import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.layers import ZeroPadding2D
from keras.layers import BatchNormalization
from tensorflow.keras.preprocessing.image import ImageDataGenerator

model = Sequential()

model.add(Conv2D(64, (9, 9), input_shape=(100,100,3),padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(128, (5, 5), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(256, (3, 3), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))

model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))


model.add(Flatten())
model.add(Dense(64))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.5))

model.add(Dense(128))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.5))

model.add(Dense(1))
model.add(BatchNormalization())
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   rotation_range=30,
                                   shear_range=0.2,
                                   zoom_range=[0.8, 1.2],
                                    horizontal_flip=True,
#                                     vertical_flip = True,
                                    fill_mode='nearest')

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('/content/data/train',
                                                 target_size = (100, 100),
                                                 batch_size = 64,
                                                 class_mode = 'binary')

test_set = test_datagen.flow_from_directory('/content/data/val',
                                            target_size = (100, 100),
                                            batch_size = 64,
                                            class_mode = 'binary')
my_callbacks = [
    # tf.keras.callbacks.EarlyStopping(patience=4, verbose=1),
    tf.keras.callbacks.ReduceLROnPlateau(factor=0.1, patience=3, min_lr=0.00001, verbose=1),
    tf.keras.callbacks.ModelCheckpoint('my_model2.h5',
    verbose=1, save_best_only=True, save_weights_only=False)
    ]

model.fit(training_set, epochs=1000, validation_data = test_set, callbacks=my_callbacks)

model.save('my_model2.h5')