[TensorFlow Cert] Q3-CNN, VGG16

import tensorflow as tf
import urllib
import zipfile
from tensorflow.keras.preprocessing.image import ImageDataGenerator

from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dropout, Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.applications import VGG16

def solution_model():
    _TRAIN_URL = "https://storage.googleapis.com/download.tensorflow.org/data/horse-or-human.zip"
    _TEST_URL = "https://storage.googleapis.com/download.tensorflow.org/data/validation-horse-or-human.zip"
    
    urllib.request.urlretrieve(_TRAIN_URL, 'horse-or-human.zip')
    local_zip = 'horse-or-human.zip'
    zip_ref = zipfile.ZipFile(local_zip, 'r')
    zip_ref.extractall('tmp/horse-or-human/')
    zip_ref.close()

    urllib.request.urlretrieve(_TEST_URL, 'validation-horse-or-human.zip')
    local_zip = 'validation-horse-or-human.zip'
    zip_ref = zipfile.ZipFile(local_zip, 'r')
    zip_ref.extractall('tmp/validation-horse-or-human/')
    zip_ref.close()

    TRAINING_DIR = 'tmp/horse-or-human/'
    VALIDATION_DIR = 'tmp/validation-horse-or-human/'

    train_datagen = ImageDataGenerator(
        rescale = 1 / 255.0,
        rotation_range = 5,
        width_shift_range = 0.05,
        height_shift_range = 0.05,
        # shear_range = 0.2,
        zoom_range = 0.05,
        horizontal_flip = True,
        fill_mode = 'nearest',
        # validation_split = 0.2
    )

    validation_datagen = ImageDataGenerator(
        rescale = 1 / 255.0,
    )

    train_generator = train_datagen.flow_from_directory(
        TRAINING_DIR,
        target_size = (300,300),
        batch_size = 32,
        class_mode = 'binary' 
    )

    validation_generator = validation_datagen.flow_from_directory(
        VALIDATION_DIR,
        target_size = (300, 300),
        batch_size = 32,
        class_mode = 'binary' 
    )

    transfer_model = VGG16(weights='imagenet', include_top=False, input_shape=(300, 300, 3)) 
    transfer_model.trainable=False

    model = Sequential([
                        # Conv2D(16, (3,3), activation='relu', input_shape = (300, 300, 3)),
                        # MaxPooling2D(2,2),
                        # Conv2D(32, (3,3), activation='relu'),
                        # MaxPooling2D(2,2),
                        # Conv2D(64, (3,3), activation='relu'),
                        # MaxPooling2D(2,2),
                        # Conv2D(64, (3,3), activation='relu'),
                        # MaxPooling2D(2,2),
                        # Conv2D(128, (3,3), activation='relu'),
                        # MaxPooling2D(2,2),
                        transfer_model,
                        Flatten(),
                        Dropout(0.5),
                        Dense(128, activation='relu'),
                        Dense(32, activation='relu'),
                        Dense(1, activation='sigmoid')
    ])

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

    checkpoint_path = 'tmp_checkpoint.ckpt'
    checkpoint = ModelCheckpoint(filepath=checkpoint_path,
                                 save_weights_only = True,
                                 save_best_only = True,
                                 monitor = 'val_loss',
                                 verbose = 1)
    
    model.fit(train_generator,
              validation_data = (validation_generator),
              epochs=25,
              callbacks=[checkpoint])
    
    model.load_weights(checkpoint_path)

    return model


if __name__ == '__main__':
    model = solution_model()
    model.save("TF3-horses-or-humans-type-B.h5")