%tensorflow_version 2.x
import tensorflow as tf
print(tf.__version__)


from tensorflow.keras.applications import ResNet50
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, InputLayer, Flatten, GlobalAveragePooling2D
num_classes = 2
IMG_SIZE = 224
IMG_SHAPE = (IMG_SIZE, IMG_SIZE, 3)
my_new_model=tf.keras.applications.ResNet50(include_top=False, weights='imagenet', input_shape=IMG_SHAPE, pooling='avg', classes=2)
my_new_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])


!wget --no-check-certificate \
   https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip -O \
   /tmp/cats_and_dogs_filtered.zip


import os
import zipfile

local_zip = '/tmp/cats_and_dogs_filtered.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('/tmp')
zip_ref.close()

base_dir = '/tmp/cats_and_dogs_filtered'
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')

# Directory with our training cat pictures
train_cats_dir = os.path.join(train_dir, 'cats')

# Directory with our training dog pictures
train_dogs_dir = os.path.join(train_dir, 'dogs')

# Directory with our validation cat pictures
validation_cats_dir = os.path.join(validation_dir, 'cats')

# Directory with our validation dog pictures
validation_dogs_dir = os.path.join(validation_dir, 'dogs')

train_cat_fnames = os.listdir(train_cats_dir)
train_dog_fnames = os.listdir(train_dogs_dir)


from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications.resnet50 import preprocess_input
train_datagen = ImageDataGenerator(
    preprocessing_function=preprocess_input,
    rotation_range=40,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,)

# Note that the validation data should not be augmented!
test_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)

train_generator = train_datagen.flow_from_directory(
        train_dir,  # This is the source directory for training images
        target_size=(224,224),  # All images will be resized to 224x224
        batch_size=20,
        class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
        validation_dir,
        target_size=(224, 224),
        class_mode='binary')

my_new_model.fit_generator(
        train_generator,
        epochs = 8,
        steps_per_epoch=100,
        validation_data=validation_generator)