf.int32, [None, 1], name="Y") +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv") +p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") + +# L1 SoundIn shape=(?, 40, 350, 1) +W1 = tf.get_variable("W1", shape=[4, 35, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +L1 = tf.nn.conv2d(X, W1, strides=[1, 1, 1, 1], padding='SAME') +L1 = tf.layers.batch_normalization(L1) +#L1 = tf.nn.relu(L1) +#L1 = tf.nn.elu(L1) +L1 = tf.nn.leaky_relu(L1,0.1) +L1 = tf.nn.max_pool(L1, ksize=[1, 4, 35, 1],strides=[1, 4, 35, 1], padding='SAME') +#L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L1 = tf.nn.dropout(L1, p_keep_conv) + +# L2 Input shape=(?,10,10,32) +W2 = tf.get_variable("W2", shape=[3, 3, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) +L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') +L2 = tf.layers.batch_normalization(L2) +#L2 = tf.nn.relu(L2) +#L2 = tf.nn.elu(L2) +L2 = tf.nn.leaky_relu(L2,0.1) +L2 = tf.nn.max_pool(L2, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') +#L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L2 = tf.nn.dropout(L2, p_keep_conv) + +# L3 Input shape=(?,4,4,64) +W3 = tf.get_variable("W3", shape=[3, 3, 64, 128],initializer=tf.contrib.layers.xavier_initializer()) +L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') +L3 = tf.layers.batch_normalization(L3) +#L3 = tf.nn.relu(L3) +#L3 = tf.nn.elu(L3) +L3 = tf.nn.leaky_relu(L3,0.1) +L3 = tf.nn.max_pool(L3, ksize=[1, 3, 3, 1],strides=[1, 2, 2, 1], padding='SAME') +#L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L3 = tf.nn.dropout(L3, p_keep_conv) +L3_flat= tf.reshape(L3, shape=[-1, 2*2*128]) + +# Final FC 2*2*128 inputs -> 41 outputs +W4 = tf.get_variable("W4", shape=[2*2*128, 410],initializer=tf.contrib.layers.xavier_initializer()) +#L4 = tf.nn.relu(tf.matmul(L3_flat, W4)) +#L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) +L4 = tf.nn.leaky_relu(tf.matmul(L3_flat, W4),0.1) +L4 = tf.nn.dropout(L4, p_keep_hidden) +W_o = tf.get_variable("W_o", shape=[410,41],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L4, W_o) + b + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +#saver = tf.train.Saver() + +# train my model +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = int(len(X_train) / batch_size) + for i in range(total_batch): + batch_xs = X_train[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .8, p_keep_hidden: 0.7} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + if epoch % steps_for_validate == steps_for_validate-1: + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + print('Accuracy:', sess.run(accuracy, feed_dict={ + X: X_test, Y: testLabel.reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + #save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') +print('Finished!') + + +trainData.shape +batch_xs.shape +batch_ys.shape + +sess.close() + + +""" +1) con2d layer * 3 + FC +lr=0.001, epoch = 100 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (4, 10, 32), (4, 10, 32), (4, 10, 32) +FC : (?, 64), (64, 41) +max_pool : (2,2), (2,2), (3,3) +accuracy: 51~60% +2) con2d layer * 3 + FC +lr=0.001, epoch = 100 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (4, 10, 32), (4, 10, 32), (4, 10, 32) +FC : (2*8*32, 64), (64, 41) +max_pool : (2,2), (2,2), (2,2) +accuracy: 55~60% +3) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.063까지 나옴) 정확도는 그대로임 +lr=0.0002, epoch = 300 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (4, 10, 32), (4, 10, 32), (4, 10, 32) +FC : (2*8*32, 64), (64, 41) +max_pool : (2,2), (2,2), (2,2) +accuracy: 54~55% +4) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.012까지 나옴) 정확도는 그대로임 +lr=0.0002, epoch = 300 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (2, 8, 32), (3, 3, 64), (3, 3, 128) +FC : (3*3*128, 512), (512, 41) +max_pool : (2,8), (3,3), (3,3) +accuracy: 61~63% +5) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.0016까지 나옴) 정확도는 그대로임 +lr=0.0002, epoch = 300 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (3, 13, 32), (3, 3, 64), (3, 3, 128) +FC : (5*22*128, 625), (625, 41) +max_pool : (2,2), (2,2), (2,2) +accuracy: 53~56% +6) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.16까지 나옴) 정확도는 그대로임 +lr=0.00008, epoch = 300 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (2, 10, 32), (3, 3, 64), (3, 3, 128) +FC : (3*2*128, 625), (625, 41) +max_pool : (2,10), (3,3), (3,3) +accuracy: 65~67% +7) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.13까지 나옴) 정확도는 그대로임 +lr=0.00005, epoch = 500 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (2, 10, 32), (3, 3, 64), (3, 3, 128) +FC : (3*2*128, 625), (625, 41) +max_pool : (2,10), (3,3), (3,3) +accuracy: 65~66% +8) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.008까지 나옴) 정확도는 그대로임 +lr=0.00005, epoch = 400 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (3, 3, 32), (3, 3, 64), (3, 3, 128) +FC : (5*5*128, 1000), (1000, 41) +max_pool : (2,10), (2,2), (2,2) +accuracy: 60~61% +9) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.22까지 나옴) 정확도는 그대로임 +lr=0.00005, epoch = 500 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (2, 10, 32), (3, 3, 64), (3, 3, 128) +FC : (5*1*128, 650), (650, 41) +max_pool : (2,10), (2,10), (3,3) +accuracy: 60~61% +10) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.22까지 나옴) 정확도는 그대로임 +lr=0.00005, epoch = 500 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (2, 10, 32), (3, 3, 64), (3, 3, 128) +FC : (10*10*128, 650), (650, 41) +max_pool : (2,9), (2,2), (1,1) +accuracy: 59~60% +11) + cost = 0.024251443 + Accuracy: 0.64% ~ 0.65% +12) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.024까지 나옴) 정확도는 그대로임 +lr=0.0002, epoch = 700 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (4, 10, 32), (3, 3, 64), (3, 3, 128) +FC : (5*6*128, 650), (650, 41) +max_pool : (4,10), (2,2), (2,2) +accuracy: 61~62% +""" From b66279d1541657b192d3ce6f7c8438472ec77d7e Mon Sep 17 00:00:00 2001 From: suwonyu <37686127+suwonyu@users.noreply.github.com> Date: Wed, 2 May 2018 11:22:22 +0900 Subject: [PATCH 04/36] Add files via upload --- x_ysw/pipe.py | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/x_ysw/pipe.py b/x_ysw/pipe.py index 72d9737..7c36d06 100644 --- a/x_ysw/pipe.py +++ b/x_ysw/pipe.py @@ -14,7 +14,7 @@ from sklearn.model_selection import train_test_split tf.set_random_seed(777) - +""" #train_info = pd.read_csv("/home/itwill03/sound/train.csv",delimiter=',') #train_data = np.genfromtxt("/home/itwill03/sound/yy/feature_train.csv", delimiter=',') train_info = pd.read_csv("C:\data\sound/train.csv",delimiter=',') @@ -45,7 +45,7 @@ validataLabel = validate_set.values[:,-1] #print (trainData.shape,trainLabel.shape,validateData.shape,validataLabel.shape) - +""" tf.reset_default_graph() # 텐서플로우 모델 생성 @@ -67,25 +67,25 @@ # img shape = (?, 128, 128, 1) -c1 = tf.layers.conv2d(tf.reshape(X, [-1, 128, 128, 1]), 32, kernel_size=[4, 4], strides=(1, 1), +c1 = tf.layers.conv2d(tf.reshape(X, [-1, 128, 128, 1]), 32, kernel_size=[4, 4], strides=(2, 2), padding='same', activation=tf.nn.elu, name="c1") -p1 = tf.layers.max_pooling2d(inputs=c1, pool_size=[5, 2], strides=(5,2)) +p1 = tf.layers.max_pooling2d(inputs=c1, pool_size=[2, 2], strides=(2,2)) p1 = tf.nn.dropout(p1, p_keep_conv) # img shape = (?, 64, 64, 32) -c2 = tf.layers.conv2d(tf.reshape(p1, [-1, 25, 64, 32]), 64, kernel_size=[4, 4], strides=(1, 1), +c2 = tf.layers.conv2d(tf.reshape(p1, [-1, 32, 32, 32]), 64, kernel_size=[4, 4], strides=(1, 1), padding='same', activation=tf.nn.elu, name="c2") p2 = tf.layers.max_pooling2d(inputs=c2, pool_size=[2, 2], strides=2) #shape = [?, 1, 48, 100] p2 = tf.nn.dropout(p2, p_keep_conv) # img shape = (?, 32, 32, 64) -c3 = tf.layers.conv2d(tf.reshape(p2, [-1, 12, 32, 64]), 128, kernel_size=[4, 4], strides=(1, 1), +c3 = tf.layers.conv2d(tf.reshape(p2, [-1, 16, 16, 64]), 128, kernel_size=[4, 4], strides=(1, 1), padding='same', activation=tf.nn.elu, name="c3") p3 = tf.layers.max_pooling2d(inputs=c3, pool_size=[2, 2], strides=2) #shape = [?, 1, 24, 200] p3 = tf.nn.dropout(p3, p_keep_conv) -L4_flat = tf.reshape(p3, shape=[-1, 6*16*128]) -W1 = tf.get_variable("W1", shape=[6*16*128, 64], initializer=tf.contrib.layers.xavier_initializer()) +L4_flat = tf.reshape(p3, shape=[-1, 8*8*128]) +W1 = tf.get_variable("W1", shape=[8*8*128, 64], initializer=tf.contrib.layers.xavier_initializer()) L5 = tf.nn.relu(tf.matmul(L4_flat, W1)) L5 = tf.nn.dropout(L5, p_keep_hidden) @@ -132,5 +132,3 @@ print('Accuracy:', sess.run(accuracy, feed_dict={ X: validateData, Y: validataLabel.reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) print('Finished!') - - From 5ef04222801d1316223f8182fa7975dcd26c59e2 Mon Sep 17 00:00:00 2001 From: suwonyu <37686127+suwonyu@users.noreply.github.com> Date: Wed, 2 May 2018 11:45:10 +0900 Subject: [PATCH 05/36] Add files via upload --- x_ysw/pipe.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/x_ysw/pipe.py b/x_ysw/pipe.py index 7c36d06..cdc051c 100644 --- a/x_ysw/pipe.py +++ b/x_ysw/pipe.py @@ -126,7 +126,7 @@ c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) avg_cost += c / total_batch print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) - if epoch % steps_for_validate == steps_for_validate-1: + if epoch % steps_for_validate == steps_for_validate: correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print('Accuracy:', sess.run(accuracy, feed_dict={ X: validateData, Y: validataLabel.reshape(-1, 1), From 93777596d02110f66f4ba9836de57df9ffa87d4f Mon Sep 17 00:00:00 2001 From: smallerhand Date: Wed, 2 May 2018 12:01:29 +0900 Subject: [PATCH 06/36] all --- x_ksh/SA_cnn_ps3.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/x_ksh/SA_cnn_ps3.py b/x_ksh/SA_cnn_ps3.py index f9d6324..6264eb9 100644 --- a/x_ksh/SA_cnn_ps3.py +++ b/x_ksh/SA_cnn_ps3.py @@ -11,12 +11,12 @@ import tensorflow as tf tf.set_random_seed(777) -trainData = np.genfromtxt('/home/paperspace/Downloads/trainData5.csv', delimiter=',') +trainData = np.genfromtxt('/home/paperspace/Downloads/trainData6.csv', delimiter=',') trainData = trainData.reshape(-1, 20, 200) -testData = np.genfromtxt('/home/paperspace/Downloads/testData5.csv', delimiter=',') +testData = np.genfromtxt('/home/paperspace/Downloads/testData6.csv', delimiter=',') testData = testData.reshape(-1, 20, 200) -trainLabel = np.genfromtxt('/home/paperspace/Downloads/trainLabel5.csv', delimiter=',') -testLabel = np.genfromtxt('/home/paperspace/Downloads/testLabel5.csv', delimiter=',') +trainLabel = np.genfromtxt('/home/paperspace/Downloads/trainLabel6.csv', delimiter=',') +testLabel = np.genfromtxt('/home/paperspace/Downloads/testLabel6.csv', delimiter=',') print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape) # (6631, 20, 200) (2842, 20, 200) (6631,) (2842,) @@ -25,8 +25,8 @@ tf.reset_default_graph() #그래프 초기화 # hyper parameters -learning_rate = 0.0004 -training_epochs = 400 +learning_rate = 0.0002 +training_epochs = 700 batch_size = 200 steps_for_validate = 5 From 7b47275a47e5adb643571f21aa1b221fa3ed5b2a Mon Sep 17 00:00:00 2001 From: suwonyu <37686127+suwonyu@users.noreply.github.com> Date: Wed, 2 May 2018 12:20:11 +0900 Subject: [PATCH 07/36] Add files via upload --- x_ysw/pipe.py | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/x_ysw/pipe.py b/x_ysw/pipe.py index cdc051c..2e97792 100644 --- a/x_ysw/pipe.py +++ b/x_ysw/pipe.py @@ -69,6 +69,7 @@ # img shape = (?, 128, 128, 1) c1 = tf.layers.conv2d(tf.reshape(X, [-1, 128, 128, 1]), 32, kernel_size=[4, 4], strides=(2, 2), padding='same', activation=tf.nn.elu, name="c1") +n1 = tf.layers.batch_normalization(c1) p1 = tf.layers.max_pooling2d(inputs=c1, pool_size=[2, 2], strides=(2,2)) p1 = tf.nn.dropout(p1, p_keep_conv) @@ -108,8 +109,9 @@ file_list = glob.glob("C:\data\sound\mel/train_*.csv") print('Learning started. It takes sometime.') + for epoch in range(training_epochs): - avg_cost = 0 + avg_cost = 0 for n in range(len(file_list)): data = file_list[n] train_data = pd.DataFrame(np.genfromtxt(data, delimiter=',')) @@ -125,10 +127,15 @@ feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .7, p_keep_hidden: .5} c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) avg_cost += c / total_batch - print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) - if epoch % steps_for_validate == steps_for_validate: + print('Epoch:', '%04d' % (epochs + 1), 'cost =', '{:.9f}'.format(avg_cost)) + if epoch % steps_for_validate == steps_for_validate-1: correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) - print('Accuracy:', sess.run(accuracy, feed_dict={ X: validateData, Y: validataLabel.reshape(-1, 1), - p_keep_conv: 1, p_keep_hidden: 1})) + x=np.random.choice(testLabel.shape[0], 500, replace=False) + print('Accuracy:', sess.run(accuracy, feed_dict={ + X: testData[x], Y: testLabel[x].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + #save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') print('Finished!') + + + From 13184916bc2f92de984674994cd5612fec01e7fb Mon Sep 17 00:00:00 2001 From: suwonyu <37686127+suwonyu@users.noreply.github.com> Date: Wed, 2 May 2018 12:23:01 +0900 Subject: [PATCH 08/36] Add files via upload --- x_ysw/mel_cnn2.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/x_ysw/mel_cnn2.py b/x_ysw/mel_cnn2.py index 1c4b165..535cefe 100644 --- a/x_ysw/mel_cnn2.py +++ b/x_ysw/mel_cnn2.py @@ -65,7 +65,8 @@ # img shape = (?, 128, 128, 1) c1 = tf.layers.conv2d(tf.reshape(X, [-1, 128, 128, 1]), 32, kernel_size=[4, 4], strides=(1, 1), - padding='same', activation=tf.nn.elu, name="c1") + padding='same', activation=tf.nn.elu, name="c1") +n1 = tf.layers.batch_normalization(c1) p1 = tf.layers.max_pooling2d(inputs=c1, pool_size=[2, 2], strides=2) p1 = tf.nn.dropout(p1, p_keep_conv) @@ -117,8 +118,10 @@ if epoch % steps_for_validate == steps_for_validate-1: correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + x=np.random.choice(testLabel.shape[0], 500, replace=False) print('Accuracy:', sess.run(accuracy, feed_dict={ - X: validateData, Y: validataLabel.reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + X: testData[x], Y: testLabel[x].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + #save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') print('Finished!') tf.reset_default_graph() From c2e5db3fb39869e0b573ff2427f9761728d8d0f1 Mon Sep 17 00:00:00 2001 From: Kim Seunghyuck Date: Wed, 2 May 2018 01:10:21 -0400 Subject: [PATCH 09/36] Update sample.txt --- x_ksh/sample.txt | 956 +++++++++++++++++++++++------------------------ 1 file changed, 478 insertions(+), 478 deletions(-) diff --git a/x_ksh/sample.txt b/x_ksh/sample.txt index ca900b2..b580388 100644 --- a/x_ksh/sample.txt +++ b/x_ksh/sample.txt @@ -1,498 +1,498 @@ -Epoch: 0288 cost = 0.208524654 -Epoch: 0289 cost = 0.189347939 -Epoch: 0290 cost = 0.202346881 -Accuracy: 0.674 -Epoch: 0291 cost = 0.193577955 -Epoch: 0292 cost = 0.217119253 -Epoch: 0293 cost = 0.208007449 -Epoch: 0294 cost = 0.208467093 -Epoch: 0295 cost = 0.193879927 +Epoch: 0287 cost = 0.263603727 +Epoch: 0288 cost = 0.263932069 +Epoch: 0289 cost = 0.270318500 +Epoch: 0290 cost = 0.270442030 +Accuracy: 0.686 +Epoch: 0291 cost = 0.274197009 +Epoch: 0292 cost = 0.268625583 +Epoch: 0293 cost = 0.258771868 +Epoch: 0294 cost = 0.263313771 +Epoch: 0295 cost = 0.259434710 +Accuracy: 0.69 +Epoch: 0296 cost = 0.260218189 +Epoch: 0297 cost = 0.269922243 +Epoch: 0298 cost = 0.266892755 +Epoch: 0299 cost = 0.248503954 +Epoch: 0300 cost = 0.256831940 +Accuracy: 0.702 +Epoch: 0301 cost = 0.254391911 +Epoch: 0302 cost = 0.252349392 +Epoch: 0303 cost = 0.254330822 +Epoch: 0304 cost = 0.253464962 +Epoch: 0305 cost = 0.247570692 +Accuracy: 0.702 +Epoch: 0306 cost = 0.254490927 +Epoch: 0307 cost = 0.248884994 +Epoch: 0308 cost = 0.238015426 +Epoch: 0309 cost = 0.250835216 +Epoch: 0310 cost = 0.236978996 +Accuracy: 0.664 +Epoch: 0311 cost = 0.248637487 +Epoch: 0312 cost = 0.237585425 +Epoch: 0313 cost = 0.250483723 +Epoch: 0314 cost = 0.246652423 +Epoch: 0315 cost = 0.244677804 +Accuracy: 0.688 +Epoch: 0316 cost = 0.243980275 +Epoch: 0317 cost = 0.225476449 +Epoch: 0318 cost = 0.238543987 +Epoch: 0319 cost = 0.238320933 +Epoch: 0320 cost = 0.261743346 +Accuracy: 0.714 +Epoch: 0321 cost = 0.224811815 +Epoch: 0322 cost = 0.241242706 +Epoch: 0323 cost = 0.241196357 +Epoch: 0324 cost = 0.225010265 +Epoch: 0325 cost = 0.233388552 +Accuracy: 0.686 +Epoch: 0326 cost = 0.236988868 +Epoch: 0327 cost = 0.224576655 +Epoch: 0328 cost = 0.216505816 +Epoch: 0329 cost = 0.234341587 +Epoch: 0330 cost = 0.221781501 Accuracy: 0.698 -Epoch: 0296 cost = 0.207532348 -Epoch: 0297 cost = 0.195312364 -Epoch: 0298 cost = 0.177824780 -Epoch: 0299 cost = 0.188083763 -Epoch: 0300 cost = 0.181974903 -Accuracy: 0.646 -Epoch: 0301 cost = 0.200342787 -Epoch: 0302 cost = 0.188453357 -Epoch: 0303 cost = 0.201256883 -Epoch: 0304 cost = 0.201790317 -Epoch: 0305 cost = 0.195877376 -Accuracy: 0.658 -Epoch: 0306 cost = 0.189740593 -Epoch: 0307 cost = 0.187292852 -Epoch: 0308 cost = 0.187891593 -Epoch: 0309 cost = 0.175115969 -Epoch: 0310 cost = 0.184124405 -Accuracy: 0.674 -Epoch: 0311 cost = 0.179196475 -Epoch: 0312 cost = 0.181954299 -Epoch: 0313 cost = 0.171970832 -Epoch: 0314 cost = 0.168458790 -Epoch: 0315 cost = 0.181318909 +Epoch: 0331 cost = 0.231224662 +Epoch: 0332 cost = 0.234261046 +Epoch: 0333 cost = 0.230843339 +Epoch: 0334 cost = 0.216231964 +Epoch: 0335 cost = 0.215144413 +Accuracy: 0.704 +Epoch: 0336 cost = 0.205591729 +Epoch: 0337 cost = 0.228962516 +Epoch: 0338 cost = 0.230936582 +Epoch: 0339 cost = 0.224457032 +Epoch: 0340 cost = 0.216019266 Accuracy: 0.69 -Epoch: 0316 cost = 0.170158450 -Epoch: 0317 cost = 0.178810128 -Epoch: 0318 cost = 0.173557231 -Epoch: 0319 cost = 0.171763150 -Epoch: 0320 cost = 0.176990497 -Accuracy: 0.682 -Epoch: 0321 cost = 0.168191432 -Epoch: 0322 cost = 0.177624416 -Epoch: 0323 cost = 0.173962723 -Epoch: 0324 cost = 0.165333744 -Epoch: 0325 cost = 0.183993734 +Epoch: 0341 cost = 0.223690010 +Epoch: 0342 cost = 0.216028717 +Epoch: 0343 cost = 0.228029779 +Epoch: 0344 cost = 0.218507429 +Epoch: 0345 cost = 0.205987243 Accuracy: 0.704 -Epoch: 0326 cost = 0.182382465 -Epoch: 0327 cost = 0.166421612 -Epoch: 0328 cost = 0.171902532 -Epoch: 0329 cost = 0.159255681 -Epoch: 0330 cost = 0.158926720 -Accuracy: 0.698 -Epoch: 0331 cost = 0.161930311 -Epoch: 0332 cost = 0.164052966 -Epoch: 0333 cost = 0.162388876 -Epoch: 0334 cost = 0.159918311 -Epoch: 0335 cost = 0.164237844 -Accuracy: 0.686 -Epoch: 0336 cost = 0.169206282 -Epoch: 0337 cost = 0.160183274 -Epoch: 0338 cost = 0.146684959 -Epoch: 0339 cost = 0.158647120 -Epoch: 0340 cost = 0.144704580 -Accuracy: 0.682 -Epoch: 0341 cost = 0.152584301 -Epoch: 0342 cost = 0.152169802 -Epoch: 0343 cost = 0.155065432 -Epoch: 0344 cost = 0.158025604 -Epoch: 0345 cost = 0.148087940 -Accuracy: 0.692 -Epoch: 0346 cost = 0.147618784 -Epoch: 0347 cost = 0.151548640 -Epoch: 0348 cost = 0.144099430 -Epoch: 0349 cost = 0.145818583 -Epoch: 0350 cost = 0.143193354 -Accuracy: 0.712 -Epoch: 0351 cost = 0.142278598 -Epoch: 0352 cost = 0.147256077 -Epoch: 0353 cost = 0.146577778 -Epoch: 0354 cost = 0.152065272 -Epoch: 0355 cost = 0.155690018 -Accuracy: 0.7 -Epoch: 0356 cost = 0.147445475 -Epoch: 0357 cost = 0.139286468 -Epoch: 0358 cost = 0.142695360 -Epoch: 0359 cost = 0.130476934 -Epoch: 0360 cost = 0.143144532 -Accuracy: 0.676 -Epoch: 0361 cost = 0.145091254 -Epoch: 0362 cost = 0.143312194 -Epoch: 0363 cost = 0.141417281 -Epoch: 0364 cost = 0.139009522 -Epoch: 0365 cost = 0.140111981 -Accuracy: 0.686 -Epoch: 0366 cost = 0.140670846 -Epoch: 0367 cost = 0.137542833 -Epoch: 0368 cost = 0.136917190 -Epoch: 0369 cost = 0.139783495 -Epoch: 0370 cost = 0.138550615 -Accuracy: 0.658 -Epoch: 0371 cost = 0.133887005 -Epoch: 0372 cost = 0.139870919 -Epoch: 0373 cost = 0.138877699 -Epoch: 0374 cost = 0.127385353 -Epoch: 0375 cost = 0.131465573 -Accuracy: 0.684 -Epoch: 0376 cost = 0.138824100 -Epoch: 0377 cost = 0.138345353 -Epoch: 0378 cost = 0.121960020 -Epoch: 0379 cost = 0.126012006 -Epoch: 0380 cost = 0.132951543 -Accuracy: 0.692 -Epoch: 0381 cost = 0.130236401 -Epoch: 0382 cost = 0.118894364 -Epoch: 0383 cost = 0.132232952 -Epoch: 0384 cost = 0.121125753 -Epoch: 0385 cost = 0.120822303 +Epoch: 0346 cost = 0.222720423 +Epoch: 0347 cost = 0.210164854 +Epoch: 0348 cost = 0.217193825 +Epoch: 0349 cost = 0.205538029 +Epoch: 0350 cost = 0.215030146 +Accuracy: 0.68 +Epoch: 0351 cost = 0.208047377 +Epoch: 0352 cost = 0.206625841 +Epoch: 0353 cost = 0.200706393 +Epoch: 0354 cost = 0.195989668 +Epoch: 0355 cost = 0.206576956 +Accuracy: 0.648 +Epoch: 0356 cost = 0.202053510 +Epoch: 0357 cost = 0.208723881 +Epoch: 0358 cost = 0.205642292 +Epoch: 0359 cost = 0.195928207 +Epoch: 0360 cost = 0.203882499 Accuracy: 0.67 -Epoch: 0386 cost = 0.121437317 -Epoch: 0387 cost = 0.120374837 -Epoch: 0388 cost = 0.125013598 -Epoch: 0389 cost = 0.125752942 -Epoch: 0390 cost = 0.121344876 -Accuracy: 0.646 -Epoch: 0391 cost = 0.129671833 -Epoch: 0392 cost = 0.124679725 -Epoch: 0393 cost = 0.116743313 -Epoch: 0394 cost = 0.117508006 -Epoch: 0395 cost = 0.123441409 -Accuracy: 0.704 -Epoch: 0396 cost = 0.117150421 -Epoch: 0397 cost = 0.121750419 -Epoch: 0398 cost = 0.119541690 -Epoch: 0399 cost = 0.117073561 -Epoch: 0400 cost = 0.115258883 -Accuracy: 0.676 -Epoch: 0401 cost = 0.115418623 -Epoch: 0402 cost = 0.114674588 -Epoch: 0403 cost = 0.124775051 -Epoch: 0404 cost = 0.123790300 -Epoch: 0405 cost = 0.114192073 -Accuracy: 0.722 -Epoch: 0406 cost = 0.121756520 -Epoch: 0407 cost = 0.108724652 -Epoch: 0408 cost = 0.104631309 -Epoch: 0409 cost = 0.108242579 -Epoch: 0410 cost = 0.110814022 -Accuracy: 0.736 -Epoch: 0411 cost = 0.108535172 -Epoch: 0412 cost = 0.105365605 -Epoch: 0413 cost = 0.110570262 -Epoch: 0414 cost = 0.123771836 -Epoch: 0415 cost = 0.107642760 -Accuracy: 0.668 -Epoch: 0416 cost = 0.110376359 -Epoch: 0417 cost = 0.112206541 -Epoch: 0418 cost = 0.109693166 -Epoch: 0419 cost = 0.113708952 -Epoch: 0420 cost = 0.104978930 -Accuracy: 0.686 -Epoch: 0421 cost = 0.107645262 -Epoch: 0422 cost = 0.105464140 -Epoch: 0423 cost = 0.114400616 -Epoch: 0424 cost = 0.112145791 -Epoch: 0425 cost = 0.119566376 -Accuracy: 0.664 -Epoch: 0426 cost = 0.104825094 -Epoch: 0427 cost = 0.108384092 -Epoch: 0428 cost = 0.109740001 -Epoch: 0429 cost = 0.100707257 -Epoch: 0430 cost = 0.102619070 +Epoch: 0361 cost = 0.194690624 +Epoch: 0362 cost = 0.196908630 +Epoch: 0363 cost = 0.202135414 +Epoch: 0364 cost = 0.199923388 +Epoch: 0365 cost = 0.196365428 +Accuracy: 0.68 +Epoch: 0366 cost = 0.185447128 +Epoch: 0367 cost = 0.196574427 +Epoch: 0368 cost = 0.195564287 +Epoch: 0369 cost = 0.180336359 +Epoch: 0370 cost = 0.194343807 +Accuracy: 0.688 +Epoch: 0371 cost = 0.186285626 +Epoch: 0372 cost = 0.190013710 +Epoch: 0373 cost = 0.197687892 +Epoch: 0374 cost = 0.184584371 +Epoch: 0375 cost = 0.190913253 Accuracy: 0.662 -Epoch: 0431 cost = 0.098183115 -Epoch: 0432 cost = 0.108463831 -Epoch: 0433 cost = 0.100965668 -Epoch: 0434 cost = 0.103503827 -Epoch: 0435 cost = 0.103409275 -Accuracy: 0.672 -Epoch: 0436 cost = 0.110427527 -Epoch: 0437 cost = 0.099728981 -Epoch: 0438 cost = 0.104076649 -Epoch: 0439 cost = 0.088306066 -Epoch: 0440 cost = 0.097068101 -Accuracy: 0.698 -Epoch: 0441 cost = 0.099594999 -Epoch: 0442 cost = 0.097458495 -Epoch: 0443 cost = 0.098881092 -Epoch: 0444 cost = 0.100691583 -Epoch: 0445 cost = 0.096198725 -Accuracy: 0.648 -Epoch: 0446 cost = 0.088666075 -Epoch: 0447 cost = 0.094812336 -Epoch: 0448 cost = 0.094443779 -Epoch: 0449 cost = 0.092835562 -Epoch: 0450 cost = 0.085569579 +Epoch: 0376 cost = 0.194012809 +Epoch: 0377 cost = 0.179898899 +Epoch: 0378 cost = 0.185747168 +Epoch: 0379 cost = 0.183469582 +Epoch: 0380 cost = 0.191591957 Accuracy: 0.69 -Epoch: 0451 cost = 0.104187733 -Epoch: 0452 cost = 0.090711200 -Epoch: 0453 cost = 0.092231300 -Epoch: 0454 cost = 0.093845266 -Epoch: 0455 cost = 0.095721759 +Epoch: 0381 cost = 0.176704920 +Epoch: 0382 cost = 0.184566157 +Epoch: 0383 cost = 0.185613967 +Epoch: 0384 cost = 0.182787482 +Epoch: 0385 cost = 0.181154740 +Accuracy: 0.66 +Epoch: 0386 cost = 0.183127590 +Epoch: 0387 cost = 0.183282609 +Epoch: 0388 cost = 0.181939080 +Epoch: 0389 cost = 0.186416831 +Epoch: 0390 cost = 0.184981587 Accuracy: 0.69 -Epoch: 0456 cost = 0.089287774 -Epoch: 0457 cost = 0.082753352 -Epoch: 0458 cost = 0.094513458 -Epoch: 0459 cost = 0.096048896 -Epoch: 0460 cost = 0.092998643 -Accuracy: 0.704 -Epoch: 0461 cost = 0.100456652 -Epoch: 0462 cost = 0.088285049 -Epoch: 0463 cost = 0.095274256 -Epoch: 0464 cost = 0.092481631 -Epoch: 0465 cost = 0.093727250 -Accuracy: 0.682 -Epoch: 0466 cost = 0.092273732 -Epoch: 0467 cost = 0.092993474 -Epoch: 0468 cost = 0.098149405 -Epoch: 0469 cost = 0.092842954 -Epoch: 0470 cost = 0.092795983 -Accuracy: 0.652 -Epoch: 0471 cost = 0.087841002 -Epoch: 0472 cost = 0.091099940 -Epoch: 0473 cost = 0.084489412 -Epoch: 0474 cost = 0.090249555 -Epoch: 0475 cost = 0.088538364 -Accuracy: 0.634 -Epoch: 0476 cost = 0.098813208 -Epoch: 0477 cost = 0.090878914 -Epoch: 0478 cost = 0.090631220 -Epoch: 0479 cost = 0.081986326 -Epoch: 0480 cost = 0.091490479 +Epoch: 0391 cost = 0.170424865 +Epoch: 0392 cost = 0.177383073 +Epoch: 0393 cost = 0.177053552 +Epoch: 0394 cost = 0.169662276 +Epoch: 0395 cost = 0.178390077 +Accuracy: 0.674 +Epoch: 0396 cost = 0.170196023 +Epoch: 0397 cost = 0.182006982 +Epoch: 0398 cost = 0.180935718 +Epoch: 0399 cost = 0.170829106 +Epoch: 0400 cost = 0.174306042 +Accuracy: 0.714 +Epoch: 0401 cost = 0.176974503 +Epoch: 0402 cost = 0.172893045 +Epoch: 0403 cost = 0.170582323 +Epoch: 0404 cost = 0.173241530 +Epoch: 0405 cost = 0.180335288 Accuracy: 0.668 -Epoch: 0481 cost = 0.089289207 -Epoch: 0482 cost = 0.091088564 -Epoch: 0483 cost = 0.082973128 -Epoch: 0484 cost = 0.082656463 -Epoch: 0485 cost = 0.084007710 -Accuracy: 0.656 -Epoch: 0486 cost = 0.083851892 -Epoch: 0487 cost = 0.084173576 -Epoch: 0488 cost = 0.080871655 -Epoch: 0489 cost = 0.093059263 -Epoch: 0490 cost = 0.086428211 -Accuracy: 0.684 -Epoch: 0491 cost = 0.082998318 -Epoch: 0492 cost = 0.077075451 -Epoch: 0493 cost = 0.082501093 -Epoch: 0494 cost = 0.077714321 -Epoch: 0495 cost = 0.084485505 +Epoch: 0406 cost = 0.179313468 +Epoch: 0407 cost = 0.180736936 +Epoch: 0408 cost = 0.175959413 +Epoch: 0409 cost = 0.174506581 +Epoch: 0410 cost = 0.163335416 +Accuracy: 0.658 +Epoch: 0411 cost = 0.170097443 +Epoch: 0412 cost = 0.158451459 +Epoch: 0413 cost = 0.156419013 +Epoch: 0414 cost = 0.175325938 +Epoch: 0415 cost = 0.155034284 +Accuracy: 0.69 +Epoch: 0416 cost = 0.164153294 +Epoch: 0417 cost = 0.155209700 +Epoch: 0418 cost = 0.167793508 +Epoch: 0419 cost = 0.157012567 +Epoch: 0420 cost = 0.171157722 Accuracy: 0.684 -Epoch: 0496 cost = 0.077726235 -Epoch: 0497 cost = 0.081725398 -Epoch: 0498 cost = 0.077627569 -Epoch: 0499 cost = 0.073534842 -Epoch: 0500 cost = 0.079301288 -Accuracy: 0.688 -Epoch: 0501 cost = 0.085614167 -Epoch: 0502 cost = 0.076546287 -Epoch: 0503 cost = 0.078428817 -Epoch: 0504 cost = 0.079854097 -Epoch: 0505 cost = 0.073594795 -Accuracy: 0.672 -Epoch: 0506 cost = 0.084767702 -Epoch: 0507 cost = 0.082602362 -Epoch: 0508 cost = 0.084868135 -Epoch: 0509 cost = 0.081975326 -Epoch: 0510 cost = 0.080898829 -Accuracy: 0.694 -Epoch: 0511 cost = 0.080439630 -Epoch: 0512 cost = 0.079157391 -Epoch: 0513 cost = 0.070505928 -Epoch: 0514 cost = 0.082198612 -Epoch: 0515 cost = 0.073734272 +Epoch: 0421 cost = 0.157307838 +Epoch: 0422 cost = 0.161628031 +Epoch: 0423 cost = 0.158428192 +Epoch: 0424 cost = 0.158813586 +Epoch: 0425 cost = 0.171617052 +Accuracy: 0.692 +Epoch: 0426 cost = 0.164463258 +Epoch: 0427 cost = 0.160532972 +Epoch: 0428 cost = 0.157867650 +Epoch: 0429 cost = 0.154883435 +Epoch: 0430 cost = 0.158418911 +Accuracy: 0.652 +Epoch: 0431 cost = 0.145480454 +Epoch: 0432 cost = 0.155923912 +Epoch: 0433 cost = 0.157752631 +Epoch: 0434 cost = 0.155943191 +Epoch: 0435 cost = 0.158748155 Accuracy: 0.694 -Epoch: 0516 cost = 0.078817625 -Epoch: 0517 cost = 0.083742234 -Epoch: 0518 cost = 0.070385108 -Epoch: 0519 cost = 0.076170405 -Epoch: 0520 cost = 0.069586544 -Accuracy: 0.69 -Epoch: 0521 cost = 0.076646934 -Epoch: 0522 cost = 0.066745946 -Epoch: 0523 cost = 0.070204606 -Epoch: 0524 cost = 0.075837098 -Epoch: 0525 cost = 0.070756833 -Accuracy: 0.68 -Epoch: 0526 cost = 0.080132137 -Epoch: 0527 cost = 0.075720873 -Epoch: 0528 cost = 0.081173879 -Epoch: 0529 cost = 0.080321923 -Epoch: 0530 cost = 0.080343942 -Accuracy: 0.666 -Epoch: 0531 cost = 0.070019272 -Epoch: 0532 cost = 0.071198296 -Epoch: 0533 cost = 0.067625429 -Epoch: 0534 cost = 0.070865684 -Epoch: 0535 cost = 0.072966499 -Accuracy: 0.662 -Epoch: 0536 cost = 0.072265319 -Epoch: 0537 cost = 0.067212401 -Epoch: 0538 cost = 0.082092181 -Epoch: 0539 cost = 0.070265792 -Epoch: 0540 cost = 0.067760456 -Accuracy: 0.656 -Epoch: 0541 cost = 0.069231608 -Epoch: 0542 cost = 0.060511391 -Epoch: 0543 cost = 0.061854246 -Epoch: 0544 cost = 0.065151446 -Epoch: 0545 cost = 0.065705004 +Epoch: 0436 cost = 0.160792182 +Epoch: 0437 cost = 0.155377477 +Epoch: 0438 cost = 0.157744526 +Epoch: 0439 cost = 0.155427273 +Epoch: 0440 cost = 0.159342458 Accuracy: 0.678 -Epoch: 0546 cost = 0.072479896 -Epoch: 0547 cost = 0.065524118 -Epoch: 0548 cost = 0.063442402 -Epoch: 0549 cost = 0.068894447 -Epoch: 0550 cost = 0.068971091 -Accuracy: 0.672 -Epoch: 0551 cost = 0.067538062 -Epoch: 0552 cost = 0.062418102 -Epoch: 0553 cost = 0.068736978 -Epoch: 0554 cost = 0.056161060 -Epoch: 0555 cost = 0.073247312 -Accuracy: 0.664 -Epoch: 0556 cost = 0.073996993 -Epoch: 0557 cost = 0.071416234 -Epoch: 0558 cost = 0.068308531 -Epoch: 0559 cost = 0.057465338 -Epoch: 0560 cost = 0.064353319 -Accuracy: 0.692 -Epoch: 0561 cost = 0.060391057 -Epoch: 0562 cost = 0.058765004 -Epoch: 0563 cost = 0.061932487 -Epoch: 0564 cost = 0.062956043 -Epoch: 0565 cost = 0.065893011 -Accuracy: 0.664 -Epoch: 0566 cost = 0.068777146 -Epoch: 0567 cost = 0.074046623 -Epoch: 0568 cost = 0.073273619 -Epoch: 0569 cost = 0.062365477 -Epoch: 0570 cost = 0.060675035 -Accuracy: 0.676 -Epoch: 0571 cost = 0.064057277 -Epoch: 0572 cost = 0.074385645 -Epoch: 0573 cost = 0.070157795 -Epoch: 0574 cost = 0.073181057 -Epoch: 0575 cost = 0.060791633 -Accuracy: 0.692 -Epoch: 0576 cost = 0.062692893 -Epoch: 0577 cost = 0.064864261 -Epoch: 0578 cost = 0.061046095 -Epoch: 0579 cost = 0.069534725 -Epoch: 0580 cost = 0.071717415 -Accuracy: 0.68 -Epoch: 0581 cost = 0.061808382 -Epoch: 0582 cost = 0.062836597 -Epoch: 0583 cost = 0.063492643 -Epoch: 0584 cost = 0.067609849 -Epoch: 0585 cost = 0.060992296 -Accuracy: 0.658 -Epoch: 0586 cost = 0.067809301 -Epoch: 0587 cost = 0.060394807 -Epoch: 0588 cost = 0.067361560 -Epoch: 0589 cost = 0.065536044 -Epoch: 0590 cost = 0.061374272 -Accuracy: 0.642 -Epoch: 0591 cost = 0.059078618 -Epoch: 0592 cost = 0.063829750 -Epoch: 0593 cost = 0.062298429 -Epoch: 0594 cost = 0.071287302 -Epoch: 0595 cost = 0.065749961 +Epoch: 0441 cost = 0.152804907 +Epoch: 0442 cost = 0.162825121 +Epoch: 0443 cost = 0.141242604 +Epoch: 0444 cost = 0.140490852 +Epoch: 0445 cost = 0.142243742 +Accuracy: 0.682 +Epoch: 0446 cost = 0.164571498 +Epoch: 0447 cost = 0.143767359 +Epoch: 0448 cost = 0.153597356 +Epoch: 0449 cost = 0.145654636 +Epoch: 0450 cost = 0.142549302 +Accuracy: 0.646 +Epoch: 0451 cost = 0.150071420 +Epoch: 0452 cost = 0.152995880 +Epoch: 0453 cost = 0.140282794 +Epoch: 0454 cost = 0.148593902 +Epoch: 0455 cost = 0.139312753 +Accuracy: 0.674 +Epoch: 0456 cost = 0.148530192 +Epoch: 0457 cost = 0.147251993 +Epoch: 0458 cost = 0.143778329 +Epoch: 0459 cost = 0.141319702 +Epoch: 0460 cost = 0.135436040 +Accuracy: 0.684 +Epoch: 0461 cost = 0.131511842 +Epoch: 0462 cost = 0.145072434 +Epoch: 0463 cost = 0.144240762 +Epoch: 0464 cost = 0.143982705 +Epoch: 0465 cost = 0.129912225 Accuracy: 0.672 -Epoch: 0596 cost = 0.058481170 -Epoch: 0597 cost = 0.057969925 -Epoch: 0598 cost = 0.066291787 -Epoch: 0599 cost = 0.065026434 -Epoch: 0600 cost = 0.059985628 +Epoch: 0466 cost = 0.138561623 +Epoch: 0467 cost = 0.133583417 +Epoch: 0468 cost = 0.131267091 +Epoch: 0469 cost = 0.144252766 +Epoch: 0470 cost = 0.138372278 +Accuracy: 0.648 +Epoch: 0471 cost = 0.127405322 +Epoch: 0472 cost = 0.133263303 +Epoch: 0473 cost = 0.134148620 +Epoch: 0474 cost = 0.147869974 +Epoch: 0475 cost = 0.139710613 +Accuracy: 0.694 +Epoch: 0476 cost = 0.131028253 +Epoch: 0477 cost = 0.126008114 +Epoch: 0478 cost = 0.150548821 +Epoch: 0479 cost = 0.125722083 +Epoch: 0480 cost = 0.131007737 Accuracy: 0.668 -Epoch: 0601 cost = 0.053423943 -Epoch: 0602 cost = 0.061045629 -Epoch: 0603 cost = 0.062207677 -Epoch: 0604 cost = 0.060407002 -Epoch: 0605 cost = 0.057189315 -Accuracy: 0.67 -Epoch: 0606 cost = 0.067511549 -Epoch: 0607 cost = 0.061297516 -Epoch: 0608 cost = 0.060596582 -Epoch: 0609 cost = 0.054317859 -Epoch: 0610 cost = 0.052343796 +Epoch: 0481 cost = 0.131263324 +Epoch: 0482 cost = 0.140277008 +Epoch: 0483 cost = 0.123865877 +Epoch: 0484 cost = 0.136453279 +Epoch: 0485 cost = 0.126934629 Accuracy: 0.686 -Epoch: 0611 cost = 0.056506160 -Epoch: 0612 cost = 0.063030073 -Epoch: 0613 cost = 0.055784433 -Epoch: 0614 cost = 0.054427361 -Epoch: 0615 cost = 0.063970151 -Accuracy: 0.676 -Epoch: 0616 cost = 0.061050765 -Epoch: 0617 cost = 0.058202983 -Epoch: 0618 cost = 0.050584137 -Epoch: 0619 cost = 0.052944181 -Epoch: 0620 cost = 0.065120760 -Accuracy: 0.714 -Epoch: 0621 cost = 0.055821122 -Epoch: 0622 cost = 0.051986205 -Epoch: 0623 cost = 0.054332403 -Epoch: 0624 cost = 0.056823151 -Epoch: 0625 cost = 0.060887135 -Accuracy: 0.656 -Epoch: 0626 cost = 0.056574404 -Epoch: 0627 cost = 0.058406405 -Epoch: 0628 cost = 0.056019465 -Epoch: 0629 cost = 0.061377016 -Epoch: 0630 cost = 0.056286315 -Accuracy: 0.67 -Epoch: 0631 cost = 0.064294403 -Epoch: 0632 cost = 0.057684548 -Epoch: 0633 cost = 0.055395138 -Epoch: 0634 cost = 0.061279936 -Epoch: 0635 cost = 0.055086769 -Accuracy: 0.694 -Epoch: 0636 cost = 0.056932196 -Epoch: 0637 cost = 0.053274036 -Epoch: 0638 cost = 0.057776112 -Epoch: 0639 cost = 0.060285001 -Epoch: 0640 cost = 0.052538915 -Accuracy: 0.698 -Epoch: 0641 cost = 0.057047363 -Epoch: 0642 cost = 0.044306908 -Epoch: 0643 cost = 0.053264385 -Epoch: 0644 cost = 0.048877948 -Epoch: 0645 cost = 0.050039820 +Epoch: 0486 cost = 0.126405641 +Epoch: 0487 cost = 0.134560895 +Epoch: 0488 cost = 0.132346562 +Epoch: 0489 cost = 0.126915155 +Epoch: 0490 cost = 0.133014086 +Accuracy: 0.66 +Epoch: 0491 cost = 0.128638323 +Epoch: 0492 cost = 0.130443529 +Epoch: 0493 cost = 0.126753899 +Epoch: 0494 cost = 0.114836649 +Epoch: 0495 cost = 0.141375982 +Accuracy: 0.708 +Epoch: 0496 cost = 0.139420264 +Epoch: 0497 cost = 0.134975031 +Epoch: 0498 cost = 0.124961590 +Epoch: 0499 cost = 0.130409865 +Epoch: 0500 cost = 0.124366996 Accuracy: 0.686 -Epoch: 0646 cost = 0.056626274 -Epoch: 0647 cost = 0.055075806 -Epoch: 0648 cost = 0.046829629 -Epoch: 0649 cost = 0.051321841 -Epoch: 0650 cost = 0.053735480 -Accuracy: 0.672 -Epoch: 0651 cost = 0.056439840 -Epoch: 0652 cost = 0.054284734 -Epoch: 0653 cost = 0.049454561 -Epoch: 0654 cost = 0.046565355 -Epoch: 0655 cost = 0.048603004 -Accuracy: 0.662 -Epoch: 0656 cost = 0.048294246 -Epoch: 0657 cost = 0.054303624 -Epoch: 0658 cost = 0.052814844 -Epoch: 0659 cost = 0.048079909 -Epoch: 0660 cost = 0.056449527 -Accuracy: 0.662 -Epoch: 0661 cost = 0.056652258 -Epoch: 0662 cost = 0.056827327 -Epoch: 0663 cost = 0.052064014 -Epoch: 0664 cost = 0.045687309 -Epoch: 0665 cost = 0.047456508 +Epoch: 0501 cost = 0.130502676 +Epoch: 0502 cost = 0.130456836 +Epoch: 0503 cost = 0.124294621 +Epoch: 0504 cost = 0.123371773 +Epoch: 0505 cost = 0.138835895 +Accuracy: 0.726 +Epoch: 0506 cost = 0.127717276 +Epoch: 0507 cost = 0.118944810 +Epoch: 0508 cost = 0.120451637 +Epoch: 0509 cost = 0.124473464 +Epoch: 0510 cost = 0.131683494 +Accuracy: 0.67 +Epoch: 0511 cost = 0.126660948 +Epoch: 0512 cost = 0.130330144 +Epoch: 0513 cost = 0.126406845 +Epoch: 0514 cost = 0.137334209 +Epoch: 0515 cost = 0.129676421 +Accuracy: 0.702 +Epoch: 0516 cost = 0.126082168 +Epoch: 0517 cost = 0.140236827 +Epoch: 0518 cost = 0.115603044 +Epoch: 0519 cost = 0.118083283 +Epoch: 0520 cost = 0.125525745 +Accuracy: 0.67 +Epoch: 0521 cost = 0.111245054 +Epoch: 0522 cost = 0.121379045 +Epoch: 0523 cost = 0.122450649 +Epoch: 0524 cost = 0.118737362 +Epoch: 0525 cost = 0.132155129 +Accuracy: 0.68 +Epoch: 0526 cost = 0.120026794 +Epoch: 0527 cost = 0.113095676 +Epoch: 0528 cost = 0.111921014 +Epoch: 0529 cost = 0.110120336 +Epoch: 0530 cost = 0.120337588 +Accuracy: 0.678 +Epoch: 0531 cost = 0.119837387 +Epoch: 0532 cost = 0.114530141 +Epoch: 0533 cost = 0.117952761 +Epoch: 0534 cost = 0.120651463 +Epoch: 0535 cost = 0.115092028 +Accuracy: 0.674 +Epoch: 0536 cost = 0.119301189 +Epoch: 0537 cost = 0.109547124 +Epoch: 0538 cost = 0.114721535 +Epoch: 0539 cost = 0.116361326 +Epoch: 0540 cost = 0.111751930 +Accuracy: 0.666 +Epoch: 0541 cost = 0.108961816 +Epoch: 0542 cost = 0.116229246 +Epoch: 0543 cost = 0.121288770 +Epoch: 0544 cost = 0.117621829 +Epoch: 0545 cost = 0.119737629 Accuracy: 0.686 -Epoch: 0666 cost = 0.046594262 -Epoch: 0667 cost = 0.046810585 -Epoch: 0668 cost = 0.049053001 -Epoch: 0669 cost = 0.051781630 -Epoch: 0670 cost = 0.045175623 +Epoch: 0546 cost = 0.110438816 +Epoch: 0547 cost = 0.111064304 +Epoch: 0548 cost = 0.107804560 +Epoch: 0549 cost = 0.120911170 +Epoch: 0550 cost = 0.114649650 +Accuracy: 0.684 +Epoch: 0551 cost = 0.107714061 +Epoch: 0552 cost = 0.111656883 +Epoch: 0553 cost = 0.114752793 +Epoch: 0554 cost = 0.110536426 +Epoch: 0555 cost = 0.118712040 +Accuracy: 0.674 +Epoch: 0556 cost = 0.106774314 +Epoch: 0557 cost = 0.111336236 +Epoch: 0558 cost = 0.110485898 +Epoch: 0559 cost = 0.100509318 +Epoch: 0560 cost = 0.107867145 +Accuracy: 0.698 +Epoch: 0561 cost = 0.106597122 +Epoch: 0562 cost = 0.109421511 +Epoch: 0563 cost = 0.102338426 +Epoch: 0564 cost = 0.121696794 +Epoch: 0565 cost = 0.109283897 Accuracy: 0.704 -Epoch: 0671 cost = 0.054489744 -Epoch: 0672 cost = 0.047346830 -Epoch: 0673 cost = 0.046898829 -Epoch: 0674 cost = 0.045713064 -Epoch: 0675 cost = 0.046557224 -Accuracy: 0.652 -Epoch: 0676 cost = 0.041371434 -Epoch: 0677 cost = 0.045871089 -Epoch: 0678 cost = 0.052855518 -Epoch: 0679 cost = 0.044986555 -Epoch: 0680 cost = 0.048014041 -Accuracy: 0.686 -Epoch: 0681 cost = 0.051755832 -Epoch: 0682 cost = 0.045360965 -Epoch: 0683 cost = 0.046882404 -Epoch: 0684 cost = 0.052340601 -Epoch: 0685 cost = 0.053695479 +Epoch: 0566 cost = 0.105254890 +Epoch: 0567 cost = 0.121927035 +Epoch: 0568 cost = 0.114748673 +Epoch: 0569 cost = 0.105509866 +Epoch: 0570 cost = 0.116994424 +Accuracy: 0.69 +Epoch: 0571 cost = 0.106082235 +Epoch: 0572 cost = 0.107428371 +Epoch: 0573 cost = 0.104347722 +Epoch: 0574 cost = 0.114010719 +Epoch: 0575 cost = 0.111764481 +Accuracy: 0.682 +Epoch: 0576 cost = 0.114344178 +Epoch: 0577 cost = 0.111395085 +Epoch: 0578 cost = 0.103568691 +Epoch: 0579 cost = 0.098618215 +Epoch: 0580 cost = 0.111957005 +Accuracy: 0.668 +Epoch: 0581 cost = 0.113367702 +Epoch: 0582 cost = 0.109154763 +Epoch: 0583 cost = 0.107204894 +Epoch: 0584 cost = 0.111432658 +Epoch: 0585 cost = 0.112998830 +Accuracy: 0.696 +Epoch: 0586 cost = 0.098543316 +Epoch: 0587 cost = 0.095304508 +Epoch: 0588 cost = 0.116752032 +Epoch: 0589 cost = 0.104097747 +Epoch: 0590 cost = 0.109815498 +Accuracy: 0.69 +Epoch: 0591 cost = 0.091209450 +Epoch: 0592 cost = 0.096193801 +Epoch: 0593 cost = 0.095926440 +Epoch: 0594 cost = 0.096929071 +Epoch: 0595 cost = 0.103870244 +Accuracy: 0.694 +Epoch: 0596 cost = 0.102161529 +Epoch: 0597 cost = 0.099916381 +Epoch: 0598 cost = 0.100649900 +Epoch: 0599 cost = 0.098425770 +Epoch: 0600 cost = 0.095496017 +Accuracy: 0.696 +Epoch: 0601 cost = 0.102313055 +Epoch: 0602 cost = 0.101724144 +Epoch: 0603 cost = 0.096210864 +Epoch: 0604 cost = 0.100884899 +Epoch: 0605 cost = 0.097470764 +Accuracy: 0.692 +Epoch: 0606 cost = 0.100695437 +Epoch: 0607 cost = 0.092505307 +Epoch: 0608 cost = 0.094749211 +Epoch: 0609 cost = 0.088997761 +Epoch: 0610 cost = 0.098355950 Accuracy: 0.682 -Epoch: 0686 cost = 0.046186527 -Epoch: 0687 cost = 0.045612034 -Epoch: 0688 cost = 0.047017278 -Epoch: 0689 cost = 0.046109841 -Epoch: 0690 cost = 0.044169408 +Epoch: 0611 cost = 0.100073429 +Epoch: 0612 cost = 0.098362630 +Epoch: 0613 cost = 0.092072710 +Epoch: 0614 cost = 0.088043539 +Epoch: 0615 cost = 0.106211394 +Accuracy: 0.692 +Epoch: 0616 cost = 0.114533837 +Epoch: 0617 cost = 0.099485306 +Epoch: 0618 cost = 0.107752155 +Epoch: 0619 cost = 0.088217394 +Epoch: 0620 cost = 0.101134382 +Accuracy: 0.672 +Epoch: 0621 cost = 0.094724528 +Epoch: 0622 cost = 0.098326855 +Epoch: 0623 cost = 0.096618281 +Epoch: 0624 cost = 0.104486405 +Epoch: 0625 cost = 0.093760595 +Accuracy: 0.744 +Epoch: 0626 cost = 0.099997650 +Epoch: 0627 cost = 0.090708038 +Epoch: 0628 cost = 0.100836329 +Epoch: 0629 cost = 0.095824684 +Epoch: 0630 cost = 0.096236829 +Accuracy: 0.696 +Epoch: 0631 cost = 0.100781335 +Epoch: 0632 cost = 0.090005336 +Epoch: 0633 cost = 0.084376190 +Epoch: 0634 cost = 0.100622473 +Epoch: 0635 cost = 0.089239176 +Accuracy: 0.714 +Epoch: 0636 cost = 0.096699787 +Epoch: 0637 cost = 0.085737281 +Epoch: 0638 cost = 0.099683197 +Epoch: 0639 cost = 0.093491073 +Epoch: 0640 cost = 0.091517283 +Accuracy: 0.708 +Epoch: 0641 cost = 0.110236527 +Epoch: 0642 cost = 0.096306329 +Epoch: 0643 cost = 0.096605287 +Epoch: 0644 cost = 0.088369381 +Epoch: 0645 cost = 0.088198278 +Accuracy: 0.7 +Epoch: 0646 cost = 0.086842561 +Epoch: 0647 cost = 0.091289249 +Epoch: 0648 cost = 0.090074934 +Epoch: 0649 cost = 0.094352653 +Epoch: 0650 cost = 0.094865657 +Accuracy: 0.692 +Epoch: 0651 cost = 0.092150683 +Epoch: 0652 cost = 0.100330299 +Epoch: 0653 cost = 0.085904692 +Epoch: 0654 cost = 0.102988278 +Epoch: 0655 cost = 0.097860797 +Accuracy: 0.684 +Epoch: 0656 cost = 0.096874003 +Epoch: 0657 cost = 0.094748789 +Epoch: 0658 cost = 0.092886582 +Epoch: 0659 cost = 0.084694858 +Epoch: 0660 cost = 0.090067991 Accuracy: 0.684 -Epoch: 0691 cost = 0.041358152 -Epoch: 0692 cost = 0.049221673 -Epoch: 0693 cost = 0.047056143 -Epoch: 0694 cost = 0.045664821 -Epoch: 0695 cost = 0.047862345 -Accuracy: 0.632 -Epoch: 0696 cost = 0.045854133 -Epoch: 0697 cost = 0.047327583 -Epoch: 0698 cost = 0.053963129 -Epoch: 0699 cost = 0.041466869 -Epoch: 0700 cost = 0.039984456 -Accuracy: 0.712 +Epoch: 0661 cost = 0.094678811 +Epoch: 0662 cost = 0.074775940 +Epoch: 0663 cost = 0.088533551 +Epoch: 0664 cost = 0.094765287 +Epoch: 0665 cost = 0.083389083 +Accuracy: 0.724 +Epoch: 0666 cost = 0.093813586 +Epoch: 0667 cost = 0.091408406 +Epoch: 0668 cost = 0.082687715 +Epoch: 0669 cost = 0.087257151 +Epoch: 0670 cost = 0.090747049 +Accuracy: 0.682 +Epoch: 0671 cost = 0.098532537 +Epoch: 0672 cost = 0.089712114 +Epoch: 0673 cost = 0.076336210 +Epoch: 0674 cost = 0.088785120 +Epoch: 0675 cost = 0.085748422 +Accuracy: 0.708 +Epoch: 0676 cost = 0.085310791 +Epoch: 0677 cost = 0.082828352 +Epoch: 0678 cost = 0.084820958 +Epoch: 0679 cost = 0.074392315 +Epoch: 0680 cost = 0.096775934 +Accuracy: 0.678 +Epoch: 0681 cost = 0.080296823 +Epoch: 0682 cost = 0.093049644 +Epoch: 0683 cost = 0.080510007 +Epoch: 0684 cost = 0.096826262 +Epoch: 0685 cost = 0.090120267 +Accuracy: 0.714 +Epoch: 0686 cost = 0.095147286 +Epoch: 0687 cost = 0.088083414 +Epoch: 0688 cost = 0.089795241 +Epoch: 0689 cost = 0.077340920 +Epoch: 0690 cost = 0.081029948 +Accuracy: 0.714 +Epoch: 0691 cost = 0.080220379 +Epoch: 0692 cost = 0.090332029 +Epoch: 0693 cost = 0.080781123 +Epoch: 0694 cost = 0.084629651 +Epoch: 0695 cost = 0.084704853 +Accuracy: 0.722 +Epoch: 0696 cost = 0.081616563 +Epoch: 0697 cost = 0.078657970 +Epoch: 0698 cost = 0.078298834 +Epoch: 0699 cost = 0.079918880 +Epoch: 0700 cost = 0.089530587 +Accuracy: 0.686 Finished! -Out[13]: '\n9) data5 (n_mfcc=20, length=430)\nlr=0.0002, epoch = 200 \nwin : (2, 21), (2,4), (3,3)\nmax_pool : (2,21), (2,4), (3,3)\np_keep_conv, p_keep_hidden = 0.8, 0.7\naccuracy : 63~70% \n\n10) 9와 같고\nlr=0.0002, epoch = 700 \nwin : (2, 43), (3,3), (3,3)\nmax_pool : (2,43), (3,3), (3,3)\np_keep_conv, p_keep_hidden = 0.8, 0.7\naccuracy : 66~74%\n\n11) 9와 같고\nlr=0.0002, epoch = 700\np_keep_conv, p_keep_hidden = 0.7, 0.5\naccuracy : 67~72%\n' From 6faeaddad145b8febb971fe5b4b69d02b256013b Mon Sep 17 00:00:00 2001 From: smallerhand Date: Wed, 2 May 2018 14:15:59 +0900 Subject: [PATCH 10/36] all --- x_ksh/SA_cnn_ps3.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/x_ksh/SA_cnn_ps3.py b/x_ksh/SA_cnn_ps3.py index 6264eb9..391ddc3 100644 --- a/x_ksh/SA_cnn_ps3.py +++ b/x_ksh/SA_cnn_ps3.py @@ -9,6 +9,7 @@ #import important modules import numpy as np import tensorflow as tf +from math import ceil tf.set_random_seed(777) trainData = np.genfromtxt('/home/paperspace/Downloads/trainData6.csv', delimiter=',') @@ -26,7 +27,7 @@ # hyper parameters learning_rate = 0.0002 -training_epochs = 700 +training_epochs = 1100 batch_size = 200 steps_for_validate = 5 @@ -42,6 +43,7 @@ W1 = tf.get_variable("W1", shape=[2, 20, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME') L1 = tf.nn.elu(L1) +L1 = tf.layers.batch_normalization(L1) L1 = tf.nn.max_pool(L1, ksize=[1, 2, 20, 1],strides=[1, 2, 20, 1], padding='SAME') L1 = tf.nn.dropout(L1, p_keep_conv) @@ -49,6 +51,7 @@ W2 = tf.get_variable("W2", shape=[3, 3, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') L2 = tf.nn.elu(L2) +L2 = tf.layers.batch_normalization(L2) L2 = tf.nn.max_pool(L2, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') L2 = tf.nn.dropout(L2, p_keep_conv) @@ -56,6 +59,7 @@ W3 = tf.get_variable("W3", shape=[3, 3, 64, 128],initializer=tf.contrib.layers.xavier_initializer()) L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') L3 = tf.nn.elu(L3) +L3 = tf.layers.batch_normalization(L3) L3 = tf.nn.max_pool(L3, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') L3 = tf.nn.dropout(L3, p_keep_conv) L3_flat= tf.reshape(L3, shape=[-1, 2*2*128]) @@ -63,10 +67,12 @@ # Final FC 2*3*128 inputs -> 41 outputs W4 = tf.get_variable("W4", shape=[2*2*128, 512],initializer=tf.contrib.layers.xavier_initializer()) L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) +L4 = tf.layers.batch_normalization(L4) L4 = tf.nn.dropout(L4, p_keep_hidden) W_o = tf.get_variable("W_o", shape=[512,41],initializer=tf.contrib.layers.xavier_initializer()) b = tf.Variable(tf.random_normal([41])) logits = tf.matmul(L4, W_o) + b +logits = tf.layers.batch_normalization(logits) # define cost/loss & optimizer cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) @@ -78,11 +84,12 @@ sess.run(tf.global_variables_initializer()) saver = tf.train.Saver() + # train my model print('Learning started. It takes sometime.') for epoch in range(training_epochs): avg_cost = 0 - total_batch = int(len(trainData) / batch_size) + total_batch = ceil(len(trainData) / batch_size) for i in range(total_batch): batch_xs = trainData[i*batch_size:(i+1)*batch_size] batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) From f60bfe586d944968dba534d97d823b1fd245f12b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EA=B3=BD=EC=83=81=EC=9A=B1?= <37833373+Trigger21@users.noreply.github.com> Date: Wed, 2 May 2018 14:53:47 +0900 Subject: [PATCH 11/36] Create soun_cnn4.py --- x_ksw/New Project/soun_cnn4.py | 325 +++++++++++++++++++++++++++++++++ 1 file changed, 325 insertions(+) create mode 100644 x_ksw/New Project/soun_cnn4.py diff --git a/x_ksw/New Project/soun_cnn4.py b/x_ksw/New Project/soun_cnn4.py new file mode 100644 index 0000000..8bccd03 --- /dev/null +++ b/x_ksw/New Project/soun_cnn4.py @@ -0,0 +1,325 @@ +import numpy as np +np.random.seed(1001) +tf.set_random_seed(1001) +import tensorflow as tf +import os +import shutil +import IPython +import matplotlib +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns +import glob +from sklearn.cross_validation import StratifiedKFold + +%matplotlib inline +matplotlib.style.use('ggplot') + +# Preparing data + +# *2.03 : 350 + +class Config(object): + def __init__(self, + sampling_rate=16000, audio_duration=2, n_classes=41, + use_mfcc=False, n_folds=10, learning_rate=0.0001, + max_epochs=50, n_mfcc=20): + self.sampling_rate = sampling_rate + self.audio_duration = audio_duration + self.n_classes = n_classes + self.use_mfcc = use_mfcc + self.n_mfcc = n_mfcc + self.n_folds = n_folds + self.learning_rate = learning_rate + self.max_epochs = max_epochs + + self.audio_length = self.sampling_rate * self.audio_duration*2.03 + if self.use_mfcc: + self.dim = (self.n_mfcc, 1 + int(np.floor(self.audio_length/512)), 1) + else: + self.dim = (self.audio_length, 1) + +config = Config(sampling_rate=44100, audio_duration=2, n_folds=10, + learning_rate=0.001, use_mfcc=True, n_mfcc=40) + +def prepare_data(df, config, data_dir): + X = np.empty(shape=(df.shape[0], config.dim[0], config.dim[1], 1)) + input_length = int(config.audio_length) + for i, fname in enumerate(df['fname']): + print(fname) + file_path = data_dir + fname + data, _ = librosa.core.load(file_path, sr=config.sampling_rate, res_type="kaiser_fast") + + # Random offset / Padding + if len(data) > input_length: + max_offset = len(data) - input_length + offset = np.random.randint(max_offset) + data = data[offset:(input_length+offset)] + else: + if input_length > len(data): + max_offset = input_length - len(data) + offset = np.random.randint(max_offset) + else: + offset = 0 + data = np.pad(data, (offset, input_length - len(data) - offset), "constant") + + data = librosa.feature.mfcc(data, sr=config.sampling_rate, n_mfcc=config.n_mfcc) + data = np.expand_dims(data, axis=-1) + X[i,] = data + return X + +#file = "/home/itwill03/sound/audio_train/*.wav" +file = "c:/sound/audio_train/*.wav" +train_list=glob.glob(file) + +#file = "/home/itwill03/sound/audio_test/*.wav" +#test_list=glob.glob(file) + +train = pd.read_csv("c:/sound/train.csv") +#train = pd.read_csv("/home/itwill03/sound/train.csv") + +labels = train['label'] +l = train['label'].unique() + +df_label = pd.DataFrame(labels) + +for i in range(len(l)): + df_label[df_label==l[i]] = int(i) +df_label.values + +df = pd.concat([train, df_label], axis=1) + +from sklearn.model_selection import train_test_split +train_set, test_set = train_test_split(df, test_size = 0.3) +trainfile = train_set['fname'] +testfile = test_set['fname'] +trainLabel = train_set.values[:,-1] +testLabel = test_set.values[:,-1] + +train.shape[0] +config.dim[0] +config.dim[1] +#X = np.empty(shape=(9473, 40, 350, 1)) # 엔트리를 초기화 하지 않고 값을 반환 + +train.index +train.index[1] + +config.audio_length + + +X_train = prepare_data(train_set, config, 'c:/sound/audio_train/') +X_test = prepare_data(test_set, config, 'c:/sound/audio_train/') + +#X_train = prepare_data(train_set, config, '/home/itwill03/sound/audio_train/') +#X_test = prepare_data(test_set, config, '/home/itwill03/sound/audio_train/') + +#Normalization + +mean = np.mean(X_train, axis=0) +std = np.std(X_train, axis=0) + +X_train = (X_train - mean)/std +X_test = (X_test - mean)/std + +X_train.shape # (6631, 40, 350, 1) +X_test.shape # (2842, 40, 350, 1) + +trainData = X_train.reshape(-1, 40, 173) +testData = X_test.reshape(-1, 40, 173) + +trainData.shape # (6631, 40, 350) +testData.shape # (2842, 40, 350) +trainLabel.shape # (6631,) +testLabel.shape # (2842,) + +# start +tf.reset_default_graph() #그래프 초기화 + +# hyper parameters +learning_rate = 0.0001 +training_epochs = 700 +batch_size = 100 +steps_for_validate = 5 + +#placeholder +X = tf.placeholder(tf.float32, [None, 40, 350,1], name="X") +#X_sound = tf.reshape(X, [-1, 40, 173, 1]) # 40*350*1 (frequency, time, amplitude) +Y = tf.placeholder(tf.int32, [None, 1], name="Y") +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv") +p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") + +# L1 SoundIn shape=(?, 40, 350, 1) +W1 = tf.get_variable("W1", shape=[4, 35, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +L1 = tf.nn.conv2d(X, W1, strides=[1, 1, 1, 1], padding='SAME') +L1 = tf.layers.batch_normalization(L1) +#L1 = tf.nn.relu(L1) +#L1 = tf.nn.elu(L1) +L1 = tf.nn.leaky_relu(L1,0.1) +L1 = tf.nn.max_pool(L1, ksize=[1, 4, 35, 1],strides=[1, 4, 35, 1], padding='SAME') +#L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L1 = tf.nn.dropout(L1, p_keep_conv) + +# L2 Input shape=(?,10,10,32) +W2 = tf.get_variable("W2", shape=[3, 3, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) +L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') +L2 = tf.layers.batch_normalization(L2) +#L2 = tf.nn.relu(L2) +#L2 = tf.nn.elu(L2) +L2 = tf.nn.leaky_relu(L2,0.1) +L2 = tf.nn.max_pool(L2, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') +#L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L2 = tf.nn.dropout(L2, p_keep_conv) + +# L3 Input shape=(?,4,4,64) +W3 = tf.get_variable("W3", shape=[3, 3, 64, 128],initializer=tf.contrib.layers.xavier_initializer()) +L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') +L3 = tf.layers.batch_normalization(L3) +#L3 = tf.nn.relu(L3) +#L3 = tf.nn.elu(L3) +L3 = tf.nn.leaky_relu(L3,0.1) +L3 = tf.nn.max_pool(L3, ksize=[1, 3, 3, 1],strides=[1, 2, 2, 1], padding='SAME') +#L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L3 = tf.nn.dropout(L3, p_keep_conv) +L3_flat= tf.reshape(L3, shape=[-1, 2*2*128]) + +# Final FC 2*2*128 inputs -> 41 outputs +W4 = tf.get_variable("W4", shape=[2*2*128, 410],initializer=tf.contrib.layers.xavier_initializer()) +#L4 = tf.nn.relu(tf.matmul(L3_flat, W4)) +#L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) +L4 = tf.nn.leaky_relu(tf.matmul(L3_flat, W4),0.1) +L4 = tf.nn.dropout(L4, p_keep_hidden) +W_o = tf.get_variable("W_o", shape=[410,41],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L4, W_o) + b + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +#saver = tf.train.Saver() + +# train my model +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = int(len(X_train) / batch_size) + for i in range(total_batch): + batch_xs = X_train[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .8, p_keep_hidden: 0.7} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + if epoch % steps_for_validate == steps_for_validate-1: + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + x=np.random.choice(testLabel.shape[0], 500, replace=False) + print('Accuracy:', sess.run(accuracy, feed_dict={ + X: X_test[x], Y: testLabel[x].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + #save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') +print('Finished!') + + +trainData.shape +batch_xs.shape +batch_ys.shape + +sess.close() + + +""" +1) con2d layer * 3 + FC +lr=0.001, epoch = 100 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (4, 10, 32), (4, 10, 32), (4, 10, 32) +FC : (?, 64), (64, 41) +max_pool : (2,2), (2,2), (3,3) +accuracy: 51~60% +2) con2d layer * 3 + FC +lr=0.001, epoch = 100 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (4, 10, 32), (4, 10, 32), (4, 10, 32) +FC : (2*8*32, 64), (64, 41) +max_pool : (2,2), (2,2), (2,2) +accuracy: 55~60% +3) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.063까지 나옴) 정확도는 그대로임 +lr=0.0002, epoch = 300 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (4, 10, 32), (4, 10, 32), (4, 10, 32) +FC : (2*8*32, 64), (64, 41) +max_pool : (2,2), (2,2), (2,2) +accuracy: 54~55% +4) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.012까지 나옴) 정확도는 그대로임 +lr=0.0002, epoch = 300 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (2, 8, 32), (3, 3, 64), (3, 3, 128) +FC : (3*3*128, 512), (512, 41) +max_pool : (2,8), (3,3), (3,3) +accuracy: 61~63% +5) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.0016까지 나옴) 정확도는 그대로임 +lr=0.0002, epoch = 300 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (3, 13, 32), (3, 3, 64), (3, 3, 128) +FC : (5*22*128, 625), (625, 41) +max_pool : (2,2), (2,2), (2,2) +accuracy: 53~56% +6) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.16까지 나옴) 정확도는 그대로임 +lr=0.00008, epoch = 300 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (2, 10, 32), (3, 3, 64), (3, 3, 128) +FC : (3*2*128, 625), (625, 41) +max_pool : (2,10), (3,3), (3,3) +accuracy: 65~67% +7) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.13까지 나옴) 정확도는 그대로임 +lr=0.00005, epoch = 500 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (2, 10, 32), (3, 3, 64), (3, 3, 128) +FC : (3*2*128, 625), (625, 41) +max_pool : (2,10), (3,3), (3,3) +accuracy: 65~66% +8) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.008까지 나옴) 정확도는 그대로임 +lr=0.00005, epoch = 400 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (3, 3, 32), (3, 3, 64), (3, 3, 128) +FC : (5*5*128, 1000), (1000, 41) +max_pool : (2,10), (2,2), (2,2) +accuracy: 60~61% +9) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.22까지 나옴) 정확도는 그대로임 +lr=0.00005, epoch = 500 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (2, 10, 32), (3, 3, 64), (3, 3, 128) +FC : (5*1*128, 650), (650, 41) +max_pool : (2,10), (2,10), (3,3) +accuracy: 60~61% +10) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.22까지 나옴) 정확도는 그대로임 +lr=0.00005, epoch = 500 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (2, 10, 32), (3, 3, 64), (3, 3, 128) +FC : (10*10*128, 650), (650, 41) +max_pool : (2,9), (2,2), (1,1) +accuracy: 59~60% +11) + cost = 0.024251443 + Accuracy: 0.64% ~ 0.65% +12) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.024까지 나옴) 정확도는 그대로임 +lr=0.0002, epoch = 700 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (4, 10, 32), (3, 3, 64), (3, 3, 128) +FC : (5*6*128, 650), (650, 41) +max_pool : (4,10), (2,2), (2,2) +accuracy: 61~62% +13) con2d layer * 3 + FC # V_40,350 코스트는 계속해서 내려가나 (0.14까지 나옴) 정확도는 그대로임 +lr=0.0002, epoch = 700 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (4, 35, 32), (3, 3, 64), (3, 3, 128) +FC : (2*2*128, 410), (410, 41) +max_pool : (4,35), (3,3), (2,2) +accuracy: 64~72% + +""" + From c3d9b28132f6e6ab68004d26f57c95fdf9ec0011 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EA=B3=BD=EC=83=81=EC=9A=B1?= <37833373+Trigger21@users.noreply.github.com> Date: Wed, 2 May 2018 15:09:26 +0900 Subject: [PATCH 12/36] Add files via upload --- x_ksw/New Project/mel_cnn.py | 153 +++++++++++++++++++++++++++++++++++ 1 file changed, 153 insertions(+) create mode 100644 x_ksw/New Project/mel_cnn.py diff --git a/x_ksw/New Project/mel_cnn.py b/x_ksw/New Project/mel_cnn.py new file mode 100644 index 0000000..475b7a9 --- /dev/null +++ b/x_ksw/New Project/mel_cnn.py @@ -0,0 +1,153 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Sun Apr 29 05:53:22 2018 +@author: itwill03 +""" + + +import numpy as np +import os +import glob +import tensorflow as tf +import pandas as pd + +tf.set_random_seed(777) + +#train_info = pd.read_csv("/home/itwill03/sound/train.csv",delimiter=',') +#train_data = np.genfromtxt("/home/itwill03/sound/yy/feature_train.csv", delimiter=',') +train_info = pd.read_csv("C:\data\sound/train.csv",delimiter=',') +train_data = np.genfromtxt("C:\data\sound\mel_train2.csv", delimiter=',') +train_data.shape #Out[16]: (50, 16384) + +#normalizaion +train_data = librosa.util.normalize(train_data) #axix = ?, norm = ?>0>? + +#label set +labels = train_info['label'] +df_label = pd.DataFrame(labels) +l = train_info['label'].unique() + +for i in range(len(l)): + df_label[df_label==l[i]] = i + +#train data set +train_data = pd.DataFrame(train_data) +train_data['label']=df_label +train_data = train_data.astype(np.float32) +train_data.shape #Out[20]: (50, 16385) +#np.savetxt("c:/data/sound/train_data.csv",train_data, delimiter=",") + +#훈련세트, validation세트 나누기 +from sklearn.model_selection import train_test_split +train_set, validate_set = train_test_split(train_data, test_size = 0.3) +trainData = train_set.values[:,0:16384] +trainLabel = train_set.values[:,-1] +validateData = validate_set.values[:,0:16384] +validataLabel = validate_set.values[:,-1] + +#print (trainData.shape,trainLabel.shape,validateData.shape,validataLabel.shape) +tf.reset_default_graph() + +# 텐서플로우 모델 생성 +n_dim = 16384 +n_classes = 41 +training_epochs = 700 +learning_rate = 0.0004 +batch_size = 100 +steps_for_validate = 5 +keep_prob = tf.placeholder(tf.float32) + +X = tf.placeholder(tf.float32, [None, n_dim]) +#X_img = tf.reshape(X, [-1, 128, 128, 1]) +Y = tf.placeholder(tf.int32, [None, 1]) +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name='p_keep_conv') +p_keep_hidden = tf.placeholder(tf.float32, name='p_keep_hidden') + + + +# img shape = (?, 128, 128, 1) +c1 = tf.layers.conv2d(tf.reshape(X, [-1, 128, 128, 1]), 32, kernel_size=[3, 3], strides=(1, 1), + padding='same', activation=tf.nn.elu, name="c1") +n1 = tf.layers.batch_normalization(c1) +p1 = tf.layers.max_pooling2d(inputs=n1, pool_size=[2, 2], strides=2) +p1 = tf.nn.dropout(p1, p_keep_conv) + +# img shape = (?, 64, 64, 32) +c2 = tf.layers.conv2d(tf.reshape(p1, [-1, 64, 64, 32]), 64, kernel_size=[3, 3], strides=(1, 1), + padding='same', activation=tf.nn.elu, name="c2") +n2 = tf.layers.batch_normalization(c2) +p2 = tf.layers.max_pooling2d(inputs=n2, pool_size=[2, 2], strides=2) #shape = [?, 1, 48, 100] +p2 = tf.nn.dropout(p2, p_keep_conv) + +# img shape = (?, 32, 32, 64) +c3 = tf.layers.conv2d(tf.reshape(p2, [-1, 32, 32, 64]), 128, kernel_size=[3, 3], strides=(1, 1), + padding='same', activation=tf.nn.elu, name="c3") +n3 = tf.layers.batch_normalization(c3) +p3 = tf.layers.max_pooling2d(inputs=n3, pool_size=[2, 2], strides=2) #shape = [?, 1, 24, 200] +p3 = tf.nn.dropout(p3, p_keep_conv) + +L4_flat = tf.reshape(p3, shape=[-1, 16*16*128]) +W1 = tf.get_variable("W1", shape=[16*16*128, 64], initializer=tf.contrib.layers.xavier_initializer()) +L5 = tf.nn.elu(tf.matmul(L4_flat, W1)) +n5 = tf.layers.batch_normalization(L5) +L5 = tf.nn.dropout(n5, p_keep_hidden) + +W2 = tf.get_variable("W2", shape=[64,41],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L5, W2) + b + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +saver = tf.train.Saver() + + +# train my model + +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = int(len(trainData) / batch_size) + for i in range(total_batch): + batch_xs = trainData[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .7, p_keep_hidden: .5} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + if epoch % steps_for_validate == steps_for_validate-1: + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + x=np.random.choice(validataLabel.shape[0], 500, replace=False) + print('Accuracy:', sess.run(accuracy, feed_dict={ + X: validateData[x], Y: validataLabel[x].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + #save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') +print('Finished!') + +""" +#1. +training_epochs = 700 +learning_rate = 0.0004 +activation = elu +conv = kernel_size=[3, 3], strides=(1, 1) + kernel_size=[3, 3], strides=(1, 1) + kernel_size=[3, 3], strides=(1, 1) +pool = pool_size=[2, 2], strides=2 + pool_size=[2, 2], strides=2 + pool_size=[2, 2], strides=2 +flat = 16*16*128 -> 64 +p_keep_conv: .7, p_keep_hidden: .5 + +cost : 0.06~0.08에서 계속 떨어지는중 +Accuracy: 59~63% +전처리시 정규화 고려.. librosa.util.normalize +윈도우 14*2 고려.. +러닝레이트 조절 +""" From 825ad7d18435010b53e006e145eaf1b89f140a7b Mon Sep 17 00:00:00 2001 From: Kim Seunghyuck Date: Wed, 2 May 2018 02:22:34 -0400 Subject: [PATCH 13/36] Update sample.txt --- x_ksh/sample.txt | 952 +++++++++++++++++++++++------------------------ 1 file changed, 476 insertions(+), 476 deletions(-) diff --git a/x_ksh/sample.txt b/x_ksh/sample.txt index b580388..560b603 100644 --- a/x_ksh/sample.txt +++ b/x_ksh/sample.txt @@ -1,498 +1,498 @@ -Epoch: 0287 cost = 0.263603727 -Epoch: 0288 cost = 0.263932069 -Epoch: 0289 cost = 0.270318500 -Epoch: 0290 cost = 0.270442030 -Accuracy: 0.686 -Epoch: 0291 cost = 0.274197009 -Epoch: 0292 cost = 0.268625583 -Epoch: 0293 cost = 0.258771868 -Epoch: 0294 cost = 0.263313771 -Epoch: 0295 cost = 0.259434710 +Epoch: 0687 cost = 0.085299448 +Epoch: 0688 cost = 0.076131314 +Epoch: 0689 cost = 0.079837425 +Epoch: 0690 cost = 0.072884856 +Accuracy: 0.716 +Epoch: 0691 cost = 0.085249637 +Epoch: 0692 cost = 0.080807541 +Epoch: 0693 cost = 0.079171773 +Epoch: 0694 cost = 0.078891276 +Epoch: 0695 cost = 0.084388717 +Accuracy: 0.684 +Epoch: 0696 cost = 0.081647080 +Epoch: 0697 cost = 0.074516292 +Epoch: 0698 cost = 0.074280727 +Epoch: 0699 cost = 0.077478086 +Epoch: 0700 cost = 0.076070116 +Accuracy: 0.656 +Epoch: 0701 cost = 0.088026031 +Epoch: 0702 cost = 0.080923841 +Epoch: 0703 cost = 0.084142730 +Epoch: 0704 cost = 0.079386957 +Epoch: 0705 cost = 0.088055449 Accuracy: 0.69 -Epoch: 0296 cost = 0.260218189 -Epoch: 0297 cost = 0.269922243 -Epoch: 0298 cost = 0.266892755 -Epoch: 0299 cost = 0.248503954 -Epoch: 0300 cost = 0.256831940 -Accuracy: 0.702 -Epoch: 0301 cost = 0.254391911 -Epoch: 0302 cost = 0.252349392 -Epoch: 0303 cost = 0.254330822 -Epoch: 0304 cost = 0.253464962 -Epoch: 0305 cost = 0.247570692 -Accuracy: 0.702 -Epoch: 0306 cost = 0.254490927 -Epoch: 0307 cost = 0.248884994 -Epoch: 0308 cost = 0.238015426 -Epoch: 0309 cost = 0.250835216 -Epoch: 0310 cost = 0.236978996 -Accuracy: 0.664 -Epoch: 0311 cost = 0.248637487 -Epoch: 0312 cost = 0.237585425 -Epoch: 0313 cost = 0.250483723 -Epoch: 0314 cost = 0.246652423 -Epoch: 0315 cost = 0.244677804 +Epoch: 0706 cost = 0.082291036 +Epoch: 0707 cost = 0.078528139 +Epoch: 0708 cost = 0.075961697 +Epoch: 0709 cost = 0.076678142 +Epoch: 0710 cost = 0.087907239 +Accuracy: 0.692 +Epoch: 0711 cost = 0.092222471 +Epoch: 0712 cost = 0.081929273 +Epoch: 0713 cost = 0.082702750 +Epoch: 0714 cost = 0.077231034 +Epoch: 0715 cost = 0.074587684 +Accuracy: 0.684 +Epoch: 0716 cost = 0.074169126 +Epoch: 0717 cost = 0.076376604 +Epoch: 0718 cost = 0.085256069 +Epoch: 0719 cost = 0.068677963 +Epoch: 0720 cost = 0.074765622 +Accuracy: 0.714 +Epoch: 0721 cost = 0.076868226 +Epoch: 0722 cost = 0.075913059 +Epoch: 0723 cost = 0.074811698 +Epoch: 0724 cost = 0.072882711 +Epoch: 0725 cost = 0.077264389 +Accuracy: 0.674 +Epoch: 0726 cost = 0.075198326 +Epoch: 0727 cost = 0.072747350 +Epoch: 0728 cost = 0.071098342 +Epoch: 0729 cost = 0.066048522 +Epoch: 0730 cost = 0.073817988 +Accuracy: 0.706 +Epoch: 0731 cost = 0.083407499 +Epoch: 0732 cost = 0.062347427 +Epoch: 0733 cost = 0.081045545 +Epoch: 0734 cost = 0.072677261 +Epoch: 0735 cost = 0.077720603 +Accuracy: 0.698 +Epoch: 0736 cost = 0.078412282 +Epoch: 0737 cost = 0.087022203 +Epoch: 0738 cost = 0.072287295 +Epoch: 0739 cost = 0.074129866 +Epoch: 0740 cost = 0.070785672 +Accuracy: 0.706 +Epoch: 0741 cost = 0.064805927 +Epoch: 0742 cost = 0.079868757 +Epoch: 0743 cost = 0.071447790 +Epoch: 0744 cost = 0.069862370 +Epoch: 0745 cost = 0.074266867 +Accuracy: 0.676 +Epoch: 0746 cost = 0.077721770 +Epoch: 0747 cost = 0.070636807 +Epoch: 0748 cost = 0.069656424 +Epoch: 0749 cost = 0.067379052 +Epoch: 0750 cost = 0.075178459 +Accuracy: 0.678 +Epoch: 0751 cost = 0.083935580 +Epoch: 0752 cost = 0.074249651 +Epoch: 0753 cost = 0.069557960 +Epoch: 0754 cost = 0.074536882 +Epoch: 0755 cost = 0.066169492 Accuracy: 0.688 -Epoch: 0316 cost = 0.243980275 -Epoch: 0317 cost = 0.225476449 -Epoch: 0318 cost = 0.238543987 -Epoch: 0319 cost = 0.238320933 -Epoch: 0320 cost = 0.261743346 +Epoch: 0756 cost = 0.077443903 +Epoch: 0757 cost = 0.072958820 +Epoch: 0758 cost = 0.082690764 +Epoch: 0759 cost = 0.065163857 +Epoch: 0760 cost = 0.067758314 +Accuracy: 0.696 +Epoch: 0761 cost = 0.074970884 +Epoch: 0762 cost = 0.068864436 +Epoch: 0763 cost = 0.074339888 +Epoch: 0764 cost = 0.067973309 +Epoch: 0765 cost = 0.073645321 +Accuracy: 0.698 +Epoch: 0766 cost = 0.074304814 +Epoch: 0767 cost = 0.073705268 +Epoch: 0768 cost = 0.060696671 +Epoch: 0769 cost = 0.061486270 +Epoch: 0770 cost = 0.067626831 +Accuracy: 0.694 +Epoch: 0771 cost = 0.069496351 +Epoch: 0772 cost = 0.074570409 +Epoch: 0773 cost = 0.066760784 +Epoch: 0774 cost = 0.070322840 +Epoch: 0775 cost = 0.063675705 +Accuracy: 0.674 +Epoch: 0776 cost = 0.067605669 +Epoch: 0777 cost = 0.082359258 +Epoch: 0778 cost = 0.079232910 +Epoch: 0779 cost = 0.066667000 +Epoch: 0780 cost = 0.074512960 +Accuracy: 0.718 +Epoch: 0781 cost = 0.072225336 +Epoch: 0782 cost = 0.073489279 +Epoch: 0783 cost = 0.073959650 +Epoch: 0784 cost = 0.070778199 +Epoch: 0785 cost = 0.069987658 +Accuracy: 0.666 +Epoch: 0786 cost = 0.065113335 +Epoch: 0787 cost = 0.074492098 +Epoch: 0788 cost = 0.068588954 +Epoch: 0789 cost = 0.065065401 +Epoch: 0790 cost = 0.081355163 +Accuracy: 0.672 +Epoch: 0791 cost = 0.064999981 +Epoch: 0792 cost = 0.064976724 +Epoch: 0793 cost = 0.058262304 +Epoch: 0794 cost = 0.072000359 +Epoch: 0795 cost = 0.069363772 +Accuracy: 0.712 +Epoch: 0796 cost = 0.063163055 +Epoch: 0797 cost = 0.075408488 +Epoch: 0798 cost = 0.075544576 +Epoch: 0799 cost = 0.070997118 +Epoch: 0800 cost = 0.066764026 +Accuracy: 0.658 +Epoch: 0801 cost = 0.071323923 +Epoch: 0802 cost = 0.071396704 +Epoch: 0803 cost = 0.073346549 +Epoch: 0804 cost = 0.063148788 +Epoch: 0805 cost = 0.070564414 Accuracy: 0.714 -Epoch: 0321 cost = 0.224811815 -Epoch: 0322 cost = 0.241242706 -Epoch: 0323 cost = 0.241196357 -Epoch: 0324 cost = 0.225010265 -Epoch: 0325 cost = 0.233388552 -Accuracy: 0.686 -Epoch: 0326 cost = 0.236988868 -Epoch: 0327 cost = 0.224576655 -Epoch: 0328 cost = 0.216505816 -Epoch: 0329 cost = 0.234341587 -Epoch: 0330 cost = 0.221781501 +Epoch: 0806 cost = 0.069237668 +Epoch: 0807 cost = 0.071220609 +Epoch: 0808 cost = 0.067307036 +Epoch: 0809 cost = 0.065021418 +Epoch: 0810 cost = 0.066466184 +Accuracy: 0.662 +Epoch: 0811 cost = 0.064388882 +Epoch: 0812 cost = 0.061154101 +Epoch: 0813 cost = 0.065146338 +Epoch: 0814 cost = 0.065792308 +Epoch: 0815 cost = 0.065604795 +Accuracy: 0.722 +Epoch: 0816 cost = 0.070103372 +Epoch: 0817 cost = 0.067631304 +Epoch: 0818 cost = 0.063874181 +Epoch: 0819 cost = 0.064563165 +Epoch: 0820 cost = 0.067057953 Accuracy: 0.698 -Epoch: 0331 cost = 0.231224662 -Epoch: 0332 cost = 0.234261046 -Epoch: 0333 cost = 0.230843339 -Epoch: 0334 cost = 0.216231964 -Epoch: 0335 cost = 0.215144413 -Accuracy: 0.704 -Epoch: 0336 cost = 0.205591729 -Epoch: 0337 cost = 0.228962516 -Epoch: 0338 cost = 0.230936582 -Epoch: 0339 cost = 0.224457032 -Epoch: 0340 cost = 0.216019266 -Accuracy: 0.69 -Epoch: 0341 cost = 0.223690010 -Epoch: 0342 cost = 0.216028717 -Epoch: 0343 cost = 0.228029779 -Epoch: 0344 cost = 0.218507429 -Epoch: 0345 cost = 0.205987243 -Accuracy: 0.704 -Epoch: 0346 cost = 0.222720423 -Epoch: 0347 cost = 0.210164854 -Epoch: 0348 cost = 0.217193825 -Epoch: 0349 cost = 0.205538029 -Epoch: 0350 cost = 0.215030146 -Accuracy: 0.68 -Epoch: 0351 cost = 0.208047377 -Epoch: 0352 cost = 0.206625841 -Epoch: 0353 cost = 0.200706393 -Epoch: 0354 cost = 0.195989668 -Epoch: 0355 cost = 0.206576956 -Accuracy: 0.648 -Epoch: 0356 cost = 0.202053510 -Epoch: 0357 cost = 0.208723881 -Epoch: 0358 cost = 0.205642292 -Epoch: 0359 cost = 0.195928207 -Epoch: 0360 cost = 0.203882499 -Accuracy: 0.67 -Epoch: 0361 cost = 0.194690624 -Epoch: 0362 cost = 0.196908630 -Epoch: 0363 cost = 0.202135414 -Epoch: 0364 cost = 0.199923388 -Epoch: 0365 cost = 0.196365428 -Accuracy: 0.68 -Epoch: 0366 cost = 0.185447128 -Epoch: 0367 cost = 0.196574427 -Epoch: 0368 cost = 0.195564287 -Epoch: 0369 cost = 0.180336359 -Epoch: 0370 cost = 0.194343807 +Epoch: 0821 cost = 0.066383514 +Epoch: 0822 cost = 0.066660329 +Epoch: 0823 cost = 0.071668208 +Epoch: 0824 cost = 0.069936651 +Epoch: 0825 cost = 0.071631262 +Accuracy: 0.672 +Epoch: 0826 cost = 0.072716121 +Epoch: 0827 cost = 0.068351331 +Epoch: 0828 cost = 0.067274264 +Epoch: 0829 cost = 0.062215532 +Epoch: 0830 cost = 0.071385580 +Accuracy: 0.722 +Epoch: 0831 cost = 0.064806343 +Epoch: 0832 cost = 0.068508252 +Epoch: 0833 cost = 0.067728065 +Epoch: 0834 cost = 0.069978602 +Epoch: 0835 cost = 0.068967767 +Accuracy: 0.714 +Epoch: 0836 cost = 0.060106990 +Epoch: 0837 cost = 0.066814006 +Epoch: 0838 cost = 0.068422697 +Epoch: 0839 cost = 0.066933333 +Epoch: 0840 cost = 0.073591868 +Accuracy: 0.668 +Epoch: 0841 cost = 0.064896918 +Epoch: 0842 cost = 0.060275489 +Epoch: 0843 cost = 0.070521284 +Epoch: 0844 cost = 0.069509461 +Epoch: 0845 cost = 0.059069286 +Accuracy: 0.668 +Epoch: 0846 cost = 0.064197837 +Epoch: 0847 cost = 0.066215538 +Epoch: 0848 cost = 0.063521285 +Epoch: 0849 cost = 0.062673823 +Epoch: 0850 cost = 0.066386973 +Accuracy: 0.718 +Epoch: 0851 cost = 0.066502453 +Epoch: 0852 cost = 0.067723286 +Epoch: 0853 cost = 0.065931809 +Epoch: 0854 cost = 0.057765830 +Epoch: 0855 cost = 0.058214500 +Accuracy: 0.7 +Epoch: 0856 cost = 0.056357275 +Epoch: 0857 cost = 0.061915118 +Epoch: 0858 cost = 0.065098835 +Epoch: 0859 cost = 0.051987287 +Epoch: 0860 cost = 0.065616198 +Accuracy: 0.706 +Epoch: 0861 cost = 0.067218428 +Epoch: 0862 cost = 0.069264353 +Epoch: 0863 cost = 0.065152063 +Epoch: 0864 cost = 0.053601146 +Epoch: 0865 cost = 0.063694923 +Accuracy: 0.7 +Epoch: 0866 cost = 0.063854205 +Epoch: 0867 cost = 0.060727451 +Epoch: 0868 cost = 0.072739810 +Epoch: 0869 cost = 0.058416205 +Epoch: 0870 cost = 0.057253874 +Accuracy: 0.672 +Epoch: 0871 cost = 0.061591660 +Epoch: 0872 cost = 0.069930125 +Epoch: 0873 cost = 0.066954957 +Epoch: 0874 cost = 0.064314812 +Epoch: 0875 cost = 0.058990470 Accuracy: 0.688 -Epoch: 0371 cost = 0.186285626 -Epoch: 0372 cost = 0.190013710 -Epoch: 0373 cost = 0.197687892 -Epoch: 0374 cost = 0.184584371 -Epoch: 0375 cost = 0.190913253 +Epoch: 0876 cost = 0.065380115 +Epoch: 0877 cost = 0.061019783 +Epoch: 0878 cost = 0.072920567 +Epoch: 0879 cost = 0.059825727 +Epoch: 0880 cost = 0.054957121 +Accuracy: 0.71 +Epoch: 0881 cost = 0.055641020 +Epoch: 0882 cost = 0.069315541 +Epoch: 0883 cost = 0.070773316 +Epoch: 0884 cost = 0.061714934 +Epoch: 0885 cost = 0.064786573 +Accuracy: 0.676 +Epoch: 0886 cost = 0.058506874 +Epoch: 0887 cost = 0.053162961 +Epoch: 0888 cost = 0.058588296 +Epoch: 0889 cost = 0.056947990 +Epoch: 0890 cost = 0.056197341 Accuracy: 0.662 -Epoch: 0376 cost = 0.194012809 -Epoch: 0377 cost = 0.179898899 -Epoch: 0378 cost = 0.185747168 -Epoch: 0379 cost = 0.183469582 -Epoch: 0380 cost = 0.191591957 -Accuracy: 0.69 -Epoch: 0381 cost = 0.176704920 -Epoch: 0382 cost = 0.184566157 -Epoch: 0383 cost = 0.185613967 -Epoch: 0384 cost = 0.182787482 -Epoch: 0385 cost = 0.181154740 -Accuracy: 0.66 -Epoch: 0386 cost = 0.183127590 -Epoch: 0387 cost = 0.183282609 -Epoch: 0388 cost = 0.181939080 -Epoch: 0389 cost = 0.186416831 -Epoch: 0390 cost = 0.184981587 -Accuracy: 0.69 -Epoch: 0391 cost = 0.170424865 -Epoch: 0392 cost = 0.177383073 -Epoch: 0393 cost = 0.177053552 -Epoch: 0394 cost = 0.169662276 -Epoch: 0395 cost = 0.178390077 +Epoch: 0891 cost = 0.053608599 +Epoch: 0892 cost = 0.057299671 +Epoch: 0893 cost = 0.064839569 +Epoch: 0894 cost = 0.063126703 +Epoch: 0895 cost = 0.060581908 Accuracy: 0.674 -Epoch: 0396 cost = 0.170196023 -Epoch: 0397 cost = 0.182006982 -Epoch: 0398 cost = 0.180935718 -Epoch: 0399 cost = 0.170829106 -Epoch: 0400 cost = 0.174306042 +Epoch: 0896 cost = 0.061896213 +Epoch: 0897 cost = 0.062474460 +Epoch: 0898 cost = 0.053822067 +Epoch: 0899 cost = 0.054831581 +Epoch: 0900 cost = 0.057487616 +Accuracy: 0.664 +Epoch: 0901 cost = 0.061407796 +Epoch: 0902 cost = 0.063262411 +Epoch: 0903 cost = 0.060156831 +Epoch: 0904 cost = 0.063797427 +Epoch: 0905 cost = 0.057246930 Accuracy: 0.714 -Epoch: 0401 cost = 0.176974503 -Epoch: 0402 cost = 0.172893045 -Epoch: 0403 cost = 0.170582323 -Epoch: 0404 cost = 0.173241530 -Epoch: 0405 cost = 0.180335288 +Epoch: 0906 cost = 0.061415824 +Epoch: 0907 cost = 0.061439266 +Epoch: 0908 cost = 0.059584000 +Epoch: 0909 cost = 0.060202942 +Epoch: 0910 cost = 0.057967622 +Accuracy: 0.694 +Epoch: 0911 cost = 0.061371875 +Epoch: 0912 cost = 0.064548688 +Epoch: 0913 cost = 0.056517944 +Epoch: 0914 cost = 0.066069558 +Epoch: 0915 cost = 0.056570699 +Accuracy: 0.672 +Epoch: 0916 cost = 0.064253080 +Epoch: 0917 cost = 0.063378144 +Epoch: 0918 cost = 0.055716677 +Epoch: 0919 cost = 0.068958076 +Epoch: 0920 cost = 0.058496159 +Accuracy: 0.716 +Epoch: 0921 cost = 0.060851873 +Epoch: 0922 cost = 0.057135941 +Epoch: 0923 cost = 0.062078325 +Epoch: 0924 cost = 0.059552558 +Epoch: 0925 cost = 0.051064800 +Accuracy: 0.738 +Epoch: 0926 cost = 0.059769198 +Epoch: 0927 cost = 0.052492554 +Epoch: 0928 cost = 0.055357167 +Epoch: 0929 cost = 0.052062423 +Epoch: 0930 cost = 0.063110295 +Accuracy: 0.672 +Epoch: 0931 cost = 0.057482306 +Epoch: 0932 cost = 0.060245227 +Epoch: 0933 cost = 0.050969369 +Epoch: 0934 cost = 0.051949743 +Epoch: 0935 cost = 0.061940453 Accuracy: 0.668 -Epoch: 0406 cost = 0.179313468 -Epoch: 0407 cost = 0.180736936 -Epoch: 0408 cost = 0.175959413 -Epoch: 0409 cost = 0.174506581 -Epoch: 0410 cost = 0.163335416 -Accuracy: 0.658 -Epoch: 0411 cost = 0.170097443 -Epoch: 0412 cost = 0.158451459 -Epoch: 0413 cost = 0.156419013 -Epoch: 0414 cost = 0.175325938 -Epoch: 0415 cost = 0.155034284 +Epoch: 0936 cost = 0.060474277 +Epoch: 0937 cost = 0.055633695 +Epoch: 0938 cost = 0.055032202 +Epoch: 0939 cost = 0.054478046 +Epoch: 0940 cost = 0.061125622 +Accuracy: 0.698 +Epoch: 0941 cost = 0.053515617 +Epoch: 0942 cost = 0.055082102 +Epoch: 0943 cost = 0.065794243 +Epoch: 0944 cost = 0.051559906 +Epoch: 0945 cost = 0.052938675 +Accuracy: 0.686 +Epoch: 0946 cost = 0.056229838 +Epoch: 0947 cost = 0.068344982 +Epoch: 0948 cost = 0.060470650 +Epoch: 0949 cost = 0.062149456 +Epoch: 0950 cost = 0.056360640 Accuracy: 0.69 -Epoch: 0416 cost = 0.164153294 -Epoch: 0417 cost = 0.155209700 -Epoch: 0418 cost = 0.167793508 -Epoch: 0419 cost = 0.157012567 -Epoch: 0420 cost = 0.171157722 -Accuracy: 0.684 -Epoch: 0421 cost = 0.157307838 -Epoch: 0422 cost = 0.161628031 -Epoch: 0423 cost = 0.158428192 -Epoch: 0424 cost = 0.158813586 -Epoch: 0425 cost = 0.171617052 -Accuracy: 0.692 -Epoch: 0426 cost = 0.164463258 -Epoch: 0427 cost = 0.160532972 -Epoch: 0428 cost = 0.157867650 -Epoch: 0429 cost = 0.154883435 -Epoch: 0430 cost = 0.158418911 -Accuracy: 0.652 -Epoch: 0431 cost = 0.145480454 -Epoch: 0432 cost = 0.155923912 -Epoch: 0433 cost = 0.157752631 -Epoch: 0434 cost = 0.155943191 -Epoch: 0435 cost = 0.158748155 -Accuracy: 0.694 -Epoch: 0436 cost = 0.160792182 -Epoch: 0437 cost = 0.155377477 -Epoch: 0438 cost = 0.157744526 -Epoch: 0439 cost = 0.155427273 -Epoch: 0440 cost = 0.159342458 -Accuracy: 0.678 -Epoch: 0441 cost = 0.152804907 -Epoch: 0442 cost = 0.162825121 -Epoch: 0443 cost = 0.141242604 -Epoch: 0444 cost = 0.140490852 -Epoch: 0445 cost = 0.142243742 -Accuracy: 0.682 -Epoch: 0446 cost = 0.164571498 -Epoch: 0447 cost = 0.143767359 -Epoch: 0448 cost = 0.153597356 -Epoch: 0449 cost = 0.145654636 -Epoch: 0450 cost = 0.142549302 -Accuracy: 0.646 -Epoch: 0451 cost = 0.150071420 -Epoch: 0452 cost = 0.152995880 -Epoch: 0453 cost = 0.140282794 -Epoch: 0454 cost = 0.148593902 -Epoch: 0455 cost = 0.139312753 +Epoch: 0951 cost = 0.056139875 +Epoch: 0952 cost = 0.060180753 +Epoch: 0953 cost = 0.061242194 +Epoch: 0954 cost = 0.063164885 +Epoch: 0955 cost = 0.050292767 +Accuracy: 0.704 +Epoch: 0956 cost = 0.059737396 +Epoch: 0957 cost = 0.060242107 +Epoch: 0958 cost = 0.055101492 +Epoch: 0959 cost = 0.056546976 +Epoch: 0960 cost = 0.056298839 +Accuracy: 0.688 +Epoch: 0961 cost = 0.054481870 +Epoch: 0962 cost = 0.052015899 +Epoch: 0963 cost = 0.053080680 +Epoch: 0964 cost = 0.049128262 +Epoch: 0965 cost = 0.060047859 +Accuracy: 0.712 +Epoch: 0966 cost = 0.058197484 +Epoch: 0967 cost = 0.055430211 +Epoch: 0968 cost = 0.060055969 +Epoch: 0969 cost = 0.057353539 +Epoch: 0970 cost = 0.052863473 Accuracy: 0.674 -Epoch: 0456 cost = 0.148530192 -Epoch: 0457 cost = 0.147251993 -Epoch: 0458 cost = 0.143778329 -Epoch: 0459 cost = 0.141319702 -Epoch: 0460 cost = 0.135436040 +Epoch: 0971 cost = 0.047826266 +Epoch: 0972 cost = 0.061775207 +Epoch: 0973 cost = 0.054963879 +Epoch: 0974 cost = 0.063281204 +Epoch: 0975 cost = 0.055244087 +Accuracy: 0.698 +Epoch: 0976 cost = 0.052140954 +Epoch: 0977 cost = 0.057382621 +Epoch: 0978 cost = 0.061556515 +Epoch: 0979 cost = 0.059834863 +Epoch: 0980 cost = 0.051565077 +Accuracy: 0.702 +Epoch: 0981 cost = 0.054002538 +Epoch: 0982 cost = 0.061830626 +Epoch: 0983 cost = 0.046204289 +Epoch: 0984 cost = 0.053296131 +Epoch: 0985 cost = 0.052310934 Accuracy: 0.684 -Epoch: 0461 cost = 0.131511842 -Epoch: 0462 cost = 0.145072434 -Epoch: 0463 cost = 0.144240762 -Epoch: 0464 cost = 0.143982705 -Epoch: 0465 cost = 0.129912225 +Epoch: 0986 cost = 0.053202674 +Epoch: 0987 cost = 0.050281012 +Epoch: 0988 cost = 0.051755328 +Epoch: 0989 cost = 0.060923374 +Epoch: 0990 cost = 0.053844850 +Accuracy: 0.702 +Epoch: 0991 cost = 0.056312708 +Epoch: 0992 cost = 0.058702079 +Epoch: 0993 cost = 0.057861557 +Epoch: 0994 cost = 0.054028912 +Epoch: 0995 cost = 0.056530749 +Accuracy: 0.728 +Epoch: 0996 cost = 0.059500123 +Epoch: 0997 cost = 0.050259224 +Epoch: 0998 cost = 0.054346314 +Epoch: 0999 cost = 0.054847696 +Epoch: 1000 cost = 0.055496770 +Accuracy: 0.662 +Epoch: 1001 cost = 0.049739120 +Epoch: 1002 cost = 0.058681662 +Epoch: 1003 cost = 0.056421959 +Epoch: 1004 cost = 0.053799691 +Epoch: 1005 cost = 0.055670471 Accuracy: 0.672 -Epoch: 0466 cost = 0.138561623 -Epoch: 0467 cost = 0.133583417 -Epoch: 0468 cost = 0.131267091 -Epoch: 0469 cost = 0.144252766 -Epoch: 0470 cost = 0.138372278 -Accuracy: 0.648 -Epoch: 0471 cost = 0.127405322 -Epoch: 0472 cost = 0.133263303 -Epoch: 0473 cost = 0.134148620 -Epoch: 0474 cost = 0.147869974 -Epoch: 0475 cost = 0.139710613 -Accuracy: 0.694 -Epoch: 0476 cost = 0.131028253 -Epoch: 0477 cost = 0.126008114 -Epoch: 0478 cost = 0.150548821 -Epoch: 0479 cost = 0.125722083 -Epoch: 0480 cost = 0.131007737 +Epoch: 1006 cost = 0.053713582 +Epoch: 1007 cost = 0.058783552 +Epoch: 1008 cost = 0.054619751 +Epoch: 1009 cost = 0.056878992 +Epoch: 1010 cost = 0.056031072 +Accuracy: 0.688 +Epoch: 1011 cost = 0.048446192 +Epoch: 1012 cost = 0.056050151 +Epoch: 1013 cost = 0.054149641 +Epoch: 1014 cost = 0.052401353 +Epoch: 1015 cost = 0.057565923 Accuracy: 0.668 -Epoch: 0481 cost = 0.131263324 -Epoch: 0482 cost = 0.140277008 -Epoch: 0483 cost = 0.123865877 -Epoch: 0484 cost = 0.136453279 -Epoch: 0485 cost = 0.126934629 +Epoch: 1016 cost = 0.049931960 +Epoch: 1017 cost = 0.056090683 +Epoch: 1018 cost = 0.050616200 +Epoch: 1019 cost = 0.047297842 +Epoch: 1020 cost = 0.049562548 +Accuracy: 0.698 +Epoch: 1021 cost = 0.050748166 +Epoch: 1022 cost = 0.059514189 +Epoch: 1023 cost = 0.064037815 +Epoch: 1024 cost = 0.055273722 +Epoch: 1025 cost = 0.048792946 Accuracy: 0.686 -Epoch: 0486 cost = 0.126405641 -Epoch: 0487 cost = 0.134560895 -Epoch: 0488 cost = 0.132346562 -Epoch: 0489 cost = 0.126915155 -Epoch: 0490 cost = 0.133014086 -Accuracy: 0.66 -Epoch: 0491 cost = 0.128638323 -Epoch: 0492 cost = 0.130443529 -Epoch: 0493 cost = 0.126753899 -Epoch: 0494 cost = 0.114836649 -Epoch: 0495 cost = 0.141375982 +Epoch: 1026 cost = 0.063108496 +Epoch: 1027 cost = 0.053023006 +Epoch: 1028 cost = 0.053812527 +Epoch: 1029 cost = 0.048790418 +Epoch: 1030 cost = 0.066514245 +Accuracy: 0.702 +Epoch: 1031 cost = 0.048990203 +Epoch: 1032 cost = 0.049538861 +Epoch: 1033 cost = 0.051852965 +Epoch: 1034 cost = 0.056210315 +Epoch: 1035 cost = 0.049907236 +Accuracy: 0.698 +Epoch: 1036 cost = 0.056104024 +Epoch: 1037 cost = 0.063412431 +Epoch: 1038 cost = 0.055721287 +Epoch: 1039 cost = 0.053670372 +Epoch: 1040 cost = 0.044657694 +Accuracy: 0.678 +Epoch: 1041 cost = 0.045627255 +Epoch: 1042 cost = 0.050198682 +Epoch: 1043 cost = 0.056517361 +Epoch: 1044 cost = 0.061733431 +Epoch: 1045 cost = 0.065162225 Accuracy: 0.708 -Epoch: 0496 cost = 0.139420264 -Epoch: 0497 cost = 0.134975031 -Epoch: 0498 cost = 0.124961590 -Epoch: 0499 cost = 0.130409865 -Epoch: 0500 cost = 0.124366996 +Epoch: 1046 cost = 0.054999334 +Epoch: 1047 cost = 0.048225252 +Epoch: 1048 cost = 0.053408730 +Epoch: 1049 cost = 0.057958559 +Epoch: 1050 cost = 0.050596313 Accuracy: 0.686 -Epoch: 0501 cost = 0.130502676 -Epoch: 0502 cost = 0.130456836 -Epoch: 0503 cost = 0.124294621 -Epoch: 0504 cost = 0.123371773 -Epoch: 0505 cost = 0.138835895 -Accuracy: 0.726 -Epoch: 0506 cost = 0.127717276 -Epoch: 0507 cost = 0.118944810 -Epoch: 0508 cost = 0.120451637 -Epoch: 0509 cost = 0.124473464 -Epoch: 0510 cost = 0.131683494 -Accuracy: 0.67 -Epoch: 0511 cost = 0.126660948 -Epoch: 0512 cost = 0.130330144 -Epoch: 0513 cost = 0.126406845 -Epoch: 0514 cost = 0.137334209 -Epoch: 0515 cost = 0.129676421 -Accuracy: 0.702 -Epoch: 0516 cost = 0.126082168 -Epoch: 0517 cost = 0.140236827 -Epoch: 0518 cost = 0.115603044 -Epoch: 0519 cost = 0.118083283 -Epoch: 0520 cost = 0.125525745 +Epoch: 1051 cost = 0.047923279 +Epoch: 1052 cost = 0.052132167 +Epoch: 1053 cost = 0.044652478 +Epoch: 1054 cost = 0.043851709 +Epoch: 1055 cost = 0.057625328 +Accuracy: 0.672 +Epoch: 1056 cost = 0.051447250 +Epoch: 1057 cost = 0.047989712 +Epoch: 1058 cost = 0.050256235 +Epoch: 1059 cost = 0.052407728 +Epoch: 1060 cost = 0.060057185 +Accuracy: 0.69 +Epoch: 1061 cost = 0.048864654 +Epoch: 1062 cost = 0.050839575 +Epoch: 1063 cost = 0.051276413 +Epoch: 1064 cost = 0.053576734 +Epoch: 1065 cost = 0.056270595 Accuracy: 0.67 -Epoch: 0521 cost = 0.111245054 -Epoch: 0522 cost = 0.121379045 -Epoch: 0523 cost = 0.122450649 -Epoch: 0524 cost = 0.118737362 -Epoch: 0525 cost = 0.132155129 +Epoch: 1066 cost = 0.055796233 +Epoch: 1067 cost = 0.054082064 +Epoch: 1068 cost = 0.062568208 +Epoch: 1069 cost = 0.052526650 +Epoch: 1070 cost = 0.054237034 +Accuracy: 0.676 +Epoch: 1071 cost = 0.045284928 +Epoch: 1072 cost = 0.048139665 +Epoch: 1073 cost = 0.063083464 +Epoch: 1074 cost = 0.050543990 +Epoch: 1075 cost = 0.054381826 Accuracy: 0.68 -Epoch: 0526 cost = 0.120026794 -Epoch: 0527 cost = 0.113095676 -Epoch: 0528 cost = 0.111921014 -Epoch: 0529 cost = 0.110120336 -Epoch: 0530 cost = 0.120337588 -Accuracy: 0.678 -Epoch: 0531 cost = 0.119837387 -Epoch: 0532 cost = 0.114530141 -Epoch: 0533 cost = 0.117952761 -Epoch: 0534 cost = 0.120651463 -Epoch: 0535 cost = 0.115092028 -Accuracy: 0.674 -Epoch: 0536 cost = 0.119301189 -Epoch: 0537 cost = 0.109547124 -Epoch: 0538 cost = 0.114721535 -Epoch: 0539 cost = 0.116361326 -Epoch: 0540 cost = 0.111751930 -Accuracy: 0.666 -Epoch: 0541 cost = 0.108961816 -Epoch: 0542 cost = 0.116229246 -Epoch: 0543 cost = 0.121288770 -Epoch: 0544 cost = 0.117621829 -Epoch: 0545 cost = 0.119737629 -Accuracy: 0.686 -Epoch: 0546 cost = 0.110438816 -Epoch: 0547 cost = 0.111064304 -Epoch: 0548 cost = 0.107804560 -Epoch: 0549 cost = 0.120911170 -Epoch: 0550 cost = 0.114649650 -Accuracy: 0.684 -Epoch: 0551 cost = 0.107714061 -Epoch: 0552 cost = 0.111656883 -Epoch: 0553 cost = 0.114752793 -Epoch: 0554 cost = 0.110536426 -Epoch: 0555 cost = 0.118712040 +Epoch: 1076 cost = 0.064830651 +Epoch: 1077 cost = 0.048150347 +Epoch: 1078 cost = 0.052893579 +Epoch: 1079 cost = 0.051429521 +Epoch: 1080 cost = 0.047955453 +Accuracy: 0.714 +Epoch: 1081 cost = 0.049678719 +Epoch: 1082 cost = 0.046997013 +Epoch: 1083 cost = 0.042382468 +Epoch: 1084 cost = 0.048214442 +Epoch: 1085 cost = 0.046367653 +Accuracy: 0.694 +Epoch: 1086 cost = 0.049382495 +Epoch: 1087 cost = 0.060358900 +Epoch: 1088 cost = 0.045438657 +Epoch: 1089 cost = 0.050064196 +Epoch: 1090 cost = 0.049497317 Accuracy: 0.674 -Epoch: 0556 cost = 0.106774314 -Epoch: 0557 cost = 0.111336236 -Epoch: 0558 cost = 0.110485898 -Epoch: 0559 cost = 0.100509318 -Epoch: 0560 cost = 0.107867145 -Accuracy: 0.698 -Epoch: 0561 cost = 0.106597122 -Epoch: 0562 cost = 0.109421511 -Epoch: 0563 cost = 0.102338426 -Epoch: 0564 cost = 0.121696794 -Epoch: 0565 cost = 0.109283897 +Epoch: 1091 cost = 0.047528197 +Epoch: 1092 cost = 0.051821553 +Epoch: 1093 cost = 0.058831133 +Epoch: 1094 cost = 0.051810294 +Epoch: 1095 cost = 0.056997394 Accuracy: 0.704 -Epoch: 0566 cost = 0.105254890 -Epoch: 0567 cost = 0.121927035 -Epoch: 0568 cost = 0.114748673 -Epoch: 0569 cost = 0.105509866 -Epoch: 0570 cost = 0.116994424 -Accuracy: 0.69 -Epoch: 0571 cost = 0.106082235 -Epoch: 0572 cost = 0.107428371 -Epoch: 0573 cost = 0.104347722 -Epoch: 0574 cost = 0.114010719 -Epoch: 0575 cost = 0.111764481 -Accuracy: 0.682 -Epoch: 0576 cost = 0.114344178 -Epoch: 0577 cost = 0.111395085 -Epoch: 0578 cost = 0.103568691 -Epoch: 0579 cost = 0.098618215 -Epoch: 0580 cost = 0.111957005 -Accuracy: 0.668 -Epoch: 0581 cost = 0.113367702 -Epoch: 0582 cost = 0.109154763 -Epoch: 0583 cost = 0.107204894 -Epoch: 0584 cost = 0.111432658 -Epoch: 0585 cost = 0.112998830 -Accuracy: 0.696 -Epoch: 0586 cost = 0.098543316 -Epoch: 0587 cost = 0.095304508 -Epoch: 0588 cost = 0.116752032 -Epoch: 0589 cost = 0.104097747 -Epoch: 0590 cost = 0.109815498 -Accuracy: 0.69 -Epoch: 0591 cost = 0.091209450 -Epoch: 0592 cost = 0.096193801 -Epoch: 0593 cost = 0.095926440 -Epoch: 0594 cost = 0.096929071 -Epoch: 0595 cost = 0.103870244 -Accuracy: 0.694 -Epoch: 0596 cost = 0.102161529 -Epoch: 0597 cost = 0.099916381 -Epoch: 0598 cost = 0.100649900 -Epoch: 0599 cost = 0.098425770 -Epoch: 0600 cost = 0.095496017 -Accuracy: 0.696 -Epoch: 0601 cost = 0.102313055 -Epoch: 0602 cost = 0.101724144 -Epoch: 0603 cost = 0.096210864 -Epoch: 0604 cost = 0.100884899 -Epoch: 0605 cost = 0.097470764 -Accuracy: 0.692 -Epoch: 0606 cost = 0.100695437 -Epoch: 0607 cost = 0.092505307 -Epoch: 0608 cost = 0.094749211 -Epoch: 0609 cost = 0.088997761 -Epoch: 0610 cost = 0.098355950 -Accuracy: 0.682 -Epoch: 0611 cost = 0.100073429 -Epoch: 0612 cost = 0.098362630 -Epoch: 0613 cost = 0.092072710 -Epoch: 0614 cost = 0.088043539 -Epoch: 0615 cost = 0.106211394 -Accuracy: 0.692 -Epoch: 0616 cost = 0.114533837 -Epoch: 0617 cost = 0.099485306 -Epoch: 0618 cost = 0.107752155 -Epoch: 0619 cost = 0.088217394 -Epoch: 0620 cost = 0.101134382 -Accuracy: 0.672 -Epoch: 0621 cost = 0.094724528 -Epoch: 0622 cost = 0.098326855 -Epoch: 0623 cost = 0.096618281 -Epoch: 0624 cost = 0.104486405 -Epoch: 0625 cost = 0.093760595 -Accuracy: 0.744 -Epoch: 0626 cost = 0.099997650 -Epoch: 0627 cost = 0.090708038 -Epoch: 0628 cost = 0.100836329 -Epoch: 0629 cost = 0.095824684 -Epoch: 0630 cost = 0.096236829 -Accuracy: 0.696 -Epoch: 0631 cost = 0.100781335 -Epoch: 0632 cost = 0.090005336 -Epoch: 0633 cost = 0.084376190 -Epoch: 0634 cost = 0.100622473 -Epoch: 0635 cost = 0.089239176 -Accuracy: 0.714 -Epoch: 0636 cost = 0.096699787 -Epoch: 0637 cost = 0.085737281 -Epoch: 0638 cost = 0.099683197 -Epoch: 0639 cost = 0.093491073 -Epoch: 0640 cost = 0.091517283 -Accuracy: 0.708 -Epoch: 0641 cost = 0.110236527 -Epoch: 0642 cost = 0.096306329 -Epoch: 0643 cost = 0.096605287 -Epoch: 0644 cost = 0.088369381 -Epoch: 0645 cost = 0.088198278 -Accuracy: 0.7 -Epoch: 0646 cost = 0.086842561 -Epoch: 0647 cost = 0.091289249 -Epoch: 0648 cost = 0.090074934 -Epoch: 0649 cost = 0.094352653 -Epoch: 0650 cost = 0.094865657 -Accuracy: 0.692 -Epoch: 0651 cost = 0.092150683 -Epoch: 0652 cost = 0.100330299 -Epoch: 0653 cost = 0.085904692 -Epoch: 0654 cost = 0.102988278 -Epoch: 0655 cost = 0.097860797 -Accuracy: 0.684 -Epoch: 0656 cost = 0.096874003 -Epoch: 0657 cost = 0.094748789 -Epoch: 0658 cost = 0.092886582 -Epoch: 0659 cost = 0.084694858 -Epoch: 0660 cost = 0.090067991 -Accuracy: 0.684 -Epoch: 0661 cost = 0.094678811 -Epoch: 0662 cost = 0.074775940 -Epoch: 0663 cost = 0.088533551 -Epoch: 0664 cost = 0.094765287 -Epoch: 0665 cost = 0.083389083 -Accuracy: 0.724 -Epoch: 0666 cost = 0.093813586 -Epoch: 0667 cost = 0.091408406 -Epoch: 0668 cost = 0.082687715 -Epoch: 0669 cost = 0.087257151 -Epoch: 0670 cost = 0.090747049 -Accuracy: 0.682 -Epoch: 0671 cost = 0.098532537 -Epoch: 0672 cost = 0.089712114 -Epoch: 0673 cost = 0.076336210 -Epoch: 0674 cost = 0.088785120 -Epoch: 0675 cost = 0.085748422 -Accuracy: 0.708 -Epoch: 0676 cost = 0.085310791 -Epoch: 0677 cost = 0.082828352 -Epoch: 0678 cost = 0.084820958 -Epoch: 0679 cost = 0.074392315 -Epoch: 0680 cost = 0.096775934 -Accuracy: 0.678 -Epoch: 0681 cost = 0.080296823 -Epoch: 0682 cost = 0.093049644 -Epoch: 0683 cost = 0.080510007 -Epoch: 0684 cost = 0.096826262 -Epoch: 0685 cost = 0.090120267 -Accuracy: 0.714 -Epoch: 0686 cost = 0.095147286 -Epoch: 0687 cost = 0.088083414 -Epoch: 0688 cost = 0.089795241 -Epoch: 0689 cost = 0.077340920 -Epoch: 0690 cost = 0.081029948 -Accuracy: 0.714 -Epoch: 0691 cost = 0.080220379 -Epoch: 0692 cost = 0.090332029 -Epoch: 0693 cost = 0.080781123 -Epoch: 0694 cost = 0.084629651 -Epoch: 0695 cost = 0.084704853 -Accuracy: 0.722 -Epoch: 0696 cost = 0.081616563 -Epoch: 0697 cost = 0.078657970 -Epoch: 0698 cost = 0.078298834 -Epoch: 0699 cost = 0.079918880 -Epoch: 0700 cost = 0.089530587 -Accuracy: 0.686 +Epoch: 1096 cost = 0.048722154 +Epoch: 1097 cost = 0.046894429 +Epoch: 1098 cost = 0.042995868 +Epoch: 1099 cost = 0.054932511 +Epoch: 1100 cost = 0.058484402 +Accuracy: 0.732 Finished! From 9f9f56466710e46e67aa1625d98a5f12a5939996 Mon Sep 17 00:00:00 2001 From: smallerhand Date: Wed, 2 May 2018 15:24:05 +0900 Subject: [PATCH 14/36] all --- x_ksh/README.md | 13 ++++++++++++- x_ksh/SA_cnn_ps3.py | 24 +++++++++++++----------- 2 files changed, 25 insertions(+), 12 deletions(-) diff --git a/x_ksh/README.md b/x_ksh/README.md index 1af346b..485ab3d 100644 --- a/x_ksh/README.md +++ b/x_ksh/README.md @@ -5,10 +5,21 @@ 아래로 갈수록 과거에 돌린 모델 (맨 위에 있는 게 현재 돌리고 있는 모델) -## 현재 돌리고 있는 거 (잘 돌아감) +## 현재 시도중인 모델 +
+1. core.stft
+
+2. mfcc 를 데이터 분산값 크게 들어가게 해서 20*430으로 자름.
+
+3. 이미 한거 : mfcc 최대값 많이 들어가는 걸로 20*430으로 잘라서 processing,
+트레이닝 모델은 크게 다르지 않음.
+
+ +## 완성 모델 1 - 67~74%(에폭 600~700 사이에서 67~74 왔다갔다 함) 
 preparing_data.py - 데이터 전처리
 SA_cnn_ps3.py - 모델
+아래 설명은 다시 수정해야 함.
preparing_data.py diff --git a/x_ksh/SA_cnn_ps3.py b/x_ksh/SA_cnn_ps3.py index 391ddc3..023e543 100644 --- a/x_ksh/SA_cnn_ps3.py +++ b/x_ksh/SA_cnn_ps3.py @@ -13,9 +13,9 @@ tf.set_random_seed(777) trainData = np.genfromtxt('/home/paperspace/Downloads/trainData6.csv', delimiter=',') -trainData = trainData.reshape(-1, 20, 200) +trainData = trainData.reshape(-1, 20, 430) testData = np.genfromtxt('/home/paperspace/Downloads/testData6.csv', delimiter=',') -testData = testData.reshape(-1, 20, 200) +testData = testData.reshape(-1, 20, 430) trainLabel = np.genfromtxt('/home/paperspace/Downloads/trainLabel6.csv', delimiter=',') testLabel = np.genfromtxt('/home/paperspace/Downloads/testLabel6.csv', delimiter=',') @@ -32,19 +32,19 @@ steps_for_validate = 5 #placeholder -X = tf.placeholder(tf.float32, [None, 20, 200], name="X") -X_sound = tf.reshape(X, [-1, 20, 200, 1]) +X = tf.placeholder(tf.float32, [None, 20, 430], name="X") +X_sound = tf.reshape(X, [-1, 20, 430, 1]) Y = tf.placeholder(tf.int32, [None, 1], name="Y") Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv") p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") # L1 SoundIn shape=(?, 20, 430, 1) -W1 = tf.get_variable("W1", shape=[2, 20, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +W1 = tf.get_variable("W1", shape=[2, 43, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME') L1 = tf.nn.elu(L1) L1 = tf.layers.batch_normalization(L1) -L1 = tf.nn.max_pool(L1, ksize=[1, 2, 20, 1],strides=[1, 2, 20, 1], padding='SAME') +L1 = tf.nn.max_pool(L1, ksize=[1, 2, 43, 1],strides=[1, 2, 43, 1], padding='SAME') L1 = tf.nn.dropout(L1, p_keep_conv) # L2 Input shape=(?,10,21,32) @@ -84,7 +84,6 @@ sess.run(tf.global_variables_initializer()) saver = tf.train.Saver() - # train my model print('Learning started. It takes sometime.') for epoch in range(training_epochs): @@ -96,13 +95,16 @@ feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .8, p_keep_hidden: 0.7} c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) avg_cost += c / total_batch - print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) if epoch % steps_for_validate == steps_for_validate-1: + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) - x=np.random.choice(testLabel.shape[0], 500, replace=False) - print('Accuracy:', sess.run(accuracy, feed_dict={ - X: testData[x], Y: testLabel[x].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + x1=np.random.choice(testLabel.shape[0], 300, replace=False) + print('TrainAccuracy:', sess.run(accuracy, feed_dict={ + X: trainData[x1], Y: trainLabel[x1].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + x2=np.random.choice(testLabel.shape[0], 300, replace=False) + print('TestAccuracy:', sess.run(accuracy, feed_dict={ + X: testData[x2], Y: testLabel[x2].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') print('Finished!') From aa633eec87d33c3d160b918bf2a8b5609bfdce1a Mon Sep 17 00:00:00 2001 From: smallerhand Date: Wed, 2 May 2018 15:24:27 +0900 Subject: [PATCH 15/36] all --- x_ksh/mfcc_processing.py | 32 ++++++++++--------- x_ksh/stft_processing.py | 66 ++++++++++++++++++++++++---------------- 2 files changed, 57 insertions(+), 41 deletions(-) diff --git a/x_ksh/mfcc_processing.py b/x_ksh/mfcc_processing.py index e1a93bb..5ee5fa5 100644 --- a/x_ksh/mfcc_processing.py +++ b/x_ksh/mfcc_processing.py @@ -54,11 +54,11 @@ def see_how_long(file): plt.plot(mfcc[2,]) plt.plot(np.abs(mfcc[3,])) -#2 seconds(200 segments) extract -def two_sec_extract(file): - #zero padding to file.shape[0] X 20 X 200 +#5 seconds(430 segments) extract +def five_sec_extract(file): + #zero padding to file.shape[0] X 20 X 430 n=file.shape[0] - array = np.repeat(0., n * 20 * 200).reshape(n, 20, 200) + array = np.repeat(0., n * 20 * 430).reshape(n, 20, 430) k=0 see = [] for filename in file: @@ -66,24 +66,26 @@ def two_sec_extract(file): mono=True, res_type="kaiser_fast") mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) length=mfcc.shape[1] - if length == 200: + if length == 430: array[k, :, :]=mfcc - elif length < 200: + elif length < 430: array[k, :, :length]=mfcc - elif length > 200: - sample = np.repeat(0., (length - 200)*20).reshape(20,length - 200) - for j in range(length - 200): + elif length > 430: + sample = np.repeat(0., (length - 430)*20).reshape(20,length - 430) + for j in range(length - 430): for i in range(20): - sample[i,j]=np.var(mfcc[i,j:j+200]) + sample[i,j]=np.var(mfcc[i,j:j+430]) A=np.argmax(sample, axis=1) start=np.argmax(np.bincount(A)) - array[k, :, :]=mfcc[:, start:start+200] + array[k, :, :]=mfcc[:, start:start+430] see.append(start) k+=1 return(array, see) -trainData, see1=two_sec_extract(trainfile) -testData, see2=two_sec_extract(testfile) +trainData, see1=five_sec_extract(trainfile) +testData, see2=five_sec_extract(testfile) +print(see1) +print(see2) print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape) # (6631, 20, 200) (2842, 20, 200) (6631,) (2842,) @@ -108,8 +110,8 @@ def Labeling(label): #0 40 0 40 #csv downdload totally about 600MB -trainData2D=trainData.reshape(-1, 20*200) -testData2D=testData.reshape(-1, 20*200) +trainData2D=trainData.reshape(-1, 20*430) +testData2D=testData.reshape(-1, 20*430) np.savetxt(path+'trainData6.csv', trainData2D, delimiter=",") np.savetxt(path+'testData6.csv', diff --git a/x_ksh/stft_processing.py b/x_ksh/stft_processing.py index 2495f51..3103d29 100644 --- a/x_ksh/stft_processing.py +++ b/x_ksh/stft_processing.py @@ -13,8 +13,8 @@ import tensorflow as tf tf.set_random_seed(777) import pandas as pd -#train = pd.read_csv('/Users/kimseunghyuck/desktop/sound_train.csv') -train = pd.read_csv('/home/paperspace/Downloads/audio_train.csv') +train = pd.read_csv('/Users/kimseunghyuck/desktop/sound_train.csv') +#train = pd.read_csv('/home/paperspace/Downloads/audio_train.csv') #train/test, Data/Label split from sklearn.model_selection import train_test_split @@ -25,8 +25,8 @@ testLabel = test_set.values[:,1] #data load and extract mfcc (scaling indluded) -#path = '/Users/kimseunghyuck/desktop/audio_train/' -path = '/home/paperspace/Downloads/audio_train/' +path = '/Users/kimseunghyuck/desktop/' +#path = '/home/paperspace/Downloads/' def see_how_long(file): c=[] @@ -51,34 +51,47 @@ def see_how_long(file): plt.plot(stft[3,]) plt.plot(stft[2,]) - -#2 seconds(100 segments) extract -def two_sec_extract(file): +filename=trainfile[10] + y, sr = librosa.core.load(path+'audio_train/'+filename, + mono=True, res_type="kaiser_fast") + stft=librosa.core.stft(y,1024,512) + mag, pha = librosa.magphase(stft) +mag, pha.real +plt.plot(mag[10]) +plt.show +plt.plot(pha.real[10]) +plt.show +#magnitude만 해도 될 것 같음. + +#5 seconds(100 segments) extract +def five_sec_extract(file): #zero padding to file.shape[0] X 513 X 100 n=file.shape[0] - array = np.repeat(0., n * 513 * 100).reshape(n, 513, 100) + array = np.repeat(0., n * 17 * 200).reshape(n, 17, 200) k=0 for filename in file: - y, sr = librosa.core.load(path+filename, mono=True, res_type="kaaiser_fast") - stft=librosa.core.stft(y,1024,512) + y, sr = librosa.core.load(path+'audio_train/'+filename, + mono=True, res_type="kaiser_fast") + stft=librosa.core.stft(y,32,16) + mag, pha = librosa.magphase(stft) length=stft.shape[1] - if length == 100: - array[k, :, :]=stft - elif length < 100: - array[k, :, :length]=stft - elif length > 100: - sample = np.repeat(0., (length - 100)*513).reshape(513,length - 100) - for j in range(length - 100): - for i in range(513): - sample[i,j]=np.var(stft[i,j:j+100]) + if length == 200: + array[k, :, :]=mag + elif length < 200: + array[k, :, :length]=mag + elif length > 200: + sample = np.repeat(0., (length - 200)*17).reshape(17,length - 200) + for j in range(length - 200): + for i in range(17): + sample[i,j]=np.var(mag[i,j:j+200]) A=np.argmax(sample, axis=1) start=np.argmax(np.bincount(A)) - array[k, :, :]=stft[:, start:start+100] + array[k, :, :]=mag[:, start:start+200] k+=1 return(array) -trainData=two_sec_extract(trainfile) -testData=two_sec_extract(testfile) +trainData=five_sec_extract(trainfile) +testData=five_sec_extract(testfile) print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape) # (6631, 20, 430) (2842, 20, 430) (6631,) (2842,) @@ -105,12 +118,13 @@ def Labeling(label): #csv downdload totally about 600MB trainData2D=trainData.reshape(-1, 513*100) testData2D=testData.reshape(-1, 513*100) -np.savetxt('/home/paperspace/Downloads/trainData6.csv', +np.savetxt(path+'trainData7.csv', trainData2D, delimiter=",") -np.savetxt('/home/paperspace/Downloads/testData6.csv', +np.savetxt(path+'testData7.csv', testData2D, delimiter=",") -np.savetxt('/home/paperspace/Downloads/trainLabel6.csv', +np.savetxt(path+'trainLabel7.csv', trainLabel, delimiter=",") -np.savetxt('/home/paperspace/Downloads/testLabel6.csv', +np.savetxt(path+'testLabel7.csv', testLabel, delimiter=",") + From 3eb8258fdb3e51cd77c0d71319802547c1a46dcd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EA=B3=BD=EC=83=81=EC=9A=B1?= <37833373+Trigger21@users.noreply.github.com> Date: Wed, 2 May 2018 15:25:01 +0900 Subject: [PATCH 16/36] Add files via upload --- x_ysw/mel_cnn.py | 63 ++++++++++++++++++++++++++++++++++++------------ 1 file changed, 47 insertions(+), 16 deletions(-) diff --git a/x_ysw/mel_cnn.py b/x_ysw/mel_cnn.py index f1cf6a5..af7faac 100644 --- a/x_ysw/mel_cnn.py +++ b/x_ysw/mel_cnn.py @@ -20,6 +20,8 @@ train_data = np.genfromtxt("C:\data\sound\mel_train2.csv", delimiter=',') train_data.shape #Out[16]: (50, 16384) + + #label set labels = train_info['label'] df_label = pd.DataFrame(labels) @@ -43,12 +45,16 @@ validateData = validate_set.values[:,0:16384] validataLabel = validate_set.values[:,-1] +#normalizaion +#train_data = librosa.util.normalize(trainData, axis = 1, norm = 0) #axix = ?, norm = ?>0>? + #print (trainData.shape,trainLabel.shape,validateData.shape,validataLabel.shape) +tf.reset_default_graph() # 텐서플로우 모델 생성 n_dim = 16384 n_classes = 41 -training_epochs = 10 +training_epochs = 700 learning_rate = 0.001 batch_size = 100 steps_for_validate = 5 @@ -64,29 +70,33 @@ # img shape = (?, 128, 128, 1) -c1 = tf.layers.conv2d(tf.reshape(X, [-1, 128, 128, 1]), 32, (1, 1), padding='same', - activation=tf.nn.elu, name="c1") -p1 = tf.layers.max_pooling2d(inputs=c1, pool_size=[2, 2], strides=2) +c1 = tf.layers.conv2d(tf.reshape(X, [-1, 128, 128, 1]), 32, kernel_size=[5, 3], strides=(1, 1), + padding='same', activation=tf.nn.relu, name="c1") +n1 = tf.layers.batch_normalization(c1) +p1 = tf.layers.max_pooling2d(inputs=n1, pool_size=[3, 3], strides=2) p1 = tf.nn.dropout(p1, p_keep_conv) # img shape = (?, 64, 64, 32) -c2 = tf.layers.conv2d(tf.reshape(p1, [-1, 64, 64, 32]), 64, (1, 1), padding='same', - activation=tf.nn.elu, name="c2") -p2 = tf.layers.max_pooling2d(inputs=c2, pool_size=[2, 2], strides=2) #shape = [?, 1, 48, 100] +c2 = tf.layers.conv2d(tf.reshape(p1, [-1, 63, 63, 32]), 64, kernel_size=[5, 3], strides=(1, 1), + padding='same', activation=tf.nn.relu, name="c2") +n2 = tf.layers.batch_normalization(c2) +p2 = tf.layers.max_pooling2d(inputs=n2, pool_size=[3, 3], strides=2) #shape = [?, 1, 48, 100] p2 = tf.nn.dropout(p2, p_keep_conv) # img shape = (?, 32, 32, 64) -c3 = tf.layers.conv2d(tf.reshape(p2, [-1, 32, 32, 64]), 128, (1, 1), padding='same', - activation=tf.nn.elu, name="c3") -p3 = tf.layers.max_pooling2d(inputs=c3, pool_size=[2, 2], strides=2) #shape = [?, 1, 24, 200] +c3 = tf.layers.conv2d(tf.reshape(p2, [-1, 31, 31, 64]), 128, kernel_size=[5, 3], strides=(1, 1), + padding='same', activation=tf.nn.relu, name="c3") +n3 = tf.layers.batch_normalization(c3) +p3 = tf.layers.max_pooling2d(inputs=n3, pool_size=[3, 3], strides=2) #shape = [?, 1, 24, 200] p3 = tf.nn.dropout(p3, p_keep_conv) -L4_flat = tf.reshape(p3, shape=[-1, 16*16*128]) -W1 = tf.get_variable("W1", shape=[16*16*128, 624], initializer=tf.contrib.layers.xavier_initializer()) +L4_flat = tf.reshape(p3, shape=[-1, 15*15*128]) +W1 = tf.get_variable("W1", shape=[15*15*128, 640], initializer=tf.contrib.layers.xavier_initializer()) L5 = tf.nn.relu(tf.matmul(L4_flat, W1)) -L5 = tf.nn.dropout(L5, p_keep_hidden) +n5 = tf.layers.batch_normalization(L5) +L5 = tf.nn.dropout(n5, p_keep_hidden) -W2 = tf.get_variable("W2", shape=[624,41],initializer=tf.contrib.layers.xavier_initializer()) +W2 = tf.get_variable("W2", shape=[640,41],initializer=tf.contrib.layers.xavier_initializer()) b = tf.Variable(tf.random_normal([41])) logits = tf.matmul(L5, W2) + b @@ -117,8 +127,29 @@ if epoch % steps_for_validate == steps_for_validate-1: correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + x=np.random.choice(validataLabel.shape[0], 500, replace=False) print('Accuracy:', sess.run(accuracy, feed_dict={ - X: validateData, Y: validataLabel.reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + X: validateData[x], Y: validataLabel[x].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + #save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') print('Finished!') -tf.reset_default_graph() \ No newline at end of file +""" +#1. +training_epochs = 700 +learning_rate = 0.0004 +activation = elu +conv = kernel_size=[3, 3], strides=(1, 1) + kernel_size=[3, 3], strides=(1, 1) + kernel_size=[3, 3], strides=(1, 1) +pool = pool_size=[2, 2], strides=2 + pool_size=[2, 2], strides=2 + pool_size=[2, 2], strides=2 +flat = 16*16*128 -> 64 +p_keep_conv: .7, p_keep_hidden: .5 + +cost : 0.06~0.08에서 계속 떨어지는중 +Accuracy: 59~63% +전처리시 정규화 고려.. librosa.util.normalize +윈도우 14*2 고려.. +러닝레이트 조절 +""" From 2c03fa8553b8dfea5518def9a475c6b8bf8e84f6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EA=B3=BD=EC=83=81=EC=9A=B1?= <37833373+Trigger21@users.noreply.github.com> Date: Wed, 2 May 2018 15:25:49 +0900 Subject: [PATCH 17/36] Create sound_cnn_v_kaggle5.py --- x_ksw/New Project/sound_cnn_v_kaggle5.py | 328 +++++++++++++++++++++++ 1 file changed, 328 insertions(+) create mode 100644 x_ksw/New Project/sound_cnn_v_kaggle5.py diff --git a/x_ksw/New Project/sound_cnn_v_kaggle5.py b/x_ksw/New Project/sound_cnn_v_kaggle5.py new file mode 100644 index 0000000..2e8db40 --- /dev/null +++ b/x_ksw/New Project/sound_cnn_v_kaggle5.py @@ -0,0 +1,328 @@ +import numpy as np +np.random.seed(1001) +tf.set_random_seed(1001) +import tensorflow as tf +import os +import shutil +import IPython +import matplotlib +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns +import glob +import librosa +from sklearn.cross_validation import StratifiedKFold + +%matplotlib inline +matplotlib.style.use('ggplot') + +# Preparing data + +# *2.03 : 350 + +class Config(object): + def __init__(self, + sampling_rate=16000, audio_duration=2, n_classes=41, + use_mfcc=False, n_folds=10, learning_rate=0.0001, + max_epochs=50, n_mfcc=20): + self.sampling_rate = sampling_rate + self.audio_duration = audio_duration + self.n_classes = n_classes + self.use_mfcc = use_mfcc + self.n_mfcc = n_mfcc + self.n_folds = n_folds + self.learning_rate = learning_rate + self.max_epochs = max_epochs + + self.audio_length = self.sampling_rate * self.audio_duration*2.03 + if self.use_mfcc: + self.dim = (self.n_mfcc, 1 + int(np.floor(self.audio_length/512)), 1) + else: + self.dim = (self.audio_length, 1) + +config = Config(sampling_rate=44100, audio_duration=2, n_folds=10, + learning_rate=0.001, use_mfcc=True, n_mfcc=40) + +def prepare_data(df, config, data_dir): + X = np.empty(shape=(df.shape[0], config.dim[0], config.dim[1], 1)) + input_length = int(config.audio_length) + for i, fname in enumerate(df['fname']): + print(fname) + file_path = data_dir + fname + data, _ = librosa.core.load(file_path, sr=config.sampling_rate, res_type="kaiser_fast") + + # Random offset / Padding + if len(data) > input_length: + max_offset = len(data) - input_length + offset = np.random.randint(max_offset) + data = data[offset:(input_length+offset)] + else: + if input_length > len(data): + max_offset = input_length - len(data) + offset = np.random.randint(max_offset) + else: + offset = 0 + data = np.pad(data, (offset, input_length - len(data) - offset), "constant") + + data = librosa.feature.mfcc(data, sr=config.sampling_rate, n_mfcc=config.n_mfcc) + data = np.expand_dims(data, axis=-1) + X[i,] = data + return X + +#file = "/home/itwill03/sound/audio_train/*.wav" +#file = "c:/sound/audio_train/*.wav" +#train_list=glob.glob(file) + +#file = "/home/itwill03/sound/audio_test/*.wav" +#test_list=glob.glob(file) + +#train = pd.read_csv("c:/sound/train.csv") +train = pd.read_csv("/home/itwill03/sound/train.csv") + +labels = train['label'] +l = train['label'].unique() + +df_label = pd.DataFrame(labels) + +for i in range(len(l)): + df_label[df_label==l[i]] = int(i) +df_label.values + +df = pd.concat([train, df_label], axis=1) + +from sklearn.model_selection import train_test_split +train_set, test_set = train_test_split(df, test_size = 0.3) +trainfile = train_set['fname'] +testfile = test_set['fname'] +trainLabel = train_set.values[:,-1] +testLabel = test_set.values[:,-1] + +train.shape[0] +config.dim[0] +config.dim[1] +#X = np.empty(shape=(9473, 40, 350, 1)) # 엔트리를 초기화 하지 않고 값을 반환 + +train.index +train.index[1] + +config.audio_length + + +#X_train = prepare_data(train_set, config, 'c:/sound/audio_train/') +#X_test = prepare_data(test_set, config, 'c:/sound/audio_train/') + +X_train = prepare_data(train_set, config, '/home/itwill03/sound/audio_train/') +X_test = prepare_data(test_set, config, '/home/itwill03/sound/audio_train/') + +#Normalization + +mean = np.mean(X_train, axis=0) +std = np.std(X_train, axis=0) + +X_train = (X_train - mean)/std +X_test = (X_test - mean)/std + +X_train.shape # (6631, 40, 350, 1) +X_test.shape # (2842, 40, 350, 1) + +np.savetxt("c:/home/itwill03/sound/X_train.csv",X_train, delimiter=",") +np.savetxt("c:/home/itwill03/sound/X_test.csv",X_train, delimiter=",") +#np.savetxt("c:/data/sound/train_data.csv",train_data, delimiter=",") + +trainData = X_train.reshape(-1, 40, 173) +testData = X_test.reshape(-1, 40, 173) + +trainData.shape # (6631, 40, 350) +testData.shape # (2842, 40, 350) +trainLabel.shape # (6631,) +testLabel.shape # (2842,) + +# start +tf.reset_default_graph() #그래프 초기화 + +# hyper parameters +learning_rate = 0.00008 +training_epochs = 700 +batch_size = 100 +steps_for_validate = 5 + +#placeholder +X = tf.placeholder(tf.float32, [None, 40, 350,1], name="X") +#X_sound = tf.reshape(X, [-1, 40, 173, 1]) # 40*350*1 (frequency, time, amplitude) +Y = tf.placeholder(tf.int32, [None, 1], name="Y") +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv") +p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") + +# L1 SoundIn shape=(?, 40, 350, 1) +W1 = tf.get_variable("W1", shape=[4, 35, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +L1 = tf.nn.conv2d(X, W1, strides=[1, 1, 1, 1], padding='SAME') +L1 = tf.layers.batch_normalization(L1) +#L1 = tf.nn.relu(L1) +#L1 = tf.nn.elu(L1) +L1 = tf.nn.leaky_relu(L1,0.1) +L1 = tf.nn.max_pool(L1, ksize=[1, 2, 18, 1],strides=[1, 2, 18, 1], padding='SAME') +#L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L1 = tf.nn.dropout(L1, p_keep_conv) + +# L2 Input shape=(?,10,10,32) +W2 = tf.get_variable("W2", shape=[3, 3, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) +L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') +L2 = tf.layers.batch_normalization(L2) +#L2 = tf.nn.relu(L2) +#L2 = tf.nn.elu(L2) +L2 = tf.nn.leaky_relu(L2,0.1) +L2 = tf.nn.max_pool(L2, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') +#L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L2 = tf.nn.dropout(L2, p_keep_conv) + +# L3 Input shape=(?,4,4,64) +W3 = tf.get_variable("W3", shape=[3, 3, 64, 128],initializer=tf.contrib.layers.xavier_initializer()) +L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') +L3 = tf.layers.batch_normalization(L3) +#L3 = tf.nn.relu(L3) +#L3 = tf.nn.elu(L3) +L3 = tf.nn.leaky_relu(L3,0.1) +L3 = tf.nn.max_pool(L3, ksize=[1, 3, 3, 1],strides=[1, 2, 2, 1], padding='SAME') +#L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L3 = tf.nn.dropout(L3, p_keep_conv) +L3_flat= tf.reshape(L3, shape=[-1, 4*4*128]) + +# Final FC 2*2*128 inputs -> 41 outputs +W4 = tf.get_variable("W4", shape=[4*4*128, 625],initializer=tf.contrib.layers.xavier_initializer()) +#L4 = tf.nn.relu(tf.matmul(L3_flat, W4)) +#L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) +L4 = tf.nn.leaky_relu(tf.matmul(L3_flat, W4),0.1) +L4 = tf.nn.dropout(L4, p_keep_hidden) +W_o = tf.get_variable("W_o", shape=[625,41],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L4, W_o) + b + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +#saver = tf.train.Saver() + +# train my model +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = int(len(X_train) / batch_size) + for i in range(total_batch): + batch_xs = X_train[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .8, p_keep_hidden: 0.7} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + if epoch % steps_for_validate == steps_for_validate-1: + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + x=np.random.choice(testLabel.shape[0], 500, replace=False) + print('Accuracy:', sess.run(accuracy, feed_dict={ + X: X_test[x], Y: testLabel[x].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + #save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') +print('Finished!') + + +trainData.shape +batch_xs.shape +batch_ys.shape + +sess.close() + + +""" +1) con2d layer * 3 + FC +lr=0.001, epoch = 100 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (4, 10, 32), (4, 10, 32), (4, 10, 32) +FC : (?, 64), (64, 41) +max_pool : (2,2), (2,2), (3,3) +accuracy: 51~60% +2) con2d layer * 3 + FC +lr=0.001, epoch = 100 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (4, 10, 32), (4, 10, 32), (4, 10, 32) +FC : (2*8*32, 64), (64, 41) +max_pool : (2,2), (2,2), (2,2) +accuracy: 55~60% +3) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.063까지 나옴) 정확도는 그대로임 +lr=0.0002, epoch = 300 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (4, 10, 32), (4, 10, 32), (4, 10, 32) +FC : (2*8*32, 64), (64, 41) +max_pool : (2,2), (2,2), (2,2) +accuracy: 54~55% +4) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.012까지 나옴) 정확도는 그대로임 +lr=0.0002, epoch = 300 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (2, 8, 32), (3, 3, 64), (3, 3, 128) +FC : (3*3*128, 512), (512, 41) +max_pool : (2,8), (3,3), (3,3) +accuracy: 61~63% +5) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.0016까지 나옴) 정확도는 그대로임 +lr=0.0002, epoch = 300 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (3, 13, 32), (3, 3, 64), (3, 3, 128) +FC : (5*22*128, 625), (625, 41) +max_pool : (2,2), (2,2), (2,2) +accuracy: 53~56% +6) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.16까지 나옴) 정확도는 그대로임 +lr=0.00008, epoch = 300 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (2, 10, 32), (3, 3, 64), (3, 3, 128) +FC : (3*2*128, 625), (625, 41) +max_pool : (2,10), (3,3), (3,3) +accuracy: 65~67% +7) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.13까지 나옴) 정확도는 그대로임 +lr=0.00005, epoch = 500 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (2, 10, 32), (3, 3, 64), (3, 3, 128) +FC : (3*2*128, 625), (625, 41) +max_pool : (2,10), (3,3), (3,3) +accuracy: 65~66% +8) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.008까지 나옴) 정확도는 그대로임 +lr=0.00005, epoch = 400 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (3, 3, 32), (3, 3, 64), (3, 3, 128) +FC : (5*5*128, 1000), (1000, 41) +max_pool : (2,10), (2,2), (2,2) +accuracy: 60~61% +9) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.22까지 나옴) 정확도는 그대로임 +lr=0.00005, epoch = 500 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (2, 10, 32), (3, 3, 64), (3, 3, 128) +FC : (5*1*128, 650), (650, 41) +max_pool : (2,10), (2,10), (3,3) +accuracy: 60~61% +10) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.22까지 나옴) 정확도는 그대로임 +lr=0.00005, epoch = 500 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (2, 10, 32), (3, 3, 64), (3, 3, 128) +FC : (10*10*128, 650), (650, 41) +max_pool : (2,9), (2,2), (1,1) +accuracy: 59~60% +11) + cost = 0.024251443 + Accuracy: 0.64% ~ 0.65% +12) con2d layer * 3 + FC # 코스트는 계속해서 내려가나 (0.024까지 나옴) 정확도는 그대로임 +lr=0.0002, epoch = 700 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (4, 10, 32), (3, 3, 64), (3, 3, 128) +FC : (5*6*128, 650), (650, 41) +max_pool : (4,10), (2,2), (2,2) +accuracy: 61~62% +13) con2d layer * 3 + FC # V_40,350 코스트는 계속해서 내려가나 (0.14까지 나옴) 정확도는 그대로임 +lr=0.0002, epoch = 700 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (4, 35, 32), (3, 3, 64), (3, 3, 128) +FC : (2*2*128, 410), (410, 41) +max_pool : (4,35), (3,3), (2,2) +accuracy: 64~72% +""" From c7a9e483d8b82c0f17588aa4e2a86be49630d38d Mon Sep 17 00:00:00 2001 From: smallerhand Date: Wed, 2 May 2018 16:54:47 +0900 Subject: [PATCH 18/36] all --- README.md | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 92e7372..e15b971 100644 --- a/README.md +++ b/README.md @@ -52,9 +52,21 @@ mfcc, stft, melspectogram 등 6. 캐글에 올리고, 추천 권유 승혁: sound processing + processing.py -상욱: data set 설명(graph, column, label) mfcc, stft 그래프 등 +상욱: data set 설명(graph, column, label) mfcc, stft 그래프(이미지) 등 대곤: 앙상블로 나온 정확도/ 캐글에 올리기 수원: 각각으로 나온 정확도 + 추천 권유 + +__________ +상욱: 40*350 으로 바꿔서 69~72%, 최대 73.4% 마지막 출력을 크게 해서 더 올라감. +40*400 만들어놓음. 40*500 도 해보려고 함. + +대곤: 68~69%. 앙상블 코드. 앙상블 개념도. + +수원: conv1d 54%. MEL 64%. + +코드 각자 주석달아서 취합. +앙상블은 일단 승혁+MEL+chroma 이렇게 테스트 모델. +오늘 집에 가기 전에 PROJECT2 에 넣어놓기. From afe2585bae907dc700278572b6e6cc708ee4c833 Mon Sep 17 00:00:00 2001 From: smallerhand Date: Wed, 2 May 2018 17:39:12 +0900 Subject: [PATCH 19/36] all --- x_ksh/mfcc_cnn_ps.py | 109 +++++++++++++++++++++++++++++++++++++++ x_ksh/mfcc_processing.py | 4 +- x_ksh/stft_cnn_ps.py | 109 +++++++++++++++++++++++++++++++++++++++ x_ksh/stft_processing.py | 35 +++++++++++-- 4 files changed, 252 insertions(+), 5 deletions(-) create mode 100644 x_ksh/mfcc_cnn_ps.py create mode 100644 x_ksh/stft_cnn_ps.py diff --git a/x_ksh/mfcc_cnn_ps.py b/x_ksh/mfcc_cnn_ps.py new file mode 100644 index 0000000..7c7c4c5 --- /dev/null +++ b/x_ksh/mfcc_cnn_ps.py @@ -0,0 +1,109 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Apr 26 18:53:10 2018 + +@author: kimseunghyuck +""" + +#import important modules +import numpy as np +import tensorflow as tf +from math import ceil +tf.set_random_seed(777) + +trainData = np.genfromtxt('/home/paperspace/Downloads/trainData6.csv', delimiter=',') +trainData = trainData.reshape(-1, 20, 430) +testData = np.genfromtxt('/home/paperspace/Downloads/testData6.csv', delimiter=',') +testData = testData.reshape(-1, 20, 430) +trainLabel = np.genfromtxt('/home/paperspace/Downloads/trainLabel6.csv', delimiter=',') +testLabel = np.genfromtxt('/home/paperspace/Downloads/testLabel6.csv', delimiter=',') + +print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape) +# (6631, 20, 200) (2842, 20, 200) (6631,) (2842,) + +#다시 돌릴때는 여기부터 +tf.reset_default_graph() #그래프 초기화 + +# hyper parameters +learning_rate = 0.0002 +training_epochs = 1100 +batch_size = 200 +steps_for_validate = 5 + +#placeholder +X = tf.placeholder(tf.float32, [None, 20, 430], name="X") +X_sound = tf.reshape(X, [-1, 20, 430, 1]) +Y = tf.placeholder(tf.int32, [None, 1], name="Y") +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv") +p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") + +# L1 SoundIn shape=(?, 20, 430, 1) +W1 = tf.get_variable("W1", shape=[2, 43, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME') +L1 = tf.nn.elu(L1) +L1 = tf.layers.batch_normalization(L1) +L1 = tf.nn.max_pool(L1, ksize=[1, 2, 43, 1],strides=[1, 2, 43, 1], padding='SAME') +L1 = tf.nn.dropout(L1, p_keep_conv) +""" +# L2 Input shape=(?,10,21,32) +W2 = tf.get_variable("W2", shape=[3, 3, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) +L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') +L2 = tf.nn.elu(L2) +L2 = tf.layers.batch_normalization(L2) +L2 = tf.nn.max_pool(L2, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') +L2 = tf.nn.dropout(L2, p_keep_conv) +""" +# L3 Input shape=(?,3,12,64) +W3 = tf.get_variable("W3", shape=[3, 3, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) +L3 = tf.nn.conv2d(L1, W3, strides=[1, 1, 1, 1], padding='SAME') +L3 = tf.nn.elu(L3) +L3 = tf.layers.batch_normalization(L3) +L3 = tf.nn.max_pool(L3, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') +L3 = tf.nn.dropout(L3, p_keep_conv) +L3_flat= tf.reshape(L3, shape=[-1, 4*4*64]) + +# Final FC 2*3*128 inputs -> 41 outputs +W4 = tf.get_variable("W4", shape=[4*4*64, 512],initializer=tf.contrib.layers.xavier_initializer()) +L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) +L4 = tf.layers.batch_normalization(L4) +L4 = tf.nn.dropout(L4, p_keep_hidden) +W_o = tf.get_variable("W_o", shape=[512,41],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L4, W_o) + b +logits = tf.layers.batch_normalization(logits) + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +saver = tf.train.Saver() + +# train my model +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = ceil(len(trainData) / batch_size) + for i in range(total_batch): + batch_xs = trainData[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .8, p_keep_hidden: 0.7} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + if epoch % steps_for_validate == steps_for_validate-1: + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + x1=np.random.choice(testLabel.shape[0], 300, replace=False) + print('TrainAccuracy:', sess.run(accuracy, feed_dict={ + X: trainData[x1], Y: trainLabel[x1].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + x2=np.random.choice(testLabel.shape[0], 300, replace=False) + print('TestAccuracy:', sess.run(accuracy, feed_dict={ + X: testData[x2], Y: testLabel[x2].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') +print('Finished!') diff --git a/x_ksh/mfcc_processing.py b/x_ksh/mfcc_processing.py index 5ee5fa5..f86fb52 100644 --- a/x_ksh/mfcc_processing.py +++ b/x_ksh/mfcc_processing.py @@ -110,8 +110,8 @@ def Labeling(label): #0 40 0 40 #csv downdload totally about 600MB -trainData2D=trainData.reshape(-1, 20*430) -testData2D=testData.reshape(-1, 20*430) +trainData2D=trainData.reshape(-1, 20*200) +testData2D=testData.reshape(-1, 20*200) np.savetxt(path+'trainData6.csv', trainData2D, delimiter=",") np.savetxt(path+'testData6.csv', diff --git a/x_ksh/stft_cnn_ps.py b/x_ksh/stft_cnn_ps.py new file mode 100644 index 0000000..2238f00 --- /dev/null +++ b/x_ksh/stft_cnn_ps.py @@ -0,0 +1,109 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Apr 26 18:53:10 2018 + +@author: kimseunghyuck +""" + +#import important modules +import numpy as np +import tensorflow as tf +from math import ceil +tf.set_random_seed(777) + +trainData = np.genfromtxt('/home/paperspace/Downloads/trainData7.csv', delimiter=',') +trainData = trainData.reshape(-1, 17, 200) +testData = np.genfromtxt('/home/paperspace/Downloads/testData7.csv', delimiter=',') +testData = testData.reshape(-1, 17, 200) +trainLabel = np.genfromtxt('/home/paperspace/Downloads/trainLabel7.csv', delimiter=',') +testLabel = np.genfromtxt('/home/paperspace/Downloads/testLabel7.csv', delimiter=',') + +print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape) +# (6631, 17, 200) (2842, 17, 200) (6631,) (2842,) + +#다시 돌릴때는 여기부터 +tf.reset_default_graph() #그래프 초기화 + +# hyper parameters +learning_rate = 0.0002 +training_epochs = 700 +batch_size = 100 +steps_for_validate = 5 + +#placeholder +X = tf.placeholder(tf.float32, [None, 17, 200], name="X") +X_sound = tf.reshape(X, [-1, 17, 200, 1]) +Y = tf.placeholder(tf.int32, [None, 1], name="Y") +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv") +p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") + +# L1 SoundIn shape=(?, 20, 430, 1) +W1 = tf.get_variable("W1", shape=[2, 24, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME') +L1 = tf.nn.elu(L1) +L1 = tf.layers.batch_normalization(L1) +L1 = tf.nn.max_pool(L1, ksize=[1, 2, 24, 1],strides=[1, 2, 24, 1], padding='SAME') +L1 = tf.nn.dropout(L1, p_keep_conv) + +# L2 Input shape=(?,10,21,32) +W2 = tf.get_variable("W2", shape=[3, 3, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) +L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') +L2 = tf.nn.elu(L2) +L2 = tf.layers.batch_normalization(L2) +L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') +L2 = tf.nn.dropout(L2, p_keep_conv) + +# L3 Input shape=(?,3,12,64) +W3 = tf.get_variable("W3", shape=[3, 3, 64, 128],initializer=tf.contrib.layers.xavier_initializer()) +L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') +L3 = tf.nn.elu(L3) +L3 = tf.layers.batch_normalization(L3) +L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') +L3 = tf.nn.dropout(L3, p_keep_conv) +L3_flat= tf.reshape(L3, shape=[-1, 3*3*128]) + +# Final FC 2*3*128 inputs -> 41 outputs +W4 = tf.get_variable("W4", shape=[3*3*128, 512],initializer=tf.contrib.layers.xavier_initializer()) +L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) +L4 = tf.layers.batch_normalization(L4) +L4 = tf.nn.dropout(L4, p_keep_hidden) +W_o = tf.get_variable("W_o", shape=[512,41],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L4, W_o) + b +logits = tf.layers.batch_normalization(logits) + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +saver = tf.train.Saver() + +# train my model +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = ceil(len(trainData) / batch_size) + for i in range(total_batch): + batch_xs = trainData[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .8, p_keep_hidden: 0.7} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + if epoch % steps_for_validate == steps_for_validate-1: + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + x1=np.random.choice(testLabel.shape[0], 300, replace=False) + print('TrainAccuracy:', sess.run(accuracy, feed_dict={ + X: trainData[x1], Y: trainLabel[x1].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + x2=np.random.choice(testLabel.shape[0], 300, replace=False) + print('TestAccuracy:', sess.run(accuracy, feed_dict={ + X: testData[x2], Y: testLabel[x2].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + save_path = saver.save(sess, '/home/paperspace/Downloads/optx2/optx2') +print('Finished!') diff --git a/x_ksh/stft_processing.py b/x_ksh/stft_processing.py index 3103d29..91fa879 100644 --- a/x_ksh/stft_processing.py +++ b/x_ksh/stft_processing.py @@ -63,6 +63,8 @@ def see_how_long(file): plt.show #magnitude만 해도 될 것 같음. +""" +# it takes too much time #5 seconds(100 segments) extract def five_sec_extract(file): #zero padding to file.shape[0] X 513 X 100 @@ -89,12 +91,39 @@ def five_sec_extract(file): array[k, :, :]=mag[:, start:start+200] k+=1 return(array) +""" + +def five_sec_extract(file): + #zero padding to file.shape[0] X 17 X 200 + n=file.shape[0] + array = np.repeat(0., n * 17 * 200).reshape(n, 17, 200) + k=0 + for filename in file: + y, sr = librosa.core.load(path+'audio_train/'+filename, + mono=True, res_type="kaiser_fast") + stft=librosa.core.stft(y,32,16) + mag, pha = librosa.magphase(stft) + length=stft.shape[1] + abs_mag=np.abs(mag) + if length == 200: + array[k, :, :]=mag + elif length < 200: + array[k, :, :length]=mag + elif length > 200: + argmax=np.argmax(abs_mag, axis=1) + sample=[] + for i in range(np.max(argmax)): + sample.append(np.sum((argmax>=i) & (argmax Date: Wed, 2 May 2018 17:49:40 +0900 Subject: [PATCH 20/36] Add files via upload --- x_ysw/mel_cnn.py | 46 ++++++++++++++++++++++++++++++++-------------- 1 file changed, 32 insertions(+), 14 deletions(-) diff --git a/x_ysw/mel_cnn.py b/x_ysw/mel_cnn.py index af7faac..a1efed4 100644 --- a/x_ysw/mel_cnn.py +++ b/x_ysw/mel_cnn.py @@ -14,10 +14,10 @@ tf.set_random_seed(777) -#train_info = pd.read_csv("/home/itwill03/sound/train.csv",delimiter=',') -#train_data = np.genfromtxt("/home/itwill03/sound/yy/feature_train.csv", delimiter=',') -train_info = pd.read_csv("C:\data\sound/train.csv",delimiter=',') -train_data = np.genfromtxt("C:\data\sound\mel_train2.csv", delimiter=',') +train_info = pd.read_csv("/home/itwill03/sound/train.csv",delimiter=',') +train_data = np.genfromtxt("/home/itwill03/sound/mel_train2.csv", delimiter=',') +#train_info = pd.read_csv("C:\data\sound/train.csv",delimiter=',') +#train_data = np.genfromtxt("C:\data\sound\mel_train2.csv", delimiter=',') train_data.shape #Out[16]: (50, 16384) @@ -55,7 +55,7 @@ n_dim = 16384 n_classes = 41 training_epochs = 700 -learning_rate = 0.001 +learning_rate = 0.0004 batch_size = 100 steps_for_validate = 5 keep_prob = tf.placeholder(tf.float32) @@ -73,30 +73,30 @@ c1 = tf.layers.conv2d(tf.reshape(X, [-1, 128, 128, 1]), 32, kernel_size=[5, 3], strides=(1, 1), padding='same', activation=tf.nn.relu, name="c1") n1 = tf.layers.batch_normalization(c1) -p1 = tf.layers.max_pooling2d(inputs=n1, pool_size=[3, 3], strides=2) +p1 = tf.layers.max_pooling2d(inputs=n1, pool_size=[12, 4], strides=4) p1 = tf.nn.dropout(p1, p_keep_conv) # img shape = (?, 64, 64, 32) -c2 = tf.layers.conv2d(tf.reshape(p1, [-1, 63, 63, 32]), 64, kernel_size=[5, 3], strides=(1, 1), +c2 = tf.layers.conv2d(tf.reshape(p1, [-1, 30, 32, 32]), 64, kernel_size=[5, 3], strides=(1, 1), padding='same', activation=tf.nn.relu, name="c2") n2 = tf.layers.batch_normalization(c2) -p2 = tf.layers.max_pooling2d(inputs=n2, pool_size=[3, 3], strides=2) #shape = [?, 1, 48, 100] +p2 = tf.layers.max_pooling2d(inputs=n2, pool_size=[12, 4], strides=2) #shape = [?, 1, 48, 100] p2 = tf.nn.dropout(p2, p_keep_conv) # img shape = (?, 32, 32, 64) -c3 = tf.layers.conv2d(tf.reshape(p2, [-1, 31, 31, 64]), 128, kernel_size=[5, 3], strides=(1, 1), +c3 = tf.layers.conv2d(tf.reshape(p2, [-1, 10, 15, 64]), 128, kernel_size=[5, 3], strides=(1, 1), padding='same', activation=tf.nn.relu, name="c3") n3 = tf.layers.batch_normalization(c3) -p3 = tf.layers.max_pooling2d(inputs=n3, pool_size=[3, 3], strides=2) #shape = [?, 1, 24, 200] +p3 = tf.layers.max_pooling2d(inputs=n3, pool_size=[2, 2], strides=2) #shape = [?, 1, 24, 200] p3 = tf.nn.dropout(p3, p_keep_conv) -L4_flat = tf.reshape(p3, shape=[-1, 15*15*128]) -W1 = tf.get_variable("W1", shape=[15*15*128, 640], initializer=tf.contrib.layers.xavier_initializer()) +L4_flat = tf.reshape(p3, shape=[-1, 5*7*128]) +W1 = tf.get_variable("W1", shape=[5*7*128, 420], initializer=tf.contrib.layers.xavier_initializer()) L5 = tf.nn.relu(tf.matmul(L4_flat, W1)) n5 = tf.layers.batch_normalization(L5) L5 = tf.nn.dropout(n5, p_keep_hidden) -W2 = tf.get_variable("W2", shape=[640,41],initializer=tf.contrib.layers.xavier_initializer()) +W2 = tf.get_variable("W2", shape=[420,41],initializer=tf.contrib.layers.xavier_initializer()) b = tf.Variable(tf.random_normal([41])) logits = tf.matmul(L5, W2) + b @@ -120,7 +120,7 @@ for i in range(total_batch): batch_xs = trainData[i*batch_size:(i+1)*batch_size] batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) - feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .7, p_keep_hidden: .5} + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .8, p_keep_hidden: .7} c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) avg_cost += c / total_batch print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) @@ -149,7 +149,25 @@ cost : 0.06~0.08에서 계속 떨어지는중 Accuracy: 59~63% + 전처리시 정규화 고려.. librosa.util.normalize 윈도우 14*2 고려.. 러닝레이트 조절 + +#2. +training_epochs = 700 +learning_rate = 0.0001 +activation = relu +conv = kernel_size=[5, 3], strides=(1, 1) + kernel_size=[5, 3], strides=(1, 1) + kernel_size=[5, 3], strides=(1, 1) +pool = pool_size=[4, 4], strides=4 + pool_size=[3, 3], strides=2 + pool_size=[3, 3], strides=2 +flat = 7*7*128, 640 +p_keep_conv: .7, p_keep_hidden: .5 + +cost = 0.122217526 +Accuracy: 60~67% + """ From 77211129fb39f4fcf756973c079ab8d2bb77e9b5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EA=B3=BD=EC=83=81=EC=9A=B1?= <37833373+Trigger21@users.noreply.github.com> Date: Wed, 2 May 2018 17:51:58 +0900 Subject: [PATCH 21/36] Create final_Test.py --- x_ksw/New Project/final_Test.py | 205 ++++++++++++++++++++++++++++++++ 1 file changed, 205 insertions(+) create mode 100644 x_ksw/New Project/final_Test.py diff --git a/x_ksw/New Project/final_Test.py b/x_ksw/New Project/final_Test.py new file mode 100644 index 0000000..88ac15b --- /dev/null +++ b/x_ksw/New Project/final_Test.py @@ -0,0 +1,205 @@ +import numpy as np +import tensorflow as tf +np.random.seed(1001) +tf.set_random_seed(1001) +import os +import shutil +import IPython +import matplotlib +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns +import glob +import librosa +from sklearn.cross_validation import StratifiedKFold + +%matplotlib inline +matplotlib.style.use('ggplot') + +# Preparing data + +# *2.03 : 350 + +class Config(object): + def __init__(self, + sampling_rate=16000, audio_duration=2, n_classes=41, + use_mfcc=False, n_folds=10, learning_rate=0.0001, + max_epochs=50, n_mfcc=20): + self.sampling_rate = sampling_rate + self.audio_duration = audio_duration + self.n_classes = n_classes + self.use_mfcc = use_mfcc + self.n_mfcc = n_mfcc + self.n_folds = n_folds + self.learning_rate = learning_rate + self.max_epochs = max_epochs + + self.audio_length = self.sampling_rate * self.audio_duration*2.61 + if self.use_mfcc: + self.dim = (self.n_mfcc, 1 + int(np.floor(self.audio_length/512)), 1) + else: + self.dim = (self.audio_length, 1) + +config = Config(sampling_rate=44100, audio_duration=2, n_folds=10, + learning_rate=0.001, use_mfcc=True, n_mfcc=40) + +def prepare_data(df, config, data_dir): + X = np.empty(shape=(df.shape[0], config.dim[0], config.dim[1], 1)) + input_length = int(config.audio_length) + for i, fname in enumerate(df['fname']): + print(fname) + file_path = data_dir + fname + data, _ = librosa.core.load(file_path, sr=config.sampling_rate, res_type="kaiser_fast") + + # Random offset / Padding + if len(data) > input_length: + max_offset = len(data) - input_length + offset = np.random.randint(max_offset) + data = data[offset:(input_length+offset)] + else: + if input_length > len(data): + max_offset = input_length - len(data) + offset = np.random.randint(max_offset) + else: + offset = 0 + data = np.pad(data, (offset, input_length - len(data) - offset), "constant") + + data = librosa.feature.mfcc(data, sr=config.sampling_rate, n_mfcc=config.n_mfcc) + data = np.expand_dims(data, axis=-1) + X[i,] = data + return X + + +train = pd.read_csv("c:/sound/train.csv") +#train = pd.read_csv("/home/itwill03/sound/train.csv") + +labels = train['label'] +l = train['label'].unique() + +df_label = pd.DataFrame(labels) + +for i in range(len(l)): + df_label[df_label==l[i]] = int(i) +df_label.values + +df = pd.concat([train, df_label], axis=1) + +from sklearn.model_selection import train_test_split +train_set, test_set = train_test_split(df, test_size = 0.3) +trainfile = train_set['fname'] +testfile = test_set['fname'] +trainLabel = train_set.values[:,-1] +testLabel = test_set.values[:,-1] + +X_train = prepare_data(train_set, config, 'c:/sound/audio_train/') +X_test = prepare_data(test_set, config, 'c:/sound/audio_train/') + +#X_train = prepare_data(train_set, config, '/home/itwill03/sound/audio_train/') +#X_test = prepare_data(test_set, config, '/home/itwill03/sound/audio_train/') + +#Normalization + +mean = np.mean(X_train, axis=0) +std = np.std(X_train, axis=0) + +X_train = (X_train - mean)/std +X_test = (X_test - mean)/std + +X_train.shape # (6631, 40, 350, 1) +X_test.shape # (2842, 40, 350, 1) +trainLabel.shape # (6631,) +testLabel.shape # (2842,) + +# start +tf.reset_default_graph() #그래프 초기화 + +# hyper parameters +learning_rate = 0.0002 +training_epochs = 700 +batch_size = 100 +steps_for_validate = 5 + +#placeholder +X = tf.placeholder(tf.float32, [None, 40, 450,1], name="X") # 40*350*1 (frequency, time, amplitude) +Y = tf.placeholder(tf.int32, [None, 1], name="Y") +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv") +p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") + +# L1 SoundIn shape=(?, 40, 450, 1) +W1 = tf.get_variable("W1", shape=[2, 23, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +L1 = tf.nn.conv2d(X, W1, strides=[1, 1, 1, 1], padding='SAME') +L1 = tf.layers.batch_normalization(L1) +#L1 = tf.nn.relu(L1) +#L1 = tf.nn.elu(L1) +L1 = tf.nn.leaky_relu(L1,0.1) +L1 = tf.nn.max_pool(L1, ksize=[1, 2, 23, 1],strides=[1, 2, 23, 1], padding='SAME') +L1 = tf.nn.dropout(L1, p_keep_conv) + +# L2 Input shape=(?,10,10,32) +W2 = tf.get_variable("W2", shape=[2, 2, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) +L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') +L2 = tf.layers.batch_normalization(L2) +#L2 = tf.nn.relu(L2) +#L2 = tf.nn.elu(L2) +L2 = tf.nn.leaky_relu(L2,0.1) +L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') +#L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L2 = tf.nn.dropout(L2, p_keep_conv) + +# L3 Input shape=(?,4,4,64) +W3 = tf.get_variable("W3", shape=[2, 2, 64, 128],initializer=tf.contrib.layers.xavier_initializer()) +L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') +L3 = tf.layers.batch_normalization(L3) +#L3 = tf.nn.relu(L3) +#L3 = tf.nn.elu(L3) +L3 = tf.nn.leaky_relu(L3,0.1) +L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 1, 1, 1], padding='SAME') +#L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L3 = tf.nn.dropout(L3, p_keep_conv) +L3_flat= tf.reshape(L3, shape=[-1, 10*10*128]) + +# Final FC 2*2*128 inputs -> 41 outputs +W4 = tf.get_variable("W4", shape=[10*10*128, 1250],initializer=tf.contrib.layers.xavier_initializer()) +#L4 = tf.nn.relu(tf.matmul(L3_flat, W4)) +#L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) +L4 = tf.nn.leaky_relu(tf.matmul(L3_flat, W4),0.1) +L4 = tf.nn.dropout(L4, p_keep_hidden) +W_o = tf.get_variable("W_o", shape=[1250,41],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L4, W_o) + b + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +#saver = tf.train.Saver() + +# train my model +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = int(len(X_train) / batch_size) + for i in range(total_batch): + batch_xs = X_train[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: 0.7, p_keep_hidden: 0.5} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + if epoch % steps_for_validate == steps_for_validate-1: + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + x=np.random.choice(testLabel.shape[0], 500, replace=False) + print('Accuracy:', sess.run(accuracy, feed_dict={ + X: X_test[x], Y: testLabel[x].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + #save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') +print('Finished!') + + +sess.close() + From bd6c53ca81f81c3f2d318438c7f5b82d2f81ca87 Mon Sep 17 00:00:00 2001 From: smallerhand Date: Wed, 2 May 2018 17:53:29 +0900 Subject: [PATCH 22/36] all --- x_ksh/mfcc_cnn_ps.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/x_ksh/mfcc_cnn_ps.py b/x_ksh/mfcc_cnn_ps.py index 7c7c4c5..80c8b11 100644 --- a/x_ksh/mfcc_cnn_ps.py +++ b/x_ksh/mfcc_cnn_ps.py @@ -27,7 +27,7 @@ # hyper parameters learning_rate = 0.0002 -training_epochs = 1100 +training_epochs = 500 batch_size = 200 steps_for_validate = 5 @@ -44,28 +44,28 @@ L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME') L1 = tf.nn.elu(L1) L1 = tf.layers.batch_normalization(L1) -L1 = tf.nn.max_pool(L1, ksize=[1, 2, 43, 1],strides=[1, 2, 43, 1], padding='SAME') +#L1 = tf.nn.max_pool(L1, ksize=[1, 2, 43, 1],strides=[1, 2, 43, 1], padding='SAME') L1 = tf.nn.dropout(L1, p_keep_conv) -""" + # L2 Input shape=(?,10,21,32) -W2 = tf.get_variable("W2", shape=[3, 3, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) +W2 = tf.get_variable("W2", shape=[2, 43, 32, 32],initializer=tf.contrib.layers.xavier_initializer()) L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') L2 = tf.nn.elu(L2) L2 = tf.layers.batch_normalization(L2) -L2 = tf.nn.max_pool(L2, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') +#L2 = tf.nn.max_pool(L2, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') L2 = tf.nn.dropout(L2, p_keep_conv) -""" + # L3 Input shape=(?,3,12,64) -W3 = tf.get_variable("W3", shape=[3, 3, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) +W3 = tf.get_variable("W3", shape=[2, 43, 32, 32],initializer=tf.contrib.layers.xavier_initializer()) L3 = tf.nn.conv2d(L1, W3, strides=[1, 1, 1, 1], padding='SAME') L3 = tf.nn.elu(L3) L3 = tf.layers.batch_normalization(L3) -L3 = tf.nn.max_pool(L3, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') +#L3 = tf.nn.max_pool(L3, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') L3 = tf.nn.dropout(L3, p_keep_conv) -L3_flat= tf.reshape(L3, shape=[-1, 4*4*64]) +L3_flat= tf.reshape(L3, shape=[-1, 20*43*32]) # Final FC 2*3*128 inputs -> 41 outputs -W4 = tf.get_variable("W4", shape=[4*4*64, 512],initializer=tf.contrib.layers.xavier_initializer()) +W4 = tf.get_variable("W4", shape=[20*43*32, 512],initializer=tf.contrib.layers.xavier_initializer()) L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) L4 = tf.layers.batch_normalization(L4) L4 = tf.nn.dropout(L4, p_keep_hidden) From c97215ced983649044b0f05858dd3efc9cfee22c Mon Sep 17 00:00:00 2001 From: smallerhand Date: Wed, 2 May 2018 17:55:10 +0900 Subject: [PATCH 23/36] all --- x_ksh/mfcc_cnn_ps.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/x_ksh/mfcc_cnn_ps.py b/x_ksh/mfcc_cnn_ps.py index 80c8b11..47a43cb 100644 --- a/x_ksh/mfcc_cnn_ps.py +++ b/x_ksh/mfcc_cnn_ps.py @@ -29,7 +29,7 @@ learning_rate = 0.0002 training_epochs = 500 batch_size = 200 -steps_for_validate = 5 +steps_for_validate = 20 #placeholder X = tf.placeholder(tf.float32, [None, 20, 430], name="X") From c7a5ef8acf77f3f27c40f8aa0a78a0b5a755b1d0 Mon Sep 17 00:00:00 2001 From: smallerhand Date: Wed, 2 May 2018 18:16:14 +0900 Subject: [PATCH 24/36] all --- x_ksh/mfcc_cnn_ps.py | 19 ++++++++++--------- x_ksh/stft_cnn_ps.py | 2 +- 2 files changed, 11 insertions(+), 10 deletions(-) diff --git a/x_ksh/mfcc_cnn_ps.py b/x_ksh/mfcc_cnn_ps.py index 47a43cb..447b891 100644 --- a/x_ksh/mfcc_cnn_ps.py +++ b/x_ksh/mfcc_cnn_ps.py @@ -27,7 +27,7 @@ # hyper parameters learning_rate = 0.0002 -training_epochs = 500 +training_epochs = 700 batch_size = 200 steps_for_validate = 20 @@ -44,28 +44,28 @@ L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME') L1 = tf.nn.elu(L1) L1 = tf.layers.batch_normalization(L1) -#L1 = tf.nn.max_pool(L1, ksize=[1, 2, 43, 1],strides=[1, 2, 43, 1], padding='SAME') +L1 = tf.nn.max_pool(L1, ksize=[1, 2, 43, 1],strides=[1, 2, 43, 1], padding='SAME') L1 = tf.nn.dropout(L1, p_keep_conv) # L2 Input shape=(?,10,21,32) -W2 = tf.get_variable("W2", shape=[2, 43, 32, 32],initializer=tf.contrib.layers.xavier_initializer()) +W2 = tf.get_variable("W2", shape=[3, 3, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') L2 = tf.nn.elu(L2) L2 = tf.layers.batch_normalization(L2) -#L2 = tf.nn.max_pool(L2, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') +L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') L2 = tf.nn.dropout(L2, p_keep_conv) # L3 Input shape=(?,3,12,64) -W3 = tf.get_variable("W3", shape=[2, 43, 32, 32],initializer=tf.contrib.layers.xavier_initializer()) -L3 = tf.nn.conv2d(L1, W3, strides=[1, 1, 1, 1], padding='SAME') +W3 = tf.get_variable("W3", shape=[3, 3, 64, 128],initializer=tf.contrib.layers.xavier_initializer()) +L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') L3 = tf.nn.elu(L3) L3 = tf.layers.batch_normalization(L3) -#L3 = tf.nn.max_pool(L3, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') +L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') L3 = tf.nn.dropout(L3, p_keep_conv) -L3_flat= tf.reshape(L3, shape=[-1, 20*43*32]) +L3_flat= tf.reshape(L3, shape=[-1, 3*3*128]) # Final FC 2*3*128 inputs -> 41 outputs -W4 = tf.get_variable("W4", shape=[20*43*32, 512],initializer=tf.contrib.layers.xavier_initializer()) +W4 = tf.get_variable("W4", shape=[3*3*128, 512],initializer=tf.contrib.layers.xavier_initializer()) L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) L4 = tf.layers.batch_normalization(L4) L4 = tf.nn.dropout(L4, p_keep_hidden) @@ -107,3 +107,4 @@ X: testData[x2], Y: testLabel[x2].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') print('Finished!') + diff --git a/x_ksh/stft_cnn_ps.py b/x_ksh/stft_cnn_ps.py index 2238f00..6b4406a 100644 --- a/x_ksh/stft_cnn_ps.py +++ b/x_ksh/stft_cnn_ps.py @@ -29,7 +29,7 @@ learning_rate = 0.0002 training_epochs = 700 batch_size = 100 -steps_for_validate = 5 +steps_for_validate = 20 #placeholder X = tf.placeholder(tf.float32, [None, 17, 200], name="X") From 26438bdfe28b6c6fb2f511babb6b3d32e78ecbe1 Mon Sep 17 00:00:00 2001 From: smallerhand Date: Wed, 2 May 2018 18:41:52 +0900 Subject: [PATCH 25/36] all --- x_ksh/mfcc_cnn_ps.py | 32 +++++++++++++++++--------------- x_ksh/stft_cnn_ps.py | 4 ++-- 2 files changed, 19 insertions(+), 17 deletions(-) diff --git a/x_ksh/mfcc_cnn_ps.py b/x_ksh/mfcc_cnn_ps.py index 447b891..e55b09b 100644 --- a/x_ksh/mfcc_cnn_ps.py +++ b/x_ksh/mfcc_cnn_ps.py @@ -1,17 +1,14 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Apr 26 18:53:10 2018 +#mfcc_cnn_ps.py +#librosa.features.mfcc(n_mfcc=20) 이용해서 저장한 데이터와 CNN을 이용하여 사운드 분류하는 코드입니다. +#데이터 프로세싱 코드는 mfcc_processing.py에 있습니다. -@author: kimseunghyuck -""" - -#import important modules +#모듈 임포트(numpy, tensorflow, math.ceil) 및 랜덤 지정 import numpy as np import tensorflow as tf from math import ceil tf.set_random_seed(777) +#트레이닝/테스트 셋 각각 데이터/라벨 임포트 trainData = np.genfromtxt('/home/paperspace/Downloads/trainData6.csv', delimiter=',') trainData = trainData.reshape(-1, 20, 430) testData = np.genfromtxt('/home/paperspace/Downloads/testData6.csv', delimiter=',') @@ -19,11 +16,14 @@ trainLabel = np.genfromtxt('/home/paperspace/Downloads/trainLabel6.csv', delimiter=',') testLabel = np.genfromtxt('/home/paperspace/Downloads/testLabel6.csv', delimiter=',') +#임포트한 데이터가 원하는 데이터가 맞는지 shape을 통해 확인 print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape) -# (6631, 20, 200) (2842, 20, 200) (6631,) (2842,) +# (6631, 20, 430) (2842, 20, 430) (6631,) (2842,) +#데이터 준비 끝. #다시 돌릴때는 여기부터 -tf.reset_default_graph() #그래프 초기화 +#그래프 초기화 +tf.reset_default_graph() # hyper parameters learning_rate = 0.0002 @@ -47,7 +47,7 @@ L1 = tf.nn.max_pool(L1, ksize=[1, 2, 43, 1],strides=[1, 2, 43, 1], padding='SAME') L1 = tf.nn.dropout(L1, p_keep_conv) -# L2 Input shape=(?,10,21,32) +# L2 Input shape=(?,10,10,32) W2 = tf.get_variable("W2", shape=[3, 3, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') L2 = tf.nn.elu(L2) @@ -55,24 +55,26 @@ L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') L2 = tf.nn.dropout(L2, p_keep_conv) -# L3 Input shape=(?,3,12,64) +# L3 Input shape=(?,5,5,64) W3 = tf.get_variable("W3", shape=[3, 3, 64, 128],initializer=tf.contrib.layers.xavier_initializer()) L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') L3 = tf.nn.elu(L3) L3 = tf.layers.batch_normalization(L3) L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') L3 = tf.nn.dropout(L3, p_keep_conv) +#L3 shape=(?,3,3,128) L3_flat= tf.reshape(L3, shape=[-1, 3*3*128]) -# Final FC 2*3*128 inputs -> 41 outputs -W4 = tf.get_variable("W4", shape=[3*3*128, 512],initializer=tf.contrib.layers.xavier_initializer()) +# Final FC 3*3*128 inputs -> 217 -> 41 outputs +W4 = tf.get_variable("W4", shape=[3*3*128, 217],initializer=tf.contrib.layers.xavier_initializer()) L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) L4 = tf.layers.batch_normalization(L4) L4 = tf.nn.dropout(L4, p_keep_hidden) -W_o = tf.get_variable("W_o", shape=[512,41],initializer=tf.contrib.layers.xavier_initializer()) +W_o = tf.get_variable("W_o", shape=[217,41],initializer=tf.contrib.layers.xavier_initializer()) b = tf.Variable(tf.random_normal([41])) logits = tf.matmul(L4, W_o) + b logits = tf.layers.batch_normalization(logits) +#logits shape=(?,41) # define cost/loss & optimizer cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) diff --git a/x_ksh/stft_cnn_ps.py b/x_ksh/stft_cnn_ps.py index 6b4406a..e00d4bb 100644 --- a/x_ksh/stft_cnn_ps.py +++ b/x_ksh/stft_cnn_ps.py @@ -65,11 +65,11 @@ L3_flat= tf.reshape(L3, shape=[-1, 3*3*128]) # Final FC 2*3*128 inputs -> 41 outputs -W4 = tf.get_variable("W4", shape=[3*3*128, 512],initializer=tf.contrib.layers.xavier_initializer()) +W4 = tf.get_variable("W4", shape=[3*3*128, 217],initializer=tf.contrib.layers.xavier_initializer()) L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) L4 = tf.layers.batch_normalization(L4) L4 = tf.nn.dropout(L4, p_keep_hidden) -W_o = tf.get_variable("W_o", shape=[512,41],initializer=tf.contrib.layers.xavier_initializer()) +W_o = tf.get_variable("W_o", shape=[217,41],initializer=tf.contrib.layers.xavier_initializer()) b = tf.Variable(tf.random_normal([41])) logits = tf.matmul(L4, W_o) + b logits = tf.layers.batch_normalization(logits) From 4b179fdb735f846aca2c5d0f5a8b2c76bb977471 Mon Sep 17 00:00:00 2001 From: smallerhand Date: Wed, 2 May 2018 19:12:32 +0900 Subject: [PATCH 26/36] all --- x_ksh/stft_cnn_ps.py | 34 +++++++++++++++++++++------------- 1 file changed, 21 insertions(+), 13 deletions(-) diff --git a/x_ksh/stft_cnn_ps.py b/x_ksh/stft_cnn_ps.py index e00d4bb..0c45a21 100644 --- a/x_ksh/stft_cnn_ps.py +++ b/x_ksh/stft_cnn_ps.py @@ -40,38 +40,46 @@ p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") # L1 SoundIn shape=(?, 20, 430, 1) -W1 = tf.get_variable("W1", shape=[2, 24, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +W1 = tf.get_variable("W1", shape=[2, 12, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME') L1 = tf.nn.elu(L1) L1 = tf.layers.batch_normalization(L1) -L1 = tf.nn.max_pool(L1, ksize=[1, 2, 24, 1],strides=[1, 2, 24, 1], padding='SAME') +L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') L1 = tf.nn.dropout(L1, p_keep_conv) # L2 Input shape=(?,10,21,32) -W2 = tf.get_variable("W2", shape=[3, 3, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) +W2 = tf.get_variable("W2", shape=[2, 12, 32, 32],initializer=tf.contrib.layers.xavier_initializer()) L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') L2 = tf.nn.elu(L2) L2 = tf.layers.batch_normalization(L2) -L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') +L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') L2 = tf.nn.dropout(L2, p_keep_conv) # L3 Input shape=(?,3,12,64) -W3 = tf.get_variable("W3", shape=[3, 3, 64, 128],initializer=tf.contrib.layers.xavier_initializer()) +W3 = tf.get_variable("W3", shape=[2, 12, 32, 32],initializer=tf.contrib.layers.xavier_initializer()) L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') L3 = tf.nn.elu(L3) L3 = tf.layers.batch_normalization(L3) -L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') +L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') L3 = tf.nn.dropout(L3, p_keep_conv) -L3_flat= tf.reshape(L3, shape=[-1, 3*3*128]) + +# L4 Input shape=(?,3,25,32) +W4 = tf.get_variable("W4", shape=[2, 12, 32, 32],initializer=tf.contrib.layers.xavier_initializer()) +L4 = tf.nn.conv2d(L3, W4, strides=[1, 1, 1, 1], padding='SAME') +L4 = tf.nn.elu(L3) +L4 = tf.layers.batch_normalization(L3) +L4 = tf.nn.max_pool(L4, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L4 = tf.nn.dropout(L4, p_keep_conv) +L4_flat= tf.reshape(L4, shape=[-1, 2*13*32]) # Final FC 2*3*128 inputs -> 41 outputs -W4 = tf.get_variable("W4", shape=[3*3*128, 217],initializer=tf.contrib.layers.xavier_initializer()) -L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) -L4 = tf.layers.batch_normalization(L4) -L4 = tf.nn.dropout(L4, p_keep_hidden) -W_o = tf.get_variable("W_o", shape=[217,41],initializer=tf.contrib.layers.xavier_initializer()) +W5 = tf.get_variable("W5", shape=[2*13*32, 185],initializer=tf.contrib.layers.xavier_initializer()) +L5 = tf.nn.elu(tf.matmul(L4_flat, W5)) +L5 = tf.layers.batch_normalization(L5) +L5 = tf.nn.dropout(L5, p_keep_hidden) +W_o = tf.get_variable("W_o", shape=[185,41],initializer=tf.contrib.layers.xavier_initializer()) b = tf.Variable(tf.random_normal([41])) -logits = tf.matmul(L4, W_o) + b +logits = tf.matmul(L5, W_o) + b logits = tf.layers.batch_normalization(logits) # define cost/loss & optimizer From 5216982a8f3edcd3f32d7d3a1cd3dd3450e853fc Mon Sep 17 00:00:00 2001 From: smallerhand Date: Wed, 2 May 2018 19:25:07 +0900 Subject: [PATCH 27/36] all --- x_ksh/mfcc_4cnn.py | 120 +++++++++++++++++++++++++++++++++++++++++++ x_ksh/stft_cnn_ps.py | 10 ++-- 2 files changed, 125 insertions(+), 5 deletions(-) create mode 100644 x_ksh/mfcc_4cnn.py diff --git a/x_ksh/mfcc_4cnn.py b/x_ksh/mfcc_4cnn.py new file mode 100644 index 0000000..d76daea --- /dev/null +++ b/x_ksh/mfcc_4cnn.py @@ -0,0 +1,120 @@ +#mfcc_cnn_ps.py +#librosa.features.mfcc(n_mfcc=20) 이용해서 저장한 데이터와 CNN을 이용하여 사운드 분류하는 코드입니다. +#데이터 프로세싱 코드는 mfcc_processing.py에 있습니다. + +#모듈 임포트(numpy, tensorflow, math.ceil) 및 랜덤 지정 +import numpy as np +import tensorflow as tf +from math import ceil +tf.set_random_seed(777) + +#트레이닝/테스트 셋 각각 데이터/라벨 임포트 +trainData = np.genfromtxt('/home/paperspace/Downloads/trainData6.csv', delimiter=',') +trainData = trainData.reshape(-1, 20, 430) +testData = np.genfromtxt('/home/paperspace/Downloads/testData6.csv', delimiter=',') +testData = testData.reshape(-1, 20, 430) +trainLabel = np.genfromtxt('/home/paperspace/Downloads/trainLabel6.csv', delimiter=',') +testLabel = np.genfromtxt('/home/paperspace/Downloads/testLabel6.csv', delimiter=',') + +#임포트한 데이터가 원하는 데이터가 맞는지 shape을 통해 확인 +print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape) +# (6631, 20, 430) (2842, 20, 430) (6631,) (2842,) + +#데이터 준비 끝. +#다시 돌릴때는 여기부터 +#그래프 초기화 +tf.reset_default_graph() + +# hyper parameters +learning_rate = 0.0002 +training_epochs = 700 +batch_size = 200 +steps_for_validate = 20 + +#placeholder +X = tf.placeholder(tf.float32, [None, 20, 430], name="X") +X_sound = tf.reshape(X, [-1, 20, 430, 1]) +Y = tf.placeholder(tf.int32, [None, 1], name="Y") +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv") +p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") + + +# L1 SoundIn shape=(?, 20, 430, 1) +W1 = tf.get_variable("W1", shape=[2, 21, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME') +L1 = tf.nn.elu(L1) +L1 = tf.layers.batch_normalization(L1) +L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L1 = tf.nn.dropout(L1, p_keep_conv) + +# L2 Input shape=(?,10,21,32) +W2 = tf.get_variable("W2", shape=[2, 21, 32, 32],initializer=tf.contrib.layers.xavier_initializer()) +L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') +L2 = tf.nn.elu(L2) +L2 = tf.layers.batch_normalization(L2) +L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L2 = tf.nn.dropout(L2, p_keep_conv) + +# L3 Input shape=(?,3,12,64) +W3 = tf.get_variable("W3", shape=[2, 21, 32, 32],initializer=tf.contrib.layers.xavier_initializer()) +L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') +L3 = tf.nn.elu(L3) +L3 = tf.layers.batch_normalization(L3) +L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L3 = tf.nn.dropout(L3, p_keep_conv) + +# L4 Input shape=(?,3,25,32) +W4 = tf.get_variable("W4", shape=[2, 21, 32, 32],initializer=tf.contrib.layers.xavier_initializer()) +L4 = tf.nn.conv2d(L3, W4, strides=[1, 1, 1, 1], padding='SAME') +L4 = tf.nn.elu(L4) +L4 = tf.layers.batch_normalization(L4) +L4 = tf.nn.max_pool(L4, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L4 = tf.nn.dropout(L4, p_keep_conv) +L4_flat= tf.reshape(L4, shape=[-1, 26*32]) + +# Final FC 2*3*128 inputs -> 41 outputs +W5 = tf.get_variable("W5", shape=[26*32, 185],initializer=tf.contrib.layers.xavier_initializer()) +L5 = tf.nn.elu(tf.matmul(L4_flat, W5)) +L5 = tf.layers.batch_normalization(L5) +L5 = tf.nn.dropout(L5, p_keep_hidden) +W_o = tf.get_variable("W_o", shape=[185,41],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L5, W_o) + b +logits = tf.layers.batch_normalization(logits) +#logits shape=(?,41) + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +saver = tf.train.Saver() + +# train my model +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = ceil(len(trainData) / batch_size) + for i in range(total_batch): + batch_xs = trainData[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .8, p_keep_hidden: 0.7} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + if epoch % steps_for_validate == steps_for_validate-1: + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + x1=np.random.choice(testLabel.shape[0], 300, replace=False) + print('TrainAccuracy:', sess.run(accuracy, feed_dict={ + X: trainData[x1], Y: trainLabel[x1].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + x2=np.random.choice(testLabel.shape[0], 300, replace=False) + print('TestAccuracy:', sess.run(accuracy, feed_dict={ + X: testData[x2], Y: testLabel[x2].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') +print('Finished!') + diff --git a/x_ksh/stft_cnn_ps.py b/x_ksh/stft_cnn_ps.py index 0c45a21..df7175d 100644 --- a/x_ksh/stft_cnn_ps.py +++ b/x_ksh/stft_cnn_ps.py @@ -66,18 +66,18 @@ # L4 Input shape=(?,3,25,32) W4 = tf.get_variable("W4", shape=[2, 12, 32, 32],initializer=tf.contrib.layers.xavier_initializer()) L4 = tf.nn.conv2d(L3, W4, strides=[1, 1, 1, 1], padding='SAME') -L4 = tf.nn.elu(L3) -L4 = tf.layers.batch_normalization(L3) +L4 = tf.nn.elu(L4) +L4 = tf.layers.batch_normalization(L4) L4 = tf.nn.max_pool(L4, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') L4 = tf.nn.dropout(L4, p_keep_conv) -L4_flat= tf.reshape(L4, shape=[-1, 2*13*32]) +L4_flat= tf.reshape(L4, shape=[-1, 12*32]) # Final FC 2*3*128 inputs -> 41 outputs -W5 = tf.get_variable("W5", shape=[2*13*32, 185],initializer=tf.contrib.layers.xavier_initializer()) +W5 = tf.get_variable("W5", shape=[12*32, 125],initializer=tf.contrib.layers.xavier_initializer()) L5 = tf.nn.elu(tf.matmul(L4_flat, W5)) L5 = tf.layers.batch_normalization(L5) L5 = tf.nn.dropout(L5, p_keep_hidden) -W_o = tf.get_variable("W_o", shape=[185,41],initializer=tf.contrib.layers.xavier_initializer()) +W_o = tf.get_variable("W_o", shape=[125,41],initializer=tf.contrib.layers.xavier_initializer()) b = tf.Variable(tf.random_normal([41])) logits = tf.matmul(L5, W_o) + b logits = tf.layers.batch_normalization(logits) From d909fcd32dd7e95d72de1789e16f74d2dc6ded76 Mon Sep 17 00:00:00 2001 From: suwonyu <37686127+suwonyu@users.noreply.github.com> Date: Wed, 2 May 2018 20:14:32 +0900 Subject: [PATCH 28/36] Add files via upload --- PROJECT2/audio_conv2d_features.py | 169 +++++++++++++++++++++++++++ PROJECT2/audio_conv2d_mel.py | 187 ++++++++++++++++++++++++++++++ PROJECT2/extract_features.py | 51 ++++++++ PROJECT2/mel_extract_128.py | 59 ++++++++++ 4 files changed, 466 insertions(+) create mode 100644 PROJECT2/audio_conv2d_features.py create mode 100644 PROJECT2/audio_conv2d_mel.py create mode 100644 PROJECT2/extract_features.py create mode 100644 PROJECT2/mel_extract_128.py diff --git a/PROJECT2/audio_conv2d_features.py b/PROJECT2/audio_conv2d_features.py new file mode 100644 index 0000000..a3e7fba --- /dev/null +++ b/PROJECT2/audio_conv2d_features.py @@ -0,0 +1,169 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Sun Apr 29 05:53:22 2018 +@author: itwill03 +""" +#전처리 code : extract_features.py + +import numpy as np +import os +import glob +import tensorflow as tf +import pandas as pd + +tf.set_random_seed(777) + +train_info = pd.read_csv("C:/data/sound/train.csv",delimiter=',') +train_data = np.genfromtxt("C:/data/sound/feature_train.csv", delimiter=',') + + +#label set : 라벨 값을 0~41로 바꿈 + +def labels2Num(labels): + df_label = pd.DataFrame(labels) + l = train_info['label'].unique() + for i in range(len(l)): + df_label[df_label==l[i]] = i + return df_label + +labels = train_info['label'] +df_label = labels2Num(labels) + +#train data set +train_data = pd.DataFrame(train_data) +train_data['label']=df_label +train_data = train_data.astype(np.float32) + +#훈련세트, validation세트 나누기 +from sklearn.model_selection import train_test_split +train_set, validate_set = train_test_split(train_data, test_size = 0.3) +trainData = train_set.values[:,0:193] +trainLabel = train_set.values[:,-1] +validateData = validate_set.values[:,0:193] +validataLabel = validate_set.values[:,-1] + + +# 텐서플로우 모델 생성 +tf.reset_default_graph() + +n_dim = 193 +n_classes = 41 +training_epochs = 100 +learning_rate = 0.001 +batch_size = 100 +steps_for_validate = 5 +keep_prob = tf.placeholder(tf.float32) + +X = tf.placeholder(tf.float32, [None, n_dim]) +Y = tf.placeholder(tf.int32, [None, 1]) +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name='p_keep_conv') +p_keep_hidden = tf.placeholder(tf.float32, name='p_keep_hidden') + +#convolution layer 1 +c1 = tf.layers.conv2d(tf.reshape(X, [-1, 1, n_dim, 1]), 32, kernel_size=[2, 1], strides=(1, 1), padding='same', + activation=tf.nn.elu, name="c1") +n1 = tf.layers.batch_normalization(c1) +p1 = tf.layers.max_pooling2d(inputs=n1, pool_size=[1, 2], strides=2) +p1 = tf.nn.dropout(p1, p_keep_conv) + +#convolution layer 2 + # shape=(?, 1, 96, 32) +c2 = tf.layers.conv2d(p1, 64, kernel_size=[2, 1], strides=(1, 1), padding='same', + activation=tf.nn.elu, name="c2") +n2 = tf.layers.batch_normalization(c2) +p2 = tf.layers.max_pooling2d(inputs=n2, pool_size=[1, 2], strides=2) +p2 = tf.nn.dropout(p2, p_keep_conv) + +#convolution layer 3 + # shape=(?, 1, 48, 64) +c3 = tf.layers.conv2d(p2, 128, kernel_size=[2, 1], strides=(1, 1), padding='same', + activation=tf.nn.elu, name="c3") +n3 = tf.layers.batch_normalization(c3) +p3 = tf.layers.max_pooling2d(inputs=n3, pool_size=[1, 2], strides=2) +p3 = tf.nn.dropout(p3, p_keep_conv) + + +# shape=(?, 1, 24, 128) +L4_flat = tf.reshape(p3, shape=[-1, 1*24*128]) +W1 = tf.get_variable("W1", shape=[1*24*128, 624], initializer=tf.contrib.layers.xavier_initializer()) +L5 = tf.nn.relu(tf.matmul(L4_flat, W1)) +n4 = tf.layers.batch_normalization(L5) +L5 = tf.nn.dropout(n4, p_keep_hidden) + +#shape=(?, 624) +W2 = tf.get_variable("W2", shape=[624,41],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L5, W2) + b + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +saver = tf.train.Saver() + + +# train my model + +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = int(len(trainData) / batch_size) + for i in range(total_batch): + batch_xs = trainData[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .7, p_keep_hidden: .5} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + if epoch % steps_for_validate == steps_for_validate-1: + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + print('Accuracy:', sess.run(accuracy, feed_dict={ + X: validateData, Y: validataLabel.reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) +print('Finished!') + +tf.reset_default_graph() +""" +#1 +training_epochs = 500 +learning_rate = 0.001 +sigmoid +conv2d : kenel size = 1,2 , strides=(1, 1) +pool_size=[1, 2], strides=2 +flat = 1000 +adam +cost = 1.203582104 +ccuracy: 0.476073 + +#2 'sigmoid -> relu' +training_epochs = 500 +learning_rate = 0.001 +relu +conv2d kenel size = 1,2 +flat = 1000 +adam +cost = 0.021049510 +Accuracy: 0.626319 + +#3 'kanel size = 1,2 -> 1,5' +training_epochs = 500 +learning_rate = 0.001 +relu +conv2d kenel size = 1,5 +flat = 1000 +adam +cost = 0.026283483 +Accuracy: 0.626671 + +#4 relu -> elu +Accuracy: 0.64145 +#5 conv2d layer +1, drop out +cost = 0.300675828 +Accuracy: 0.632301 +""" \ No newline at end of file diff --git a/PROJECT2/audio_conv2d_mel.py b/PROJECT2/audio_conv2d_mel.py new file mode 100644 index 0000000..074e9e9 --- /dev/null +++ b/PROJECT2/audio_conv2d_mel.py @@ -0,0 +1,187 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Sun Apr 29 05:53:22 2018 +@author: itwill03 +""" + +# 전처리 코드 : mel_extract_128.py + + +import numpy as np +import os +import glob +import tensorflow as tf +import pandas as pd + +tf.set_random_seed(777) + +train_info = pd.read_csv("/home/itwill03/sound/train.csv",delimiter=',') +train_data = np.genfromtxt("/home/itwill03/sound/mel_train2.csv", delimiter=',') +#train_info = pd.read_csv("C:\data\sound/train.csv",delimiter=',') +#train_data = np.genfromtxt("C:\data\sound\mel_train2.csv", delimiter=',') + + +#labels to number : 라벨은 0~41의 숫자로 전환 +def labels2Num(labels): + df_label = pd.DataFrame(labels) + l = train_info['label'].unique() + for i in range(len(l)): + df_label[df_label==l[i]] = i + return df_label + +labels = train_info['label'] +df_label = labels2Num(labels) + +#train data set +train_data = pd.DataFrame(train_data) +train_data['label']=df_label +train_data = train_data.astype(np.float32) + +#np.savetxt("c:/data/sound/train_data.csv",train_data, delimiter=",") + +#훈련세트, validation세트 나누기 +from sklearn.model_selection import train_test_split +train_set, validate_set = train_test_split(train_data, test_size = 0.3) +trainData = train_set.values[:,0:16384] +trainLabel = train_set.values[:,-1] +validateData = validate_set.values[:,0:16384] +validataLabel = validate_set.values[:,-1] + +# graph reset +tf.reset_default_graph() + +# 텐서플로우 모델 생성 +n_dim = 16384 +n_classes = 41 +training_epochs = 700 +learning_rate = 0.0004 +batch_size = 100 +steps_for_validate = 5 +keep_prob = tf.placeholder(tf.float32) + +X = tf.placeholder(tf.float32, [None, n_dim]) +Y = tf.placeholder(tf.int32, [None, 1]) +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name='p_keep_conv') +p_keep_hidden = tf.placeholder(tf.float32, name='p_keep_hidden') + + +#convolution layer1 + # img shape = (?, 128, 128, 1) +c1 = tf.layers.conv2d(tf.reshape(X, [-1, 128, 128, 1]), 32, kernel_size=[5, 3], strides=(1, 1), + padding='same', activation=tf.nn.relu, name="c1") +n1 = tf.layers.batch_normalization(c1) +p1 = tf.layers.max_pooling2d(inputs=n1, pool_size=[4, 4], strides=4) +p1 = tf.nn.dropout(p1, p_keep_conv) + +#convolution layer2 + # img shape = (?, 32, 32, 32) +c2 = tf.layers.conv2d(tf.reshape(p1, [-1, 32, 32, 32]), 64, kernel_size=[5, 3], strides=(1, 1), + padding='same', activation=tf.nn.relu, name="c2") +n2 = tf.layers.batch_normalization(c2) +p2 = tf.layers.max_pooling2d(inputs=n2, pool_size=[3, 3], strides=2) #shape = [?, 1, 48, 100] +p2 = tf.nn.dropout(p2, p_keep_conv) + +#convolution layer3 + # img shape = (?, 15, 15, 64) +c3 = tf.layers.conv2d(tf.reshape(p2, [-1, 15, 15, 64]), 128, kernel_size=[5, 3], strides=(1, 1), + padding='same', activation=tf.nn.relu, name="c3") +n3 = tf.layers.batch_normalization(c3) +p3 = tf.layers.max_pooling2d(inputs=n3, pool_size=[3, 3], strides=2) #shape = [?, 1, 24, 200] +p3 = tf.nn.dropout(p3, p_keep_conv) + +#flating layer, hidenlayer +L4_flat = tf.reshape(p3, shape=[-1, 7*7*128]) +W1 = tf.get_variable("W1", shape=[7*7*128, 640], initializer=tf.contrib.layers.xavier_initializer()) +L5 = tf.nn.relu(tf.matmul(L4_flat, W1)) +n5 = tf.layers.batch_normalization(L5) +L5 = tf.nn.dropout(n5, p_keep_hidden) + +W2 = tf.get_variable("W2", shape=[640,41],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L5, W2) + b + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +saver = tf.train.Saver() + + +# train my model +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = int(len(trainData) / batch_size) + for i in range(total_batch): + batch_xs = trainData[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .7, p_keep_hidden: .5} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + if epoch % steps_for_validate == steps_for_validate-1: + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + x=np.random.choice(validataLabel.shape[0], 500, replace=False) + print('Accuracy:', sess.run(accuracy, feed_dict={ + X: validateData[x], Y: validataLabel[x].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + #save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') +print('Finished!') + +""" +#1. +training_epochs = 700 +learning_rate = 0.0004 +activation = elu +conv = kernel_size=[3, 3], strides=(1, 1) + kernel_size=[3, 3], strides=(1, 1) + kernel_size=[3, 3], strides=(1, 1) +pool = pool_size=[2, 2], strides=2 + pool_size=[2, 2], strides=2 + pool_size=[2, 2], strides=2 +flat = 16*16*128 -> 64 +p_keep_conv: .7, p_keep_hidden: .5 +cost : 0.06~0.08에서 계속 떨어지는중 +Accuracy: 59~63% +전처리시 정규화 고려.. librosa.util.normalize +윈도우 14*2 고려.. +러닝레이트 조절 + +#2. +training_epochs = 700 +learning_rate = 0.0001 +activation = relu +conv = kernel_size=[5, 3], strides=(1, 1) + kernel_size=[5, 3], strides=(1, 1) + kernel_size=[5, 3], strides=(1, 1) +pool = pool_size=[4, 4], strides=4 + pool_size=[3, 3], strides=2 + pool_size=[3, 3], strides=2 +flat = 7*7*128, 640 +p_keep_conv: .7, p_keep_hidden: .5 +cost = 0.122217526 +Accuracy: 60~67% + +#3. +training_epochs = 700 +learning_rate = 0.0004 +activation = relu +conv = kernel_size=[5, 3], strides=(1, 1) + kernel_size=[5, 3], strides=(1, 1) + kernel_size=[5, 3], strides=(1, 1) +pool = pool_size=[12, 4], strides=4 + pool_size=[12, 4], strides=2 + pool_size=[2, 2], strides=2 +flat = 5*7*128, 420 +p_keep_conv: .8, p_keep_hidden: .7 +Accuracy: 57~64% + + + +""" diff --git a/PROJECT2/extract_features.py b/PROJECT2/extract_features.py new file mode 100644 index 0000000..51a069d --- /dev/null +++ b/PROJECT2/extract_features.py @@ -0,0 +1,51 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Apr 23 23:59:42 2018 + +@author: modes +""" +import os +import glob +import librosa +import numpy as np + +""" +행,열 축을 변환하여 특성값을 추출하는 함수 +stft(Short-time Fourier transform) :복소수 값을 갖는 행렬을 반환 +mfcc(Mel-frequency cepstral coefficients), +chroma_stft(chromagram from a waveform or power spectrogram), +melspectrogram(Mel-scaled power spectrogram), +spectral_contrast(spectral contrast), +tonnetz(tonal centroid features) +""" +def extract_feature(file_name): + X, sample_rate = librosa.load(file_name) #부동 소수점 시계열로 로드, sample_rate:자동 리샘플링(default sr=22050) + stft = np.abs(librosa.stft(X)) #stft를 절대값으로 변환 + mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T,axis=0) #오디오 신호를 mfcc로 바꿈 + chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0) # stft에서 chromagram 계산 + mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0) #melspectrogram + contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0) # spectral_contrast + tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0) #tonnetz + return mfccs,chroma,mel,contrast,tonnetz + +#hstack feature matrix : 추출한 값을 열을 기준으로 스택 +def parse_audio_files(filenames): + rows = len(filenames) + features = np.zeros((rows,193)) + i = 0 + for f_names in filenames: + mfccs, chroma, mel, contrast, tonnetz = extract_feature(f_names) + features[i] = np.hstack([mfccs, chroma, mel, contrast, tonnetz]) + i += 1 + return features + +#file_list 생성 +file = "C:/data/sound/audio_train/*.wav" +train_list=glob.glob(file) + +file = "c:/data/sound/audio_test/*.wav" +test_list=glob.glob(file) + +#extrect audio features : 함수 실행 +feature_train = parse_audio_files(train_list) +feature_test = parse_audio_files(test_list) \ No newline at end of file diff --git a/PROJECT2/mel_extract_128.py b/PROJECT2/mel_extract_128.py new file mode 100644 index 0000000..5989eec --- /dev/null +++ b/PROJECT2/mel_extract_128.py @@ -0,0 +1,59 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Apr 30 14:48:54 2018 + +@author: stu +""" + +import os +import glob +import librosa +import numpy as np +import matplotlib.pyplot as plt + +#file_list 생성 +file = "C:/data/sound/audio_train/*.wav" +train_list=glob.glob(file) + +file = "c:/data/sound/audio_test/*.wav" +test_list=glob.glob(file) + + +#zero padding funtion : 128보다 작은경우 128*128로 zero padding +def mel2square(mel): +# 1 zero padded to the top +# 2 zeros padded to the bottom +# 2 zeros padded to the left +# 1 zero padded to the right + y,x=mel.shape + return np.pad(mel, ((0,0),(0,abs(x-y))), 'constant') +mel.shape #Out[96]: (105, 128) +mel2square(mel).shape #Out[95]: (128, 128) + +# extract melspectrogram with padding : 'duration=2.97'로 설정해서 y축길이의 max값을 128로 고정 +def Mk_mel(audio_list): + b= [] + for i in audio_list: + y,sr = librosa.load(i, duration=2.97) + mel = librosa.feature.melspectrogram(y=y, sr=sr) + res = mel2square(mel) + b.append(res) + print(i) + return np.array(b) + + +lists = Mk_mel(train_list) + +#uploading csv file : shape = [?,128,128] +lists2D=np.reshape(lists,[-1,16384]) +np.savetxt('C:/data/sound/mel_train4.csv',lists2D, delimiter=',') + +mel_train=np.genfromtxt('C:/data/sound/mel_train5.csv', delimiter=',') + + + + + + + + From f683b7b39f62842e02debf29639bf50d5d6410ee Mon Sep 17 00:00:00 2001 From: suwonyu <37686127+suwonyu@users.noreply.github.com> Date: Wed, 2 May 2018 20:34:08 +0900 Subject: [PATCH 29/36] Create audio_conv1d_features.py --- x_ysw/audio_conv1d_features.py | 130 +++++++++++++++++++++++++++++++++ 1 file changed, 130 insertions(+) create mode 100644 x_ysw/audio_conv1d_features.py diff --git a/x_ysw/audio_conv1d_features.py b/x_ysw/audio_conv1d_features.py new file mode 100644 index 0000000..474d50b --- /dev/null +++ b/x_ysw/audio_conv1d_features.py @@ -0,0 +1,130 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed May 2 19:21:50 2018 + +@author: stu +""" + +#전처리 code : extract_features.py + +import numpy as np +import os +import glob +import tensorflow as tf +import pandas as pd + +tf.set_random_seed(777) + +train_info = pd.read_csv("C:/data/sound/train.csv",delimiter=',') +train_data = np.genfromtxt("C:/data/sound/feature_train.csv", delimiter=',') + +#label set : 라벨 값을 0~41로 바꿈 + +def labels2Num(labels): + df_label = pd.DataFrame(labels) + l = train_info['label'].unique() + for i in range(len(l)): + df_label[df_label==l[i]] = i + return df_label + +labels = train_info['label'] +df_label = labels2Num(labels) + +#train data set +train_data = pd.DataFrame(train_data) +train_data['label']=df_label +train_data = train_data.astype(np.float32) + +#훈련세트, validation세트 나누기 +from sklearn.model_selection import train_test_split +train_set, validate_set = train_test_split(train_data, test_size = 0.3) +trainData = train_set.values[:,0:193] +trainLabel = train_set.values[:,-1] +validateData = validate_set.values[:,0:193] +validataLabel = validate_set.values[:,-1] + + +# 텐서플로우 모델 생성 +tf.reset_default_graph() + +n_dim = 193 +n_classes = 41 +training_epochs = 700 +learning_rate = 0.001 +batch_size = 100 +steps_for_validate = 5 +keep_prob = tf.placeholder(tf.float32) + +X = tf.placeholder(tf.float32, [None, n_dim]) +X_1d = tf.reshape(X, [-1,193,1]) +Y = tf.placeholder(tf.int32, [None, 1]) +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name='p_keep_conv') +p_keep_hidden = tf.placeholder(tf.float32, name='p_keep_hidden') +193*1.5 +#convolution layer 1 +c1 = tf.layers.conv1d(X_1d, 386,kernel_size=2, strides=1, padding='Same', + activation=tf.nn.relu, name='c1') +n1 = tf.layers.batch_normalization(c1) +p1 = tf.layers.max_pooling1d(n1, pool_size=2, strides=2, padding='Same') +p1 = tf.nn.dropout(p1, p_keep_conv) +386*1.5 +#shape=(?, 97, 386) +c2 = tf.layers.conv1d(p1, 579,kernel_size=2, strides=1, padding='Same', + activation=tf.nn.relu, name='c2') +n2 = tf.layers.batch_normalization(c2) +p2 = tf.layers.max_pooling1d(n2, pool_size=2, strides=2, padding='Same') +p2 = tf.nn.dropout(p2, p_keep_conv) + +#shape=(?, 49, 579) +c3 = tf.layers.conv1d(p2, 579,kernel_size=1, strides=1, padding='Same', + activation=tf.nn.relu, name='c3') +n3 = tf.layers.batch_normalization(c3) +p3 = tf.layers.max_pooling1d(n3, pool_size=2, strides=2, padding='Same') +p3 = tf.nn.dropout(p3, p_keep_conv) + +#shape=(?, 25, 579) +L4_flat = tf.reshape(p3, [-1,25*579]) + +W4 = tf.get_variable("W4", shape=[25*579, 624], initializer=tf.contrib.layers.xavier_initializer()) +L5 = tf.nn.relu(tf.matmul(L4_flat, W4)) +n5 = tf.layers.batch_normalization(L5) +L5 = tf.nn.dropout(n5, p_keep_hidden) + +W5 = tf.get_variable("W5", shape=[624,41], initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L5, W5) + b + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +saver = tf.train.Saver() + + +# train my model + +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = int(len(trainData) / batch_size) + for i in range(total_batch): + batch_xs = trainData[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .7, p_keep_hidden: .5} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + if epoch % steps_for_validate == steps_for_validate-1: + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + print('Accuracy:', sess.run(accuracy, feed_dict={ + X: validateData, Y: validataLabel.reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) +print('Finished!') + + From 085458d10671eb6fb55ea00b99ab85f269dd221b Mon Sep 17 00:00:00 2001 From: smallerhand Date: Wed, 2 May 2018 20:56:23 +0900 Subject: [PATCH 30/36] all --- .../optx/checkpoint" | 0 .../MNIST\352\264\200\353\240\250/optx/optx" | 0 .../optx/optx.data-00000-of-00001" | Bin .../optx/optx.index" | Bin .../optx/optx.meta" | Bin x_ksh/__pycache__/mfcc.cpython-36.pyc | Bin 0 -> 852 bytes .../mfcc_processing.cpython-36.pyc | Bin 0 -> 2913 bytes x_ksh/__pycache__/sound_pred.cpython-36.pyc | Bin 0 -> 1278 bytes x_ksh/__pycache__/stft.cpython-36.pyc | Bin 0 -> 869 bytes x_ksh/{ => keep}/batch2_ps.py | 0 x_ksh/mfcc.py | 21 +++++++++++ x_ksh/mfcc_4cnn.py | 4 +- x_ksh/mfcc_cnn_ps.py | 4 +- x_ksh/mfcc_processing.py | 32 +++++++--------- x_ksh/optx2/checkpoint | 2 + x_ksh/optx2/optx2.data-00000-of-00001 | Bin 0 -> 1541360 bytes x_ksh/optx2/optx2.index | Bin 0 -> 2377 bytes x_ksh/optx2/optx2.meta | Bin 0 -> 304335 bytes x_ksh/sound_pred.py | 32 ++++++++++++++++ x_ksh/sound_pred_try.py | 35 ++++++++++++++++++ x_ksh/stft.py | 23 ++++++++++++ x_ksh/stft_cnn_ps.py | 2 +- 22 files changed, 132 insertions(+), 23 deletions(-) rename x_ksh/optx/checkpoint => "x_ksh/MNIST\352\264\200\353\240\250/optx/checkpoint" (100%) rename x_ksh/optx/optx => "x_ksh/MNIST\352\264\200\353\240\250/optx/optx" (100%) rename x_ksh/optx/optx.data-00000-of-00001 => "x_ksh/MNIST\352\264\200\353\240\250/optx/optx.data-00000-of-00001" (100%) rename x_ksh/optx/optx.index => "x_ksh/MNIST\352\264\200\353\240\250/optx/optx.index" (100%) rename x_ksh/optx/optx.meta => "x_ksh/MNIST\352\264\200\353\240\250/optx/optx.meta" (100%) create mode 100644 x_ksh/__pycache__/mfcc.cpython-36.pyc create mode 100644 x_ksh/__pycache__/mfcc_processing.cpython-36.pyc create mode 100644 x_ksh/__pycache__/sound_pred.cpython-36.pyc create mode 100644 x_ksh/__pycache__/stft.cpython-36.pyc rename x_ksh/{ => keep}/batch2_ps.py (100%) create mode 100644 x_ksh/mfcc.py create mode 100644 x_ksh/optx2/checkpoint create mode 100644 x_ksh/optx2/optx2.data-00000-of-00001 create mode 100644 x_ksh/optx2/optx2.index create mode 100644 x_ksh/optx2/optx2.meta 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100% rename from x_ksh/batch2_ps.py rename to x_ksh/keep/batch2_ps.py diff --git a/x_ksh/mfcc.py b/x_ksh/mfcc.py new file mode 100644 index 0000000..3b87260 --- /dev/null +++ b/x_ksh/mfcc.py @@ -0,0 +1,21 @@ +def five_sec_extract(file): + import librosa + import numpy as np + array = np.repeat(0., 20 * 430).reshape(20, 430) + y, sr = librosa.core.load(file, + mono=True, res_type="kaiser_fast") + mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) + length=mfcc.shape[1] + if length == 430: + array=mfcc + elif length < 430: + array[:, :length]=mfcc + elif length > 430: + sample = np.repeat(0., (length - 430)*20).reshape(20,length - 430) + for j in range(length - 430): + for i in range(20): + sample[i,j]=np.var(mfcc[i,j:j+430]) + A=np.argmax(sample, axis=1) + start=np.argmax(np.bincount(A)) + array=mfcc[:, start:start+430] + return(array.reshape(20*430)) \ No newline at end of file diff --git a/x_ksh/mfcc_4cnn.py b/x_ksh/mfcc_4cnn.py index d76daea..28442ba 100644 --- a/x_ksh/mfcc_4cnn.py +++ b/x_ksh/mfcc_4cnn.py @@ -1,5 +1,5 @@ #mfcc_cnn_ps.py -#librosa.features.mfcc(n_mfcc=20) 이용해서 저장한 데이터와 CNN을 이용하여 사운드 분류하는 코드입니다. +#librosa.feature.mfcc(n_mfcc=20) 이용해서 저장한 데이터와 CNN을 이용하여 사운드 분류하는 코드입니다. #데이터 프로세싱 코드는 mfcc_processing.py에 있습니다. #모듈 임포트(numpy, tensorflow, math.ceil) 및 랜덤 지정 @@ -27,7 +27,7 @@ # hyper parameters learning_rate = 0.0002 -training_epochs = 700 +training_epochs = 300 batch_size = 200 steps_for_validate = 20 diff --git a/x_ksh/mfcc_cnn_ps.py b/x_ksh/mfcc_cnn_ps.py index e55b09b..2692254 100644 --- a/x_ksh/mfcc_cnn_ps.py +++ b/x_ksh/mfcc_cnn_ps.py @@ -1,5 +1,5 @@ #mfcc_cnn_ps.py -#librosa.features.mfcc(n_mfcc=20) 이용해서 저장한 데이터와 CNN을 이용하여 사운드 분류하는 코드입니다. +#librosa.feature.mfcc(n_mfcc=20) 이용해서 저장한 데이터와 CNN을 이용하여 사운드 분류하는 코드입니다. #데이터 프로세싱 코드는 mfcc_processing.py에 있습니다. #모듈 임포트(numpy, tensorflow, math.ceil) 및 랜덤 지정 @@ -27,7 +27,7 @@ # hyper parameters learning_rate = 0.0002 -training_epochs = 700 +training_epochs = 300 batch_size = 200 steps_for_validate = 20 diff --git a/x_ksh/mfcc_processing.py b/x_ksh/mfcc_processing.py index f86fb52..efaa073 100644 --- a/x_ksh/mfcc_processing.py +++ b/x_ksh/mfcc_processing.py @@ -1,13 +1,8 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Apr 26 18:53:10 2018 - -@author: kimseunghyuck -""" +#mfcc_processing.py +#librosa.feature.mfcc를 이용해서 mfcc 데이터를 저장하는 코드입니다. +#필요한 모듈 임포트 import librosa -import soundfile as sf import numpy as np from matplotlib import pyplot as plt #import labels @@ -32,21 +27,22 @@ def see_how_long(file): c=[] for filename in file: - y, sr = sf.read(path+filename, dtype='float32') + y, sr = librosa.core.load(path+'audio_train/'+filename, + mono=True, res_type="kaiser_fast") mfcc=librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) abs_mfcc=np.abs(mfcc) - #1025 X t 형태 c.append(abs_mfcc.shape[1]) return(c) #n=see_how_long(trainfile) -#print(np.max(n), np.min(n)) #2584 28 +#print(np.max(n), np.min(n)) #1292, 14 #n2=see_how_long(testfile) -#print(np.max(n2), np.min(n2)) #2584 26 +#print(np.max(n2), np.min(n2)) #1292, 13 #show me approximate wave shape filename= trainfile[11] -y, sr = sf.read(path+filename, dtype='float32') +y, sr = librosa.core.load(path+'audio_train/'+filename, + mono=True, res_type="kaiser_fast") mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) length=mfcc.shape[1] plt.plot(mfcc[3,]) @@ -90,12 +86,11 @@ def five_sec_extract(file): print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape) # (6631, 20, 200) (2842, 20, 200) (6631,) (2842,) -#how many kinds of label? +#라벨이 총 몇개가 되어야 하는지 확인 print(len(np.unique(trainLabel))) #41 print(len(np.unique(testLabel))) #41 -#label string -> integer(0~40) - +#문자열로 되어있는 라벨을 전부 0~40으로 바꾼다. def Labeling(label): #idx = np.unique(train.values[:,1]) #이건 abc 순 idx = train.label.unique() @@ -106,10 +101,11 @@ def Labeling(label): trainLabel=Labeling(trainLabel) testLabel=Labeling(testLabel) +#라벨이 0~40으로 잘 들어갔는지 확인 print(min(trainLabel), max(trainLabel), min(testLabel), max(testLabel)) -#0 40 0 40 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self.X=self.graph.get_tensor_by_name('X:0') + self.pred=self.graph.get_tensor_by_name('pred:0') + self.p_keep_conv=self.graph.get_tensor_by_name('p_keep_conv:0') + self.p_keep_hidden=self.graph.get_tensor_by_name('p_keep_hidden:0') + print("Variables Saved") + + def tryit(self, soundaddr): + import librosa + import numpy as np + from mfcc_processing import five_sec_extract + self.soundaddr=soundaddr + #mfcc processing + mfcc, _=five_sec_extract(self.soundaddr) + #classification result + self.result=self.sess.run(self.pred, feed_dict={self.X: mfcc, self.p_keep_conv: 1.0, self.p_keep_hidden: 1.0}) + print(self.result) diff --git a/x_ksh/sound_pred_try.py b/x_ksh/sound_pred_try.py new file mode 100644 index 0000000..2b9641f --- /dev/null +++ b/x_ksh/sound_pred_try.py @@ -0,0 +1,35 @@ +import tensorflow as tf +import numpy as np + +opt_addr="/Users/kimseunghyuck/desktop/git/daegon/KYLius-method/x_ksh/optx2/optx2" + +class sound_pred: + def __init__(self, opt_addr): + # initialize/ load + self.saver=tf.train.import_meta_graph(opt_addr+".meta") + self.sess = tf.InteractiveSession() + print("Meta_Graph Imported") + + # parameters save + self.saver.restore(self.sess, opt_addr) + print("Parameters Restored") + + # variables + self.graph=tf.get_default_graph() + self.X=self.graph.get_tensor_by_name('X:0') + self.pred=self.graph.get_tensor_by_name('pred:0') + self.p_keep_conv=self.graph.get_tensor_by_name('p_keep_conv:0') + self.p_keep_hidden=self.graph.get_tensor_by_name('p_keep_hidden:0') + print("Variables Saved") + + def tryit(self, soundaddr): + + import librosa + import numpy as np + from mfcc_processing import five_sec_extract + self.soundaddr=soundaddr + #mfcc processing + mfcc, _=five_sec_extract(self.soundaddr) + #classification result + self.result=self.sess.run(self.pred, feed_dict={self.X: mfcc, self.p_keep_conv: 1.0, self.p_keep_hidden: 1.0}) + print(self.result) diff --git a/x_ksh/stft.py b/x_ksh/stft.py new file mode 100644 index 0000000..da157d5 --- /dev/null +++ b/x_ksh/stft.py @@ -0,0 +1,23 @@ +def five_sec_extract2(file): + import librosa + import numpy as np + #zero padding to file.shape[0] X 17 X 200 + array = np.repeat(0., 17 * 200).reshape(17, 200) + y, sr = librosa.core.load(file, + mono=True, res_type="kaiser_fast") + stft=librosa.core.stft(y,32,16) + mag, pha = librosa.magphase(stft) + length=stft.shape[1] + abs_mag=np.abs(mag) + if length == 200: + array[:, :]=mag + elif length < 200: + array[:, :length]=mag + elif length > 200: + argmax=np.argmax(abs_mag, axis=1) + sample=[] + for i in range(np.max(argmax)): + sample.append(np.sum((argmax>=i) & (argmax Date: Wed, 2 May 2018 21:01:24 +0900 Subject: [PATCH 31/36] all --- PROJECT2/mfcc_cnn_ksh.py | 112 ++++++++++++++++++++++ PROJECT2/mfcc_processing_ksh.py | 119 ++++++++++++++++++++++++ PROJECT2/stft_cnn_ksh.py | 117 +++++++++++++++++++++++ PROJECT2/stft_processing_ksh.py | 159 ++++++++++++++++++++++++++++++++ 4 files changed, 507 insertions(+) create mode 100644 PROJECT2/mfcc_cnn_ksh.py create mode 100644 PROJECT2/mfcc_processing_ksh.py create mode 100644 PROJECT2/stft_cnn_ksh.py create mode 100644 PROJECT2/stft_processing_ksh.py diff --git a/PROJECT2/mfcc_cnn_ksh.py b/PROJECT2/mfcc_cnn_ksh.py new file mode 100644 index 0000000..2692254 --- /dev/null +++ b/PROJECT2/mfcc_cnn_ksh.py @@ -0,0 +1,112 @@ +#mfcc_cnn_ps.py +#librosa.feature.mfcc(n_mfcc=20) 이용해서 저장한 데이터와 CNN을 이용하여 사운드 분류하는 코드입니다. +#데이터 프로세싱 코드는 mfcc_processing.py에 있습니다. + +#모듈 임포트(numpy, tensorflow, math.ceil) 및 랜덤 지정 +import numpy as np +import tensorflow as tf +from math import ceil +tf.set_random_seed(777) + +#트레이닝/테스트 셋 각각 데이터/라벨 임포트 +trainData = np.genfromtxt('/home/paperspace/Downloads/trainData6.csv', delimiter=',') +trainData = trainData.reshape(-1, 20, 430) +testData = np.genfromtxt('/home/paperspace/Downloads/testData6.csv', delimiter=',') +testData = testData.reshape(-1, 20, 430) +trainLabel = np.genfromtxt('/home/paperspace/Downloads/trainLabel6.csv', delimiter=',') +testLabel = np.genfromtxt('/home/paperspace/Downloads/testLabel6.csv', delimiter=',') + +#임포트한 데이터가 원하는 데이터가 맞는지 shape을 통해 확인 +print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape) +# (6631, 20, 430) (2842, 20, 430) (6631,) (2842,) + +#데이터 준비 끝. +#다시 돌릴때는 여기부터 +#그래프 초기화 +tf.reset_default_graph() + +# hyper parameters +learning_rate = 0.0002 +training_epochs = 300 +batch_size = 200 +steps_for_validate = 20 + +#placeholder +X = tf.placeholder(tf.float32, [None, 20, 430], name="X") +X_sound = tf.reshape(X, [-1, 20, 430, 1]) +Y = tf.placeholder(tf.int32, [None, 1], name="Y") +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv") +p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") + +# L1 SoundIn shape=(?, 20, 430, 1) +W1 = tf.get_variable("W1", shape=[2, 43, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME') +L1 = tf.nn.elu(L1) +L1 = tf.layers.batch_normalization(L1) +L1 = tf.nn.max_pool(L1, ksize=[1, 2, 43, 1],strides=[1, 2, 43, 1], padding='SAME') +L1 = tf.nn.dropout(L1, p_keep_conv) + +# L2 Input shape=(?,10,10,32) +W2 = tf.get_variable("W2", shape=[3, 3, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) +L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') +L2 = tf.nn.elu(L2) +L2 = tf.layers.batch_normalization(L2) +L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') +L2 = tf.nn.dropout(L2, p_keep_conv) + +# L3 Input shape=(?,5,5,64) +W3 = tf.get_variable("W3", shape=[3, 3, 64, 128],initializer=tf.contrib.layers.xavier_initializer()) +L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') +L3 = tf.nn.elu(L3) +L3 = tf.layers.batch_normalization(L3) +L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') +L3 = tf.nn.dropout(L3, p_keep_conv) +#L3 shape=(?,3,3,128) +L3_flat= tf.reshape(L3, shape=[-1, 3*3*128]) + +# Final FC 3*3*128 inputs -> 217 -> 41 outputs +W4 = tf.get_variable("W4", shape=[3*3*128, 217],initializer=tf.contrib.layers.xavier_initializer()) +L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) +L4 = tf.layers.batch_normalization(L4) +L4 = tf.nn.dropout(L4, p_keep_hidden) +W_o = tf.get_variable("W_o", shape=[217,41],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L4, W_o) + b +logits = tf.layers.batch_normalization(logits) +#logits shape=(?,41) + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +saver = tf.train.Saver() + +# train my model +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = ceil(len(trainData) / batch_size) + for i in range(total_batch): + batch_xs = trainData[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .8, p_keep_hidden: 0.7} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + if epoch % steps_for_validate == steps_for_validate-1: + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + x1=np.random.choice(testLabel.shape[0], 300, replace=False) + print('TrainAccuracy:', sess.run(accuracy, feed_dict={ + X: trainData[x1], Y: trainLabel[x1].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + x2=np.random.choice(testLabel.shape[0], 300, replace=False) + print('TestAccuracy:', sess.run(accuracy, feed_dict={ + X: testData[x2], Y: testLabel[x2].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') +print('Finished!') + diff --git a/PROJECT2/mfcc_processing_ksh.py b/PROJECT2/mfcc_processing_ksh.py new file mode 100644 index 0000000..efaa073 --- /dev/null +++ b/PROJECT2/mfcc_processing_ksh.py @@ -0,0 +1,119 @@ +#mfcc_processing.py +#librosa.feature.mfcc를 이용해서 mfcc 데이터를 저장하는 코드입니다. + +#필요한 모듈 임포트 +import librosa +import numpy as np +from matplotlib import pyplot as plt +#import labels +import tensorflow as tf +tf.set_random_seed(777) +import pandas as pd +train = pd.read_csv('/Users/kimseunghyuck/desktop/sound_train.csv') +#train = pd.read_csv('/home/paperspace/Downloads/audio_train.csv') + +#train/test, Data/Label split +from sklearn.model_selection import train_test_split +train_set, test_set = train_test_split(train, test_size = 0.3) +trainfile = train_set.values[:,0] +testfile = test_set.values[:,0] +trainLabel = train_set.values[:,1] +testLabel = test_set.values[:,1] + +#data load and extract mfcc (scaling indluded) +path = '/Users/kimseunghyuck/desktop/' +#path = '/home/paperspace/Downloads/audio_train/' + +def see_how_long(file): + c=[] + for filename in file: + y, sr = librosa.core.load(path+'audio_train/'+filename, + mono=True, res_type="kaiser_fast") + mfcc=librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) + abs_mfcc=np.abs(mfcc) + c.append(abs_mfcc.shape[1]) + return(c) + +#n=see_how_long(trainfile) +#print(np.max(n), np.min(n)) #1292, 14 +#n2=see_how_long(testfile) +#print(np.max(n2), np.min(n2)) #1292, 13 + +#show me approximate wave shape +filename= trainfile[11] +y, sr = librosa.core.load(path+'audio_train/'+filename, + mono=True, res_type="kaiser_fast") +mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) +length=mfcc.shape[1] +plt.plot(mfcc[3,]) +plt.plot(np.abs(mfcc[3,])) +plt.plot(mfcc[2,]) +plt.plot(np.abs(mfcc[3,])) + +#5 seconds(430 segments) extract +def five_sec_extract(file): + #zero padding to file.shape[0] X 20 X 430 + n=file.shape[0] + array = np.repeat(0., n * 20 * 430).reshape(n, 20, 430) + k=0 + see = [] + for filename in file: + y, sr = librosa.core.load(path+'audio_train/'+filename, + mono=True, res_type="kaiser_fast") + mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) + length=mfcc.shape[1] + if length == 430: + array[k, :, :]=mfcc + elif length < 430: + array[k, :, :length]=mfcc + elif length > 430: + sample = np.repeat(0., (length - 430)*20).reshape(20,length - 430) + for j in range(length - 430): + for i in range(20): + sample[i,j]=np.var(mfcc[i,j:j+430]) + A=np.argmax(sample, axis=1) + start=np.argmax(np.bincount(A)) + array[k, :, :]=mfcc[:, start:start+430] + see.append(start) + k+=1 + return(array, see) + +trainData, see1=five_sec_extract(trainfile) +testData, see2=five_sec_extract(testfile) +print(see1) +print(see2) + +print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape) +# (6631, 20, 200) (2842, 20, 200) (6631,) (2842,) + +#라벨이 총 몇개가 되어야 하는지 확인 +print(len(np.unique(trainLabel))) #41 +print(len(np.unique(testLabel))) #41 + +#문자열로 되어있는 라벨을 전부 0~40으로 바꾼다. +def Labeling(label): + #idx = np.unique(train.values[:,1]) #이건 abc 순 + idx = train.label.unique() + r=pd.Series(label) + for i in range(len(idx)): + r[r.values==idx[i]]=i + return(r) + +trainLabel=Labeling(trainLabel) +testLabel=Labeling(testLabel) +#라벨이 0~40으로 잘 들어갔는지 확인 +print(min(trainLabel), max(trainLabel), min(testLabel), max(testLabel)) + +#트레이닝 및 테스트에 적절히 사용하기 위해 csv파일로 다운로드한다. +#(3D array는 csv파일로 저장이 안되므로 2D로 변환하여 저장) +trainData2D=trainData.reshape(-1, 20*200) +testData2D=testData.reshape(-1, 20*200) +np.savetxt(path+'trainData6.csv', + trainData2D, delimiter=",") +np.savetxt(path+'testData6.csv', + testData2D, delimiter=",") +np.savetxt(path+'trainLabel6.csv', + trainLabel, delimiter=",") +np.savetxt(path+'testLabel6.csv', + testLabel, delimiter=",") + diff --git a/PROJECT2/stft_cnn_ksh.py b/PROJECT2/stft_cnn_ksh.py new file mode 100644 index 0000000..99acbf9 --- /dev/null +++ b/PROJECT2/stft_cnn_ksh.py @@ -0,0 +1,117 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Apr 26 18:53:10 2018 + +@author: kimseunghyuck +""" + +#import important modules +import numpy as np +import tensorflow as tf +from math import ceil +tf.set_random_seed(777) + +trainData = np.genfromtxt('/home/paperspace/Downloads/trainData7.csv', delimiter=',') +trainData = trainData.reshape(-1, 17, 200) +testData = np.genfromtxt('/home/paperspace/Downloads/testData7.csv', delimiter=',') +testData = testData.reshape(-1, 17, 200) +trainLabel = np.genfromtxt('/home/paperspace/Downloads/trainLabel7.csv', delimiter=',') +testLabel = np.genfromtxt('/home/paperspace/Downloads/testLabel7.csv', delimiter=',') + +print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape) +# (6631, 17, 200) (2842, 17, 200) (6631,) (2842,) + +#다시 돌릴때는 여기부터 +tf.reset_default_graph() #그래프 초기화 + +# hyper parameters +learning_rate = 0.0002 +training_epochs = 300 +batch_size = 100 +steps_for_validate = 20 + +#placeholder +X = tf.placeholder(tf.float32, [None, 17, 200], name="X") +X_sound = tf.reshape(X, [-1, 17, 200, 1]) +Y = tf.placeholder(tf.int32, [None, 1], name="Y") +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv") +p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") + +# L1 SoundIn shape=(?, 20, 430, 1) +W1 = tf.get_variable("W1", shape=[2, 12, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME') +L1 = tf.nn.elu(L1) +L1 = tf.layers.batch_normalization(L1) +L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L1 = tf.nn.dropout(L1, p_keep_conv) + +# L2 Input shape=(?,10,21,32) +W2 = tf.get_variable("W2", shape=[2, 12, 32, 32],initializer=tf.contrib.layers.xavier_initializer()) +L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') +L2 = tf.nn.elu(L2) +L2 = tf.layers.batch_normalization(L2) +L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L2 = tf.nn.dropout(L2, p_keep_conv) + +# L3 Input shape=(?,3,12,64) +W3 = tf.get_variable("W3", shape=[2, 12, 32, 32],initializer=tf.contrib.layers.xavier_initializer()) +L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') +L3 = tf.nn.elu(L3) +L3 = tf.layers.batch_normalization(L3) +L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L3 = tf.nn.dropout(L3, p_keep_conv) + +# L4 Input shape=(?,3,25,32) +W4 = tf.get_variable("W4", shape=[2, 12, 32, 32],initializer=tf.contrib.layers.xavier_initializer()) +L4 = tf.nn.conv2d(L3, W4, strides=[1, 1, 1, 1], padding='SAME') +L4 = tf.nn.elu(L4) +L4 = tf.layers.batch_normalization(L4) +L4 = tf.nn.max_pool(L4, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L4 = tf.nn.dropout(L4, p_keep_conv) +L4_flat= tf.reshape(L4, shape=[-1, 12*32]) + +# Final FC 2*3*128 inputs -> 41 outputs +W5 = tf.get_variable("W5", shape=[12*32, 125],initializer=tf.contrib.layers.xavier_initializer()) +L5 = tf.nn.elu(tf.matmul(L4_flat, W5)) +L5 = tf.layers.batch_normalization(L5) +L5 = tf.nn.dropout(L5, p_keep_hidden) +W_o = tf.get_variable("W_o", shape=[125,41],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L5, W_o) + b +logits = tf.layers.batch_normalization(logits) + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +saver = tf.train.Saver() + +# train my model +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = ceil(len(trainData) / batch_size) + for i in range(total_batch): + batch_xs = trainData[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .8, p_keep_hidden: 0.7} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + if epoch % steps_for_validate == steps_for_validate-1: + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + x1=np.random.choice(testLabel.shape[0], 300, replace=False) + print('TrainAccuracy:', sess.run(accuracy, feed_dict={ + X: trainData[x1], Y: trainLabel[x1].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + x2=np.random.choice(testLabel.shape[0], 300, replace=False) + print('TestAccuracy:', sess.run(accuracy, feed_dict={ + X: testData[x2], Y: testLabel[x2].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + save_path = saver.save(sess, '/home/paperspace/Downloads/optx2/optx2') +print('Finished!') diff --git a/PROJECT2/stft_processing_ksh.py b/PROJECT2/stft_processing_ksh.py new file mode 100644 index 0000000..91fa879 --- /dev/null +++ b/PROJECT2/stft_processing_ksh.py @@ -0,0 +1,159 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Apr 26 18:53:10 2018 + +@author: kimseunghyuck +""" + +import librosa +import numpy as np +from matplotlib import pyplot as plt +#import labels +import tensorflow as tf +tf.set_random_seed(777) +import pandas as pd +train = pd.read_csv('/Users/kimseunghyuck/desktop/sound_train.csv') +#train = pd.read_csv('/home/paperspace/Downloads/audio_train.csv') + +#train/test, Data/Label split +from sklearn.model_selection import train_test_split +train_set, test_set = train_test_split(train, test_size = 0.3) +trainfile = train_set.values[:,0] +testfile = test_set.values[:,0] +trainLabel = train_set.values[:,1] +testLabel = test_set.values[:,1] + +#data load and extract mfcc (scaling indluded) +path = '/Users/kimseunghyuck/desktop/' +#path = '/home/paperspace/Downloads/' + +def see_how_long(file): + c=[] + for filename in file: + y, sr = librosa.core.load(path+filename, mono=True, res_type="kaiser_fast") + stft=librosa.core.stft(y,1024,512) + abs_stft=np.abs(stft) + #1025 X t 형태 + c.append(abs_stft.shape[1]) + return(c) + +#n=see_how_long(trainfile) +#print(np.max(n), np.min(n)) #1292 14 +#n2=see_how_long(testfile) +#print(np.max(n2), np.min(n2)) #1292 13 + +#show me approximate wave shape +filename= trainfile[1470] +y, sr = librosa.core.load(path+filename, mono=True, res_type="kaiser_fast") +stft=librosa.core.stft(y,1024,512) +print(stft.shape) #(513, 1148), (513, 25), (513, 28) +plt.plot(stft[3,]) +plt.plot(stft[2,]) + +filename=trainfile[10] + y, sr = librosa.core.load(path+'audio_train/'+filename, + mono=True, res_type="kaiser_fast") + stft=librosa.core.stft(y,1024,512) + mag, pha = librosa.magphase(stft) +mag, pha.real +plt.plot(mag[10]) +plt.show +plt.plot(pha.real[10]) +plt.show +#magnitude만 해도 될 것 같음. + +""" +# it takes too much time +#5 seconds(100 segments) extract +def five_sec_extract(file): + #zero padding to file.shape[0] X 513 X 100 + n=file.shape[0] + array = np.repeat(0., n * 17 * 200).reshape(n, 17, 200) + k=0 + for filename in file: + y, sr = librosa.core.load(path+'audio_train/'+filename, + mono=True, res_type="kaiser_fast") + stft=librosa.core.stft(y,32,16) + mag, pha = librosa.magphase(stft) + length=stft.shape[1] + if length == 200: + array[k, :, :]=mag + elif length < 200: + array[k, :, :length]=mag + elif length > 200: + sample = np.repeat(0., (length - 200)*17).reshape(17,length - 200) + for j in range(length - 200): + for i in range(17): + sample[i,j]=np.var(mag[i,j:j+200]) + A=np.argmax(sample, axis=1) + start=np.argmax(np.bincount(A)) + array[k, :, :]=mag[:, start:start+200] + k+=1 + return(array) +""" + +def five_sec_extract(file): + #zero padding to file.shape[0] X 17 X 200 + n=file.shape[0] + array = np.repeat(0., n * 17 * 200).reshape(n, 17, 200) + k=0 + for filename in file: + y, sr = librosa.core.load(path+'audio_train/'+filename, + mono=True, res_type="kaiser_fast") + stft=librosa.core.stft(y,32,16) + mag, pha = librosa.magphase(stft) + length=stft.shape[1] + abs_mag=np.abs(mag) + if length == 200: + array[k, :, :]=mag + elif length < 200: + array[k, :, :length]=mag + elif length > 200: + argmax=np.argmax(abs_mag, axis=1) + sample=[] + for i in range(np.max(argmax)): + sample.append(np.sum((argmax>=i) & (argmax integer(0~40) + +def Labeling(label): + #idx = np.unique(train.values[:,1]) #이건 abc 순 + idx = train.label.unique() + r=pd.Series(label) + for i in range(len(idx)): + r[r.values==idx[i]]=i + return(r) + +trainLabel=Labeling(trainLabel) +testLabel=Labeling(testLabel) +print(min(trainLabel), max(trainLabel), min(testLabel), max(testLabel)) +#0 40 0 40 + +#csv downdload totally about 600MB +trainData2D=trainData.reshape(-1, 17*200) +testData2D=testData.reshape(-1, 17*200) +np.savetxt(path+'trainData7.csv', + trainData2D, delimiter=",") +np.savetxt(path+'testData7.csv', + testData2D, delimiter=",") +np.savetxt(path+'trainLabel7.csv', + trainLabel, delimiter=",") +np.savetxt(path+'testLabel7.csv', + testLabel, delimiter=",") + + From 6d68a3f5b0b00f91de2e5c1aae017383f437c313 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EA=B3=BD=EC=83=81=EC=9A=B1?= <37833373+Trigger21@users.noreply.github.com> Date: Wed, 2 May 2018 21:07:55 +0900 Subject: [PATCH 32/36] Create sound_cnn(ksw).py --- PROJECT2/sound_cnn(ksw).py | 201 +++++++++++++++++++++++++++++++++++++ 1 file changed, 201 insertions(+) create mode 100644 PROJECT2/sound_cnn(ksw).py diff --git a/PROJECT2/sound_cnn(ksw).py b/PROJECT2/sound_cnn(ksw).py new file mode 100644 index 0000000..ea59237 --- /dev/null +++ b/PROJECT2/sound_cnn(ksw).py @@ -0,0 +1,201 @@ +import numpy as np +import tensorflow as tf +np.random.seed(1001) +tf.set_random_seed(1001) +import os +import pandas as pd +import seaborn as sns +import librosa + + +# Preparing data + +# *2.03 : 350 + +class Config(object): # mfcc에 대한 데이터 길이 정제 작업 + def __init__(self, + sampling_rate=16000, audio_duration=2, n_classes=41, + use_mfcc=False, n_folds=10, learning_rate=0.0001, + max_epochs=50, n_mfcc=20): + self.sampling_rate = sampling_rate + self.audio_duration = audio_duration + self.n_classes = n_classes + self.use_mfcc = use_mfcc + self.n_mfcc = n_mfcc + self.n_folds = n_folds + self.learning_rate = learning_rate + self.max_epochs = max_epochs + + self.audio_length = self.sampling_rate * self.audio_duration*2.03 + if self.use_mfcc: + self.dim = (self.n_mfcc, 1 + int(np.floor(self.audio_length/512)), 1) + else: + self.dim = (self.audio_length, 1) + +config = Config(sampling_rate=44100, audio_duration=2, n_folds=10, # 주파수와 채널 , mfcc 값 + learning_rate=0.001, use_mfcc=True, n_mfcc=40) + +# 데이터 길이에 대한 패딩 +def prepare_data(df, config, data_dir): + X = np.empty(shape=(df.shape[0], config.dim[0], config.dim[1], 1)) + input_length = int(config.audio_length) + for i, fname in enumerate(df['fname']): + print(fname) + file_path = data_dir + fname + data, _ = librosa.core.load(file_path, sr=config.sampling_rate, res_type="kaiser_fast") + + # Random offset / Padding + if len(data) > input_length: + max_offset = len(data) - input_length + offset = np.random.randint(max_offset) + data = data[offset:(input_length+offset)] + else: + if input_length > len(data): + max_offset = input_length - len(data) + offset = np.random.randint(max_offset) + else: + offset = 0 + data = np.pad(data, (offset, input_length - len(data) - offset), "constant") + + data = librosa.feature.mfcc(data, sr=config.sampling_rate, n_mfcc=config.n_mfcc) + data = np.expand_dims(data, axis=-1) + X[i,] = data + return X + + +train = pd.read_csv("c:/sound/train.csv") +#train = pd.read_csv("/home/itwill03/sound/train.csv") + +# 라벨 숫자화 작업 +labels = train['label'] +l = train['label'].unique() + +df_label = pd.DataFrame(labels) + +for i in range(len(l)): + df_label[df_label==l[i]] = int(i) +df_label.values + +df = pd.concat([train, df_label], axis=1) + +# 데이터를 7 : 3 비율로 나눔 +from sklearn.model_selection import train_test_split +train_set, test_set = train_test_split(df, test_size = 0.3) +trainfile = train_set['fname'] +testfile = test_set['fname'] +trainLabel = train_set.values[:,-1] +testLabel = test_set.values[:,-1] + +# 파일 nfcc 화 +X_train = prepare_data(train_set, config, 'c:/sound/audio_train/') +X_test = prepare_data(test_set, config, 'c:/sound/audio_train/') + +#X_train = prepare_data(train_set, config, '/home/itwill03/sound/audio_train/') +#X_test = prepare_data(test_set, config, '/home/itwill03/sound/audio_train/') + +#Normalization +mean = np.mean(X_train, axis=0) +std = np.std(X_train, axis=0) + +X_train = (X_train - mean)/std +X_test = (X_test - mean)/std + +X_train.shape # (6631, 40, 350, 1) +X_test.shape # (2842, 40, 350, 1) +trainLabel.shape # (6631,) +testLabel.shape # (2842,) + +# start +tf.reset_default_graph() #그래프 초기화 + +# hyper parameters +learning_rate = 0.00008 +training_epochs = 700 +batch_size = 100 +steps_for_validate = 5 + +#placeholder +X = tf.placeholder(tf.float32, [None, 40, 350,1], name="X") # 40*350*1 (frequency, time, amplitude) +Y = tf.placeholder(tf.int32, [None, 1], name="Y") +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv") +p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") + +# L1 SoundIn shape=(?, 40, 350, 1) +W1 = tf.get_variable("W1", shape=[4, 35, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +L1 = tf.nn.conv2d(X, W1, strides=[1, 1, 1, 1], padding='SAME') +L1 = tf.layers.batch_normalization(L1) +#L1 = tf.nn.relu(L1) +#L1 = tf.nn.elu(L1) +L1 = tf.nn.leaky_relu(L1,0.1) +L1 = tf.nn.max_pool(L1, ksize=[1, 4, 35, 1],strides=[1, 4, 35, 1], padding='SAME') +L1 = tf.nn.dropout(L1, p_keep_conv) + +# L2 Input shape=(?,10,10,32) +W2 = tf.get_variable("W2", shape=[3, 3, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) +L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') +L2 = tf.layers.batch_normalization(L2) +#L2 = tf.nn.relu(L2) +#L2 = tf.nn.elu(L2) +L2 = tf.nn.leaky_relu(L2,0.1) +L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') +#L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L2 = tf.nn.dropout(L2, p_keep_conv) + +# L3 Input shape=(?,5,5,64) +W3 = tf.get_variable("W3", shape=[3, 3, 64, 128],initializer=tf.contrib.layers.xavier_initializer()) +L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') +L3 = tf.layers.batch_normalization(L3) +#L3 = tf.nn.relu(L3) +#L3 = tf.nn.elu(L3) +L3 = tf.nn.leaky_relu(L3,0.1) +L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') +#L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L3 = tf.nn.dropout(L3, p_keep_conv) +L3_flat= tf.reshape(L3, shape=[-1, 3*3*128]) + +# Final FC 3*3*128 inputs -> 625 outputs -> 41 outputs +W4 = tf.get_variable("W4", shape=[3*3*128, 625],initializer=tf.contrib.layers.xavier_initializer()) +L3_flat = tf.layers.batch_normalization(L3_flat) +#L4 = tf.nn.relu(tf.matmul(L3_flat, W4)) +#L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) +L4 = tf.nn.leaky_relu(tf.matmul(L3_flat, W4),0.1) +L4 = tf.nn.dropout(L4, p_keep_hidden) +W_o = tf.get_variable("W_o", shape=[625,41],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L4, W_o) + b + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +#saver = tf.train.Saver() + +# train my model +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = int(len(X_train) / batch_size) + for i in range(total_batch): + batch_xs = X_train[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: 0.7, p_keep_hidden: 0.8} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + if epoch % steps_for_validate == steps_for_validate-1: + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + x=np.random.choice(testLabel.shape[0], 500, replace=False) + print('Accuracy:', sess.run(accuracy, feed_dict={ + X: X_test[x], Y: testLabel[x].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + #save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') +print('Finished!') + + +sess.close() + From 090bf8b5fdad2bff1503919805d02c90468ac059 Mon Sep 17 00:00:00 2001 From: suwonyu <37686127+suwonyu@users.noreply.github.com> Date: Thu, 3 May 2018 01:03:59 +0900 Subject: [PATCH 33/36] Delete audio_label.csv --- PROJECT2/audio_label.csv | 9474 -------------------------------------- 1 file changed, 9474 deletions(-) delete mode 100644 PROJECT2/audio_label.csv diff --git a/PROJECT2/audio_label.csv b/PROJECT2/audio_label.csv deleted file mode 100644 index 3827de8..0000000 --- a/PROJECT2/audio_label.csv +++ /dev/null @@ -1,9474 +0,0 @@ -label -0 -1 -2 -3 -4 -4 -5 -6 -7 -8 -0 -9 -10 -11 -4 -12 -12 -13 -14 -12 -15 -16 -0 -17 -18 -10 -16 -19 -3 -20 -21 -14 -4 -22 -23 -24 -25 -23 -7 -24 -7 -26 -11 -27 -28 -25 -4 -29 -29 -30 -0 -31 -0 -31 -19 -32 -30 -26 -25 -29 -18 -21 -11 -25 -30 -25 -13 -27 -6 -15 -19 -14 -9 -23 -28 -22 -5 -29 -8 -33 -27 -31 -12 -21 -32 -9 -0 -0 -5 -14 -33 -34 -7 -31 -35 -29 -4 -9 -6 -18 -16 -13 -11 -7 -17 -29 -4 -32 -25 -7 -16 -36 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-13 -25 -0 -15 -5 -28 -29 -36 -36 -10 -1 -26 -1 -10 -10 -21 -1 -32 -16 -23 -10 -31 -33 -35 -38 -12 -7 -6 -9 -20 -4 -31 -40 -2 -26 -17 -13 -32 -12 -12 -4 -8 -5 -27 -14 -9 -26 -5 -28 -36 -31 -0 -12 -20 -17 From 888685a05ec4c146eafa65b6250f701de136c114 Mon Sep 17 00:00:00 2001 From: suwonyu <37686127+suwonyu@users.noreply.github.com> Date: Thu, 3 May 2018 03:54:41 +0900 Subject: [PATCH 34/36] In function five_sec_extract, smaller size part changed --- PROJECT2/mfcc_processing_ksh.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/PROJECT2/mfcc_processing_ksh.py b/PROJECT2/mfcc_processing_ksh.py index efaa073..d8ceb74 100644 --- a/PROJECT2/mfcc_processing_ksh.py +++ b/PROJECT2/mfcc_processing_ksh.py @@ -65,7 +65,10 @@ def five_sec_extract(file): if length == 430: array[k, :, :]=mfcc elif length < 430: - array[k, :, :length]=mfcc + tile_num = (430//length)+1 + tile_array=np.tile(mfcc,tile_num) + mfcc=tile_array[:,0:430] + array[k, :, :]=mfcc elif length > 430: sample = np.repeat(0., (length - 430)*20).reshape(20,length - 430) for j in range(length - 430): From 7e6a4d8bcca423f4755bd260ded9af497b4a9f8f Mon Sep 17 00:00:00 2001 From: suwonyu <37686127+suwonyu@users.noreply.github.com> Date: Thu, 3 May 2018 04:10:02 +0900 Subject: [PATCH 35/36] Changed for simple code to make frames zero padded --- PROJECT2/mfcc_processing_ksh.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/PROJECT2/mfcc_processing_ksh.py b/PROJECT2/mfcc_processing_ksh.py index d8ceb74..275baed 100644 --- a/PROJECT2/mfcc_processing_ksh.py +++ b/PROJECT2/mfcc_processing_ksh.py @@ -54,7 +54,8 @@ def see_how_long(file): def five_sec_extract(file): #zero padding to file.shape[0] X 20 X 430 n=file.shape[0] - array = np.repeat(0., n * 20 * 430).reshape(n, 20, 430) + array = np.zeros(n, 20, 430) + #array = np.repeat(0., n * 20 * 430).reshape(n, 20, 430) k=0 see = [] for filename in file: @@ -70,7 +71,8 @@ def five_sec_extract(file): mfcc=tile_array[:,0:430] array[k, :, :]=mfcc elif length > 430: - sample = np.repeat(0., (length - 430)*20).reshape(20,length - 430) + sample = np.zeros((20,length-430)) + #sample = np.repeat(0., (length - 430)*20).reshape(20,length - 430) for j in range(length - 430): for i in range(20): sample[i,j]=np.var(mfcc[i,j:j+430]) From 5124b89143ee4344c27eef153aa3798d64cbaf93 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EA=B3=BD=EC=83=81=EC=9A=B1?= <37833373+Trigger21@users.noreply.github.com> Date: Thu, 3 May 2018 09:55:55 +0900 Subject: [PATCH 36/36] =?UTF-8?q?Create=201=EB=B2=88=5F=ED=8C=8C=EC=9D=BC?= =?UTF-8?q?=5F=EC=83=81=EC=9A=B1=EB=B2=84=EC=A0=BC.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...01\354\232\261\353\262\204\354\240\274.py" | 213 ++++++++++++++++++ 1 file changed, 213 insertions(+) create mode 100644 "x_ksw/New Project/1\353\262\210_\355\214\214\354\235\274_\354\203\201\354\232\261\353\262\204\354\240\274.py" diff --git "a/x_ksw/New Project/1\353\262\210_\355\214\214\354\235\274_\354\203\201\354\232\261\353\262\204\354\240\274.py" "b/x_ksw/New Project/1\353\262\210_\355\214\214\354\235\274_\354\203\201\354\232\261\353\262\204\354\240\274.py" new file mode 100644 index 0000000..fa496a8 --- /dev/null +++ "b/x_ksw/New Project/1\353\262\210_\355\214\214\354\235\274_\354\203\201\354\232\261\353\262\204\354\240\274.py" @@ -0,0 +1,213 @@ +import numpy as np +import tensorflow as tf +np.random.seed(1001) +tf.set_random_seed(1001) +import os +import pandas as pd +import seaborn as sns +import librosa + + +# Preparing data + +# *2.03 : 350 + +class Config(object): # mfcc에 대한 데이터 길이 정제 작업 + def __init__(self, + sampling_rate=16000, audio_duration=2, n_classes=41, + use_mfcc=False, n_folds=10, learning_rate=0.0001, + max_epochs=50, n_mfcc=20): + self.sampling_rate = sampling_rate + self.audio_duration = audio_duration + self.n_classes = n_classes + self.use_mfcc = use_mfcc + self.n_mfcc = n_mfcc + self.n_folds = n_folds + self.learning_rate = learning_rate + self.max_epochs = max_epochs + + self.audio_length = self.sampling_rate * self.audio_duration*2.03 + if self.use_mfcc: + self.dim = (self.n_mfcc, 1 + int(np.floor(self.audio_length/512)), 1) + else: + self.dim = (self.audio_length, 1) + +config = Config(sampling_rate=44100, audio_duration=2, n_folds=10, # 주파수와 채널 , mfcc 값 + learning_rate=0.001, use_mfcc=True, n_mfcc=40) + +# 데이터 길이에 대한 패딩 +def prepare_data(df, config, data_dir): + X = np.empty(shape=(df.shape[0], config.dim[0], config.dim[1], 1)) + input_length = int(config.audio_length) + for i, fname in enumerate(df['fname']): + print(fname) + file_path = data_dir + fname + data, _ = librosa.core.load(file_path, sr=config.sampling_rate, res_type="kaiser_fast") + + # Random offset / Padding + if len(data) > input_length: + max_offset = len(data) - input_length + offset = np.random.randint(max_offset) + data = data[offset:(input_length+offset)] + else: + if input_length > len(data): + max_offset = input_length - len(data) + offset = np.random.randint(max_offset) + else: + offset = 0 + data = np.pad(data, (offset, input_length - len(data) - offset), "constant") + + data = librosa.feature.mfcc(data, sr=config.sampling_rate, n_mfcc=config.n_mfcc) + data = np.expand_dims(data, axis=-1) + X[i,] = data + return X + + +train = pd.read_csv("c:/sound/train.csv") +#train = pd.read_csv("/home/itwill03/sound/train.csv") + +# 라벨 숫자화 작업 +labels = train['label'] +l = train['label'].unique() + +df_label = pd.DataFrame(labels) + +for i in range(len(l)): + df_label[df_label==l[i]] = int(i) +df_label.values + +df = pd.concat([train, df_label], axis=1) + +df1 = df[df['manually_verified']==1] + +# 데이터를 7 : 3 비율로 나눔 +from sklearn.model_selection import train_test_split +train_set, test_set = train_test_split(df1, test_size = 0.3) +trainfile = train_set['fname'] +testfile = test_set['fname'] +trainLabel = train_set.values[:,-1] +testLabel = test_set.values[:,-1] + +# 파일 nfcc 화 +X_train = prepare_data(train_set, config, 'c:/sound/audio_train/') +X_test = prepare_data(test_set, config, 'c:/sound/audio_train/') + +#X_train = prepare_data(train_set, config, '/home/itwill03/sound/audio_train/') +#X_test = prepare_data(test_set, config, '/home/itwill03/sound/audio_train/') + +#Normalization +mean = np.mean(X_train, axis=0) +std = np.std(X_train, axis=0) + +X_train = (X_train - mean)/std +X_test = (X_test - mean)/std + +X_train.shape # (6631, 40, 350, 1) +X_test.shape # (2842, 40, 350, 1) +trainLabel.shape # (6631,) +testLabel.shape # (2842,) + +# start +tf.reset_default_graph() #그래프 초기화 + +# hyper parameters +learning_rate = 0.00008 +training_epochs = 700 +batch_size = 100 +steps_for_validate = 5 + +#placeholder +X = tf.placeholder(tf.float32, [None, 40, 350,1], name="X") # 40*350*1 (frequency, time, amplitude) +Y = tf.placeholder(tf.int32, [None, 1], name="Y") +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv") +p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") + +# L1 SoundIn shape=(?, 40, 350, 1) +W1 = tf.get_variable("W1", shape=[4, 35, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +L1 = tf.nn.conv2d(X, W1, strides=[1, 1, 1, 1], padding='SAME') +L1 = tf.layers.batch_normalization(L1) +#L1 = tf.nn.relu(L1) +#L1 = tf.nn.elu(L1) +L1 = tf.nn.leaky_relu(L1,0.1) +L1 = tf.nn.max_pool(L1, ksize=[1, 4, 35, 1],strides=[1, 4, 35, 1], padding='SAME') +L1 = tf.nn.dropout(L1, p_keep_conv) + +# L2 Input shape=(?,10,10,32) +W2 = tf.get_variable("W2", shape=[3, 3, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) +L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') +L2 = tf.layers.batch_normalization(L2) +#L2 = tf.nn.relu(L2) +#L2 = tf.nn.elu(L2) +L2 = tf.nn.leaky_relu(L2,0.1) +L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') +#L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L2 = tf.nn.dropout(L2, p_keep_conv) + +# L3 Input shape=(?,5,5,64) +W3 = tf.get_variable("W3", shape=[3, 3, 64, 128],initializer=tf.contrib.layers.xavier_initializer()) +L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') +L3 = tf.layers.batch_normalization(L3) +#L3 = tf.nn.relu(L3) +#L3 = tf.nn.elu(L3) +L3 = tf.nn.leaky_relu(L3,0.1) +L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') +#L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='VALID') +L3 = tf.nn.dropout(L3, p_keep_conv) +L3_flat= tf.reshape(L3, shape=[-1, 3*3*128]) + +# Final FC 2*2*128 inputs -> 41 outputs +W4 = tf.get_variable("W4", shape=[3*3*128, 625],initializer=tf.contrib.layers.xavier_initializer()) +L3_flat = tf.layers.batch_normalization(L3_flat) +#L4 = tf.nn.relu(tf.matmul(L3_flat, W4)) +#L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) +L4 = tf.nn.leaky_relu(tf.matmul(L3_flat, W4),0.1) +L4 = tf.nn.dropout(L4, p_keep_hidden) +W_o = tf.get_variable("W_o", shape=[625,41],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L4, W_o) + b + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +#saver = tf.train.Saver() + +# train my model +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = int(len(X_train) / batch_size) + for i in range(total_batch): + batch_xs = X_train[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: 0.7, p_keep_hidden: 0.8} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + if epoch % steps_for_validate == steps_for_validate-1: + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + x=np.random.choice(testLabel.shape[0], 500, replace=False) + print('Accuracy:', sess.run(accuracy, feed_dict={ + X: X_test[x], Y: testLabel[x].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + #save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') +print('Finished!') + + +sess.close() + +""" +1) con2d layer * 3 + FC +lr=0.00008, epoch = 700 +p_keep_conv, p_keep_hidden = 0.7, 0.8 +win : (4, 35, 32), (3, 3, 64), (3, 3, 128) +FC : (3*3*128, 625), (625, 41) +max_pool : (4,35), (2,2), (2,2) +accuracy: 69~73% + +"""