8_cat_vs_dog_fine_tune.py

import numpy as np
import os
from tensorflow.keras import applications
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import optimizers
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Dropout, Flatten, Dense

work_dir = '../dogs_vs_cats_dataset/data'
top_model_weights_path = 'fc_model.h5'
# dimensions of our images.
img_width, img_height = 150, 150

train_data_dir = os.path.join(work_dir, 'train')
validation_data_dir = os.path.join(work_dir, 'test')
nb_train_samples = 2000
nb_validation_samples = 800
epochs = 50
batch_size = 16
input_shape = (img_height, img_width, 3)
epoch_steps = nb_train_samples // batch_size
test_steps = nb_validation_samples // batch_size

# build the VGG16 network
base_model = applications.VGG16(input_shape=input_shape, include_top=False, weights='imagenet')
print('Model loaded.')

# build a classifier model to put on top of the convolutional model
top_model = Sequential()
top_model.add(Flatten(input_shape=base_model.output_shape[1:]))
top_model.add(Dense(256, activation='relu'))
top_model.add(Dropout(0.5))
top_model.add(Dense(1, activation='sigmoid'))

# note that it is necessary to start with a fully-trained
# classifier, including the top classifier,
# in order to successfully do fine-tuning
top_model.load_weights(top_model_weights_path)

# add the model on top of the convolutional base
model = Model(base_model.input, top_model(base_model.output))

# set the first 25 layers (up to the last conv block)
# to non-trainable (weights will not be updated)
for layer in model.layers[:15]:
    layer.trainable = False

# compile the model with a SGD/momentum optimizer
# and a very slow learning rate.
model.compile(loss='binary_crossentropy',
              optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
              metrics=['accuracy'])

# prepare data augmentation configuration
train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='binary')

# fine-tune the model
model.fit_generator(
    train_generator,
    steps_per_epoch=epoch_steps,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=test_steps)