model.py

from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape = (50, 50, 3), activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Conv2D(32, (3, 3), activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Flatten())
model.add(Dense(units = 128, activation = 'relu'))
model.add(Dense(units = 1, activation = 'sigmoid'))

model.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)

training_set = train_datagen.flow_from_directory('C:/Users/Shray Tyagi/Downloads/cat-and-dog/training_set/training_set',
                                                 target_size = (50, 50),
                                                 batch_size = 25,
                                                 class_mode = 'binary')

model.fit_generator(training_set,
                         steps_per_epoch = 1000,
                         epochs = 10,)

model.save('model.h6')