model training and saving.py

#download the dataset
'''
from pygoogle_image import image as pi
pi.download("su 30 mki", limit = 100)
'''


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
from keras.preprocessing.image import ImageDataGenerator


model = Sequential()
model.add(Conv2D(32, (3,3), input_shape=(64, 64, 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'])


train_data = ImageDataGenerator(rescale=1/255,
                                shear_range=0.2,
                                zoom_range=0.2,
                                horizontal_flip=True)
val_data = ImageDataGenerator(rescale=1/255)


training_set = train_data.flow_from_directory("Dataset/train",
                                              target_size=(64, 64),
                                              batch_size=8,
                                              class_mode= 'binary')
val_set = val_data.flow_from_directory("Dataset/vals",
                                       target_size=(64, 64),
                                       batch_size=8,
                                       class_mode= 'binary')


model.fit_generator(training_set,
                    steps_per_epoch= 10,
                    epochs=100,
                    validation_data=val_set,
                    validation_steps=2)


model_json = model.to_json()
with open("model.json","w") as file:
    file.write(model_json)
model.save_weights("model.h5")