cat and dog1

from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D
from keras.layers import Dense, Activation, Dropout, Flatten
from keras import optimizers
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
import numpy as np 

# step 1: load data

img_width = 150
img_height = 150
train_data_dir = 'data/train'
valid_data_dir = 'data/validation'

datagen = ImageDataGenerator(rescale = 1./255)

train_generator = datagen.flow_from_directory(directory=train_data_dir,
											   target_size=(img_width,img_height),
											   classes=['dogs','cats'],
											   class_mode='binary',
											   batch_size=16)

validation_generator = datagen.flow_from_directory(directory=valid_data_dir,
											   target_size=(img_width,img_height),
											   classes=['dogs','cats'],
											   class_mode='binary',
											   batch_size=32)


# step-2 : build model

model =Sequential()

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

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

model.add(Conv2D(64,(3,3), input_shape=(img_width, img_height, 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'])

print('model complied!!')

print('starting training....')
training = model.fit_generator(generator=train_generator, steps_per_epoch=2048 // 16,epochs=20,validation_data=validation_generator,validation_steps=832//16)

print('training finished!!')

print('saving weights to simple_CNN.h5')

model.save_weights('models/simple_CNN.h5')

print('all weights saved successfully !!')
#models.load_weights('models/simple_CNN.h5')