Day #302 - Keras Best Practices during Training

In this post we take the raw version of code and add below features in code

• Adding Checkpoint
• Adding Logging
• Plot Results
• Restart Training from Checkpoint
• Early Stopping

	#Base code - https://keras.io/examples/mnist_cnn/
	#Added Features
	#========================
	# Adding Checkpoint
	# Adding Logging
	# Plot Results
	# Restart Training from Checkpoint
	from __future__ import print_function
	import keras
	from keras.datasets import mnist
	from keras.models import Sequential
	from keras.layers import Dense, Dropout, Flatten
	from keras.layers import Conv2D, MaxPooling2D
	from keras import backend as K
	from keras.callbacks import ModelCheckpoint, CSVLogger, EarlyStopping
	import os
	batch_size = 128
	num_classes = 10
	epochs = 5
	log_file_path = r'E:\Landmark\mnist_training_log.log'
	model_checkpoint_path = r"E:\Landmark\\mnist.h5"
	model_save_path = r"E:\Landmark\\mnist.h5"
	weights_filepath="E:\\Landmark\\mnist-weights-improvement-{epoch:02d}.hdf5"
	# input image dimensions
	img_rows, img_cols = 28, 28
	# the data, split between train and test sets
	(x_train, y_train), (x_test, y_test) = mnist.load_data()
	if K.image_data_format() == 'channels_first':
	x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
	x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
	input_shape = (1, img_rows, img_cols)
	else:
	x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
	x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
	input_shape = (img_rows, img_cols, 1)
	x_train = x_train.astype('float32')
	x_test = x_test.astype('float32')
	x_train /= 255
	x_test /= 255
	print('x_train shape:', x_train.shape)
	print(x_train.shape[0], 'train samples')
	print(x_test.shape[0], 'test samples')
	# convert class vectors to binary class matrices
	y_train = keras.utils.to_categorical(y_train, num_classes)
	y_test = keras.utils.to_categorical(y_test, num_classes)
	from keras.models import load_model
	#Load and Continue Training
	# load weights if it exists
	if os.path.exists(model_checkpoint_path):
	print('Loading Definitions')
	model = load_model(model_checkpoint_path)
	else:
	model = Sequential()
	model.add(Conv2D(32, kernel_size=(3, 3),
	activation='relu',
	input_shape=input_shape))
	model.add(Conv2D(64, (3, 3), activation='relu'))
	model.add(MaxPooling2D(pool_size=(2, 2)))
	model.add(Dropout(0.25))
	model.add(Flatten())
	model.add(Dense(128, activation='relu'))
	model.add(Dropout(0.5))
	model.add(Dense(num_classes, activation='softmax'))
	model.compile(loss=keras.losses.categorical_crossentropy,
	optimizer=keras.optimizers.Adadelta(),
	metrics=['accuracy'])
	#Add Early Stop and Checkpoint
	early_stop = EarlyStopping(monitor='val_loss', patience=5, verbose=1)
	checkpoint = ModelCheckpoint(weights_filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='auto')
	csv_logger = CSVLogger(log_file_path, append=False)
	callbacks_list = [checkpoint,early_stop,csv_logger]
	model.fit(x_train, y_train,
	batch_size=batch_size,
	epochs=epochs,
	verbose=1,
	validation_data=(x_test, y_test), callbacks=callbacks_list)
	score = model.evaluate(x_test, y_test, verbose=0)
	print('Test loss:', score[0])
	print('Test accuracy:', score[1])
	model.save(model_save_path)
	import pandas as pd
	import matplotlib.pyplot as plt
	# Plot the Loss
	file_name = log_file_path
	df = pd.DataFrame.from_csv(file_name)
	print(df.head())
	training_loss = df['loss']
	test_loss = df['val_loss']
	print(training_loss)
	print(test_loss)
	epoch_count = range(1, len(training_loss) + 1)
	plt.plot(epoch_count, training_loss, 'r--')
	plt.plot(epoch_count, test_loss, 'b-')
	plt.legend(['Training Loss', 'Test Loss'])
	plt.xlabel('Epoch')
	plt.ylabel('Loss')
	plt.show();

view raw Keras_Best_Practices.py hosted with ❤ by GitHub

Run #1 Output (10 Epochs)

Run #2 Continue with Existing Weights (5 Epochs)

Happy Learning!!!