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utils.py
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utils.py
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from tensorflow import keras
import numpy as np
import yaml
import math
import os
import matplotlib.pyplot as plt
#load model
from keras.models import load_model
def download_data(link="http://www.cs.toronto.edu/~nitish/unsupervised_video/mnist_test_seq.npy",normalize=True,number_of_data=1000,split_ratio=0.8):
"""
Downloads the data from the link provided.
:param link: The link to download the data from.
:return: The data.
"""
dataset=keras.utils.get_file("mnist_test_seq.npy", link)
dataset=np.load(dataset)
dataset = np.swapaxes(dataset, 0, 1) # separet all the frame(single image) in the sequence of frams(multiple images)
dataset=dataset[:number_of_data,...]
dataset = np.expand_dims(dataset, axis=-1) # Add the channel dimension. as it is a grayscale image.
indexes = np.arange(dataset.shape[0])
np.random.shuffle(indexes)
train_index = indexes[: int(split_ratio * dataset.shape[0])]
val_index = indexes[int(split_ratio * dataset.shape[0]) :]
train_dataset = dataset[train_index]
val_dataset = dataset[val_index]
if normalize:
train_dataset = train_dataset / 255
val_dataset = val_dataset / 255
return train_dataset,val_dataset
#(800, 20, 64, 64, 1)
#(200, 20, 64, 64, 1)
def read_yaml(path='config.yaml'):
"""
Reads the yaml file and returns the data in a dictionary.
:param path: The path to the yaml file.
:return: The data in the yaml file.
"""
with open(path, 'r') as stream:
data_loaded = yaml.load(stream, Loader=yaml.FullLoader)
return data_loaded
class SelectCallbacks(keras.callbacks.Callback):
def __init__(self,config= read_yaml()):
"""
Summary:
callback class for validation prediction and create the necessary callbacks objects
Arguments:
val_dataset (object): MyDataset class object
model (object): keras.Model object
config (dict): configuration dictionary
Return:
class object
"""
super(keras.callbacks.Callback, self).__init__()
self.config = config
self.callbacks = []
def lr_scheduler(self, epoch):
"""
Summary:
learning rate decrease according to the model performance
Arguments:
epoch (int): current epoch
Return:
learning rate
"""
drop = 0.5
epoch_drop = self.config['epochs'] / 8.
lr = self.config['learning_rate'] * math.pow(drop, math.floor((1 + epoch) / epoch_drop))
return lr
def get_callbacks(self):
"""
Summary:
creating callbacks based on configuration
Arguments:
val_dataset (object): MyDataset class object
model (object): keras.Model class object
Return:
list of callbacks
"""
if self.config['csv']:
self.callbacks.append(keras.callbacks.CSVLogger(os.path.join(self.config['csv_log_dir'], self.config['csv_log_name']), separator = ",", append = False))
if self.config['checkpoint']:
self.callbacks.append(keras.callbacks.ModelCheckpoint(filepath=self.config['checkpoint_dir']+"next_frame_prediction.hdf5", save_best_only = True))
if self.config['lr']:
self.callbacks.append(keras.callbacks.LearningRateScheduler(schedule = self.lr_scheduler))
return self.callbacks
def plot_loss(history):
"""
Summary:
plot the loss function
Arguments:
history (object): keras.callbacks.History object
Return:
None
"""
plt.plot(history.history["accuracy"])
plt.plot(history.history["val_accuracy"])
plt.title("model accuracy")
plt.ylabel("accuracy")
plt.xlabel("epoch")
plt.legend(["train","test"],loc="upper left")
plt.show()
plt.savefig("data\prediction\accuracy.png")
def predictions(model_dir,datasets):
"""
arg: model_dir --> str , contain the pathe of model dataset
arg: datasets --> list , contain the dataset for prediction
return: list of predictions and polt it
"""
model=load_model(model_dir)
fig ,axis= plt.subplot(2,5)
rand=np.random.randint(0,14)
for i in range(2):
for j in range(5):
rand_frame=datasets[0,rand:,...]
img=rand_frame[:,:,0]
img=np.expand_dims(rand_frame+j,axis=0)
pred=model.predict(img)
prediction=pred[0:0,:,:,0]
axis[i][j].plt.imshow(prediction, cmap='gray')