from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D

classifier = Sequential()

classifier.add(Convolution2D(32,3,3,input_shape=(64,64,3),activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2,2)))
classifier.add(Flatten())
classifier.add(Dense(output_dim = 128,activation = 'relu' ))
classifier.add(Dense(output_dim = 1 ,activation = 'sigmoid'))
classifier.compile(optimizer = 'adam',loss='binary_crossentropy',metrics = ['accuracy'])
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range = 0.2,
        zoom_range = 0.2,
        horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory(
            'dogscats/train',
        target_size= (64,64),
        batch_size=32,
        shuffle = True,
        class_mode = 'binary')
test_set = test_datagen.flow_from_directory(
              'dogscats/test1',
        target_size= (64,64),
        batch_size=32,
        shuffle = True,
        class_mode = 'binary')
from IPython.display import display
from PIL import Image

classifier.fit_generator(
        training_set,
        steps_per_epoch=8000,
        epochs=10,
        validation_data=test_set,
        validation_steps=800)