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8_cat_vs_dog_fine_tune.py
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8_cat_vs_dog_fine_tune.py
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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)