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selfback_cnn.py
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selfback_cnn.py
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"""
A Convolutional Network for HAR based on : rgu-selfback
Author: Maxime Golfier
"""
from __future__ import print_function
from math import *
from scipy import stats
from tensorflow.python.tools import freeze_graph
from tensorflow.python.tools import optimize_for_inference_lib
import tensorflow as tf
import numpy as np
import pandas as pd
import random
import copy
import csv
####PART FOR DATA####
def create_data(directory):
list = []
column_names = ['x-axis', 'y-axis', 'z-axis','activity']
for i in range(1,6):
name = directory+str(i)+'.csv'
data = pd.read_csv(name, header=1, names=column_names)
list.append(data)
print(list)
print('create_data is done')
return list
def read_csv(numlines,path):
filename_queue = tf.train.string_input_producer([path])
reader = tf.TextLineReader(skip_header_lines=1)
key, value = reader.read(filename_queue)
record_defaults = [tf.constant([0], dtype=tf.float32), # Column 1
tf.constant([0], dtype=tf.float32), # Column 2
tf.constant([0], dtype=tf.float32), # Column 3
tf.constant([], dtype=tf.int32)] # Column 4
col1, col2, col3, col4 = tf.decode_csv(value, record_defaults=record_defaults)
features = tf.stack([col1, col2, col3])
with tf.Session() as sess:
# Start populating the filename queue.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for i in range(numlines):
# Retrieve a single instance:
example, label = sess.run([features, col4])
#print(example, label)
coord.request_stop()
coord.join(threads)
def count_lines(path):
with open(path,"r") as f:
reader = csv.reader(f,delimiter = ",")
data = list(reader)
row_count = len(data)
return int(row_count)
def read_one_csv(name):
data = np.genfromtxt(name, delimiter=',', skip_header=1)
return data
def one_hot_encoded(class_numbers, num_classes=None):
"""
Generate the One-Hot encoded class-labels from an array of integers.
For example, if class_number=2 and num_classes=4 then
the one-hot encoded label is the float array: [0. 0. 1. 0.]
:param class_numbers:
Array of integers with class-numbers.
Assume the integers are from zero to num_classes-1 inclusive.
:param num_classes:
Number of classes. If None then use max(class_numbers)+1.
:return:
2-dim array of shape: [len(class_numbers), num_classes]
"""
# Find the number of classes if None is provided.
# Assumes the lowest class-number is zero.
if num_classes is None:
num_classes = np.max(class_numbers) + 1
return np.eye(num_classes, dtype=float)[class_numbers]
def read_all_csv(directory,X,Y):
all_data = []
all_label = []
all_lines_per_file = []
for i in range(1,7):
name = directory+str(i)+'.csv'
data = read_one_csv(name)
#data = data.astype(np.float32)
label = one_hot_encoded(i-1,6)
line = count_lines(name)
all_data.append(data)
all_label.append(label)
all_lines_per_file.append(line)
#print(all_data)
#print(all_label)[
#stuff = [[all_dataj], all_label[j]] for j in range(len(al# l_data))]
res1, res2 = format_data_label(all_data,all_label,all_lines_per_file)
print('read_all_csv is done :)')
return res1, res2
def input_pipeline(batch_size, example_list):
min_after_dequeue = 10000
capacity = min_after_dequeue + 3 * batch_size
example_batch, label_batch = tf.train.shuffle_batch_join(
example_list, batch_size=batch_size, capacity=capacity,
min_after_dequeue=min_after_dequeue)
return example_batch, label_batch
def format_data_label(all_data, all_label,all_line):
res1 = []
res2 = []
for i in range(6):
nb_sections = all_line[i]/500
nb_sections = int(floor(nb_sections))
nb_elmts_to_delete = all_line[i]%500
tab = all_data[i][:nb_elmts_to_delete-1]
newSection = np.array_split(tab, nb_sections)
for j in range(nb_sections):
label = copy.copy(all_label[i])
res1.append(newSection)
res2.append(500*label)
return res1, res2
def read_data(file_path):
column_names = ['x-axis', 'y-axis', 'z-axis', 'activity']
data = pd.read_csv(file_path, header=1, names=column_names)
return data
def create_datasets (data, lines,length=500):
ds = np.empty((0,length,3))
labels = np.empty((0))
nb_sections = lines/500
nb_sections = int(floor(nb_sections))
for i in range(nb_sections):
stop = length*(i+1)
start = stop - 500
x = data["x-axis"][start:stop]
y = data["y-axis"][start:stop]
z = data["z-axis"][start:stop]
ds = np.vstack([ds, np.dstack([x, y, z])])
labels = np.append(labels, stats.mode(data["activity"][start:stop])[0][0])
return ds, labels
####PART FOR MODEL####
def conv2d(x, W, b, strides=1):
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x)
def maxpool2d(x, k=2):
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],
padding='SAME')
def conv_net(x, weights, biases, keep_prob):
# Convolution Layer 1
conv1 = conv2d(x, weights['wc1'], biases['bc1'])
# Max Pooling (down-sampling)
conv1 = maxpool2d(conv1)
# Convolution Layer 2
conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
# Max Pooling (down-sampling)
conv2 = maxpool2d(conv2)
# Convolution Layer 3
conv3 = conv2d(conv2, weights['wc3'], biases['bc3'])
# Max Pooling (down-sampling)
conv3 = maxpool2d(conv3)
# Convolution Layer 4
conv4 = conv2d(conv3, weights['wc4'], biases['bc4'])
# Max Pooling (down-sampling)
conv4 = maxpool2d(conv4)
# Convolution Layer 5
conv5 = conv2d(conv4, weights['wc5'], biases['bc5'])
# Max Pooling (down-sampling)
conv5 = maxpool2d(conv5)
# Fully connected layer 1
# Reshape conv5 output to fit fully connected layer input
fc1 = tf.reshape(conv5, [-1, weights['wd1'].get_shape().as_list()[0]])
dense1 = tf.layers.dense(inputs=fc1, units=900, activation=tf.nn.tanh)
# Fully connected layer 2
dense2 = tf.layers.dense(dense1, units=300, activation=tf.nn.tanh)
dropout = tf.nn.dropout(dense2, keep_prob)
# Logits Layer
logits = tf.layers.dense(inputs=dropout, units=6)
# Output, class prediction
out = tf.nn.softmax(logits, name='output')
return out
def placeholder_input(input_height, input_width, num_channels, num_class):
x = tf.placeholder(tf.float32, [None, input_height, input_width, num_channels], name='input')
y = tf.placeholder(tf.float32, [None, num_class])
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
return x, y, keep_prob
def export_model(input_node_names, output_node_name , model_name):
freeze_graph.freeze_graph('out/' + model_name + '.pbtxt', None, False,
'out/' + model_name + '.chkp', output_node_name, "save/restore_all",
"save/Const:0", 'out/frozen_' + model_name + '.pb', True, "")
input_graph_def = tf.GraphDef()
with tf.gfile.Open('out/frozen_' + model_name + '.pb', "rb") as f:
input_graph_def.ParseFromString(f.read())
output_graph_def = optimize_for_inference_lib.optimize_for_inference(
input_graph_def, input_node_names, [output_node_name],
tf.float32.as_datatype_enum)
with tf.gfile.FastGFile('out/opt_' + model_name + '.pb', "wb") as f:
f.write(output_graph_def.SerializeToString())
print("Graph saved!")
########################################
##################MAIN##################
########################################
# Parameters
file = 'data/allDataLight.csv'
model_name = 'cnn_wrist500_tf'
training_epochs = 500
learning_rate = 0.01
n_input = 3
n_height = 1
n_width = 500
n_channels = 3
n_classes = 6
batch_size = 500
weights = {
# 1x10 conv, 3 input, 150 outputs
'wc1': tf.Variable(tf.random_normal([1, 10, 3, 150])),
# 1x10 conv, 150 input, 100 outputs
'wc2': tf.Variable(tf.random_normal([1, 10, 150, 100])),
# 1x10 conv, 100 input, 80 outputs
'wc3': tf.Variable(tf.random_normal([1, 10, 100, 80])),
# 1x10 conv, 80 input, 60 outputs
'wc4': tf.Variable(tf.random_normal([1, 10, 80, 60])),
# 1x10 conv, 60 input, 40 outputs
'wc5': tf.Variable(tf.random_normal([1, 10, 60, 40])),
# fully connected, 500/2^5 => 15.625 inputs, 900 outputs
'wd1': tf.Variable(tf.random_normal([16*1*40, 900]))
}
biases = {
'bc1': tf.Variable(tf.random_normal([150])),
'bc2': tf.Variable(tf.random_normal([100])),
'bc3': tf.Variable(tf.random_normal([80])),
'bc4': tf.Variable(tf.random_normal([60])),
'bc5': tf.Variable(tf.random_normal([40]))
}
X, Y, keep_prob = placeholder_input(n_height, n_width, n_channels, n_classes)
# Read Data
dataset = read_data(file)
numlines = count_lines(file)
# Create datasets from the file
data, labels = create_datasets(dataset, numlines)
# Reshape data
labels = np.asarray(pd.get_dummies(labels), dtype=np.int8)
reshaped_data = data.reshape(len(data), n_height, n_width, n_channels)
# Split data to test it
train_test_split = np.random.rand(len(reshaped_data)) < 0.85
# Create train et test data
train_x = reshaped_data[train_test_split]
train_y = labels[train_test_split]
test_x = reshaped_data[~train_test_split]
test_y = labels[~train_test_split]
# Shuffle data
r1 = random.random()
r2 = random.random()
random.shuffle(train_x, lambda: r1)
random.shuffle(train_y, lambda: r1)
random.shuffle(test_x, lambda: r2)
random.shuffle(test_y, lambda: r2)
# Construct model
pred = conv_net(X, weights, biases, keep_prob)
# Define loss and optimizer
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=Y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)
# Evaluate model
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Initializing the variables
init = tf.global_variables_initializer()
# In order to save model
saver = tf.train.Saver()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
tf.train.write_graph(sess.graph_def, 'out',
model_name + '.pbtxt', True)
# Keep training until reach max iterations
for step in range(training_epochs):
offset = (step * batch_size) % (train_y.shape[0] - batch_size)
batch_data = train_x[offset:(offset + batch_size), :]
batch_labels = train_y[offset:(offset + batch_size)]
# Make evaluation of the accuracy each 5 epochs
if step % 5 == 0:
train_accuracy = accuracy.eval(feed_dict={
X: batch_data, Y: batch_labels, keep_prob: 1.0})
print('step %d, training accuracy %f' % (step, train_accuracy))
_, summary = sess.run([optimizer, loss], feed_dict={X: batch_data, Y: batch_labels, keep_prob: 0.5})
print(str(step), ' epoch(s) completed')
saver.save(sess, 'out/' + model_name + '.chkp')
print("Optimization Finished!")
print("Testing Accuracy:", sess.run(accuracy, feed_dict={X: test_x, Y: test_y, keep_prob: 1.0}))
export_model(["input", "keep_prob"], "output", model_name)