cnn_catdog_normal.py

from datetime import datetime
import sys
from matplotlib import pyplot
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Dense
from keras.layers import Flatten
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataGenerator
 
# define cnn model
def define_model():
	model = Sequential()
	model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(200, 200, 3)))
	model.add(MaxPooling2D((2, 2)))
	model.add(Flatten())
	model.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))
	model.add(Dense(1, activation='sigmoid'))
	# compile model
	opt = SGD(lr=0.001, momentum=0.9)
	model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])
	return model
 
# plot diagnostic learning curves
def summarize_diagnostics(history):
	# plot loss
	pyplot.subplot(211)
	pyplot.title('Cross Entropy Loss')
	pyplot.plot(history.history['loss'], color='blue', label='train')
	pyplot.plot(history.history['val_loss'], color='orange', label='test')
	# plot accuracy
	pyplot.subplot(212)
	pyplot.title('Classification Accuracy')
	pyplot.plot(history.history['accuracy'], color='blue', label='train')
	pyplot.plot(history.history['val_accuracy'], color='orange', label='test')
	# save plot to file
	filename = sys.argv[0].split('/')[-1]
	pyplot.savefig(filename + '_plot.png')
	pyplot.close()
 
# run the test harness for evaluating a model
def run_test_harness():
	# define model
    model = define_model()
    print(model.summary())
    model.save('cnnCatDog.h5')
	# create data generator
    datagen = ImageDataGenerator(rescale=1.0/255.0)
	# prepare iterators
    train_it = datagen.flow_from_directory('dataset_dogs_vs_cats/train/',
		class_mode='binary', batch_size=64, target_size=(200, 200))
    test_it = datagen.flow_from_directory('dataset_dogs_vs_cats/test/',
		class_mode='binary', batch_size=64, target_size=(200, 200))
	# fit model
    history = model.fit(train_it, steps_per_epoch=len(train_it),
		validation_data=test_it, validation_steps=len(test_it), epochs=20, verbose=0)
	# evaluate model
    _, acc = model.evaluate(test_it, steps=len(test_it), verbose=0)
    print('> %.3f' % (acc * 100.0))
	# learning curves
    summarize_diagnostics(history)
 
# start time of execution
startTime = datetime.now()

# entry point, run the test harness
run_test_harness()

print("execution time:", datetime.now()-startTime)