question3.py

# ======================================================================
# There are 5 questions in this exam with increasing difficulty from 1-5.
# Please note that the weight of the grade for the question is relative
# to its difficulty. So your Category 1 question will score significantly
# less than your Category 5 question.
#
# Don't use lambda layers in your model.
# You do not need them to solve the question.
# Lambda layers are not supported by the grading infrastructure.
#
# You must use the Submit and Test button to submit your model
# at least once in this category before you finally submit your exam,
# otherwise you will score zero for this category.
# ======================================================================
#
# Computer vision with CNNs
#
# Create and train a classifier for horses or humans using the provided data.
# Make sure your final layer is a 1 neuron, activated by sigmoid as shown.
#
# The test will use images that are 300x300 with 3 bytes color depth so be sure to
# design your neural network accordingly
import urllib

import tensorflow as tf
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.layers import Dense, Flatten, Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.preprocessing.image import ImageDataGenerator

physical_devices = tf.config.list_physical_devices('GPU')
try:
    tf.config.experimental.set_memory_growth(physical_devices[0], True)
except:
    pass

local_weights_file = './inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'

pre_trained_model = InceptionV3(input_shape=(300, 300, 3),
                                include_top=False,
                                weights=None)

pre_trained_model.load_weights(local_weights_file)

for layer in pre_trained_model.layers:
    layer.trainable = False


last_layer = pre_trained_model.get_layer('mixed7')
print('last layer output shape: ', last_layer.output_shape)
last_output = last_layer.output


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()

    train_datagen = ImageDataGenerator(
        rescale=1. / 255,
        rotation_range=40,
        width_shift_range=0.2,
        height_shift_range=0.2,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True,
        fill_mode='nearest')

    validation_datagen = ImageDataGenerator(rescale=1 / 255)

    train_generator = train_datagen.flow_from_directory(
        './tmp/horse-or-human/',  # This is the source directory for training images
        target_size=(300, 300),  # All images will be resized to 150x150
        batch_size=1,
        # Since we use binary_crossentropy loss, we need binary labels
        class_mode='binary')

    validation_generator = validation_datagen.flow_from_directory(
        './tmp/validation-horse-or-human/',  # This is the source directory for training images
        target_size=(300, 300),  # All images will be resized to 150x150
        batch_size=1,
        # Since we use binary_crossentropy loss, we need binary labels
        class_mode='binary')

    x = Flatten()(last_output)
    x = Dense(1024, activation='relu')(x)
    x = Dropout(0.2)(x)
    x = Dense(1, activation='sigmoid')(x)

    model = Model(pre_trained_model.input, x)

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

    model.fit(
        train_generator,
        steps_per_epoch=8,
        epochs=100,
        verbose=1,
        validation_data=validation_generator,
        validation_steps=8,
        callbacks=[
            ModelCheckpoint(
                filepath='mymodel.h5',
                monitor='val_accuracy',
                mode='max',
                save_best_only=True,
                save_weights_only=False,
                verbose=1
            )
        ]
    )

    return model

    # NOTE: If training is taking a very long time, you should consider setting the batch size
    # appropriately on the generator, and the steps per epoch in the model.fit() function.


# Note that you'll need to save your model as a .h5 like this.
# When you press the Submit and Test button, your saved .h5 model will
# be sent to the testing infrastructure for scoring
# and the score will be returned to you.
if __name__ == '__main__':
    model = solution_model()
    model.save("mymodel.bak.h5")