trainModel.py

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.callbacks import ModelCheckpoint
from keras.utils import np_utils
from keras.optimizers import RMSprop

import DataHelper

X, y = DataHelper.imagesToNdarray("To_Train_Data/", binary = 'y')

#Uncomment in case of grayscale images
#X = X.reshape(X.shape + (1,))

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(64, 64,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

'''
filepath="new-model-updated-weights-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
'''

# fit the model
model.fit(X, y, epochs=2, batch_size=12, shuffle = True)

model.save("trainedModel.hdf5")