train.py

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
no_train_img,no_validation_img = 1001,800
img_width, img_height = 150, 150
batch_size = 16
if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)#theano
else:
    input_shape = (img_width, img_height, 3)#tensorflow

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(150, 150,3)))
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'])

train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
        '/DATA/TRAIN',  
        target_size=(150, 150), 
        batch_size=batch_size,
        class_mode='binary') 




validation_generator = test_datagen.flow_from_directory(
        'DATA/VALIDATION',
        target_size=(150, 150),
        batch_size=batch_size,
        class_mode='binary')

classes = train_generator.class_indices
print ("classes------------------>>>>>>>",classes)

model.summary()

model.fit_generator(
        train_generator,
        steps_per_epoch= no_train_img // batch_size,
        epochs=50,
        validation_data= validation_generator,
        validation_steps= no_validation_img // batch_size)
model.save_weights('huge_data_first_try.h5')