VGG-16_training_phase2.py

from __future__ import division

import numpy as np

from keras import backend as K
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint
from experiments.utils import HistoryLog
from datetime import datetime
from time import time
import experiments as exp

import os


def get_session(gpu_fraction=0.8):
    import tensorflow as tf

    num_threads = os.environ.get('OMP_NUM_THREADS')
    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_fraction)

    if num_threads:
        return tf.Session(config=tf.ConfigProto(
            gpu_options=gpu_options, intra_op_parallelism_threads=num_threads))
    else:
        return tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))


if K.backend() == 'tensorflow':
    import keras.backend.tensorflow_backend as KTF
    KTF.set_session(get_session(0.8))

img_width, img_height = 224, 224

seed = 42
np.random.seed(seed)

sgd = SGD(lr=0.00004, decay=0.000005, momentum=0.9, nesterov=True)
model = exp.vgg_16_second_phase_model(weights='weights.VGG-16.phase_1.09.hdf5')
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

# prepare data augmentation configuration
train_datagen = ImageDataGenerator(rescale=1. / 255,
                                   rotation_range=40,
                                   width_shift_range=0.2,
                                   height_shift_range=0.2,
                                   zoom_range=0.2,
                                   horizontal_flip=True)

val_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory('data/training',
                                                    target_size=(img_height, img_width),
                                                    class_mode='categorical',
                                                    batch_size=1)

validation_generator = val_datagen.flow_from_directory(
    'data/validation',
    target_size=(img_height, img_width),
    class_mode='categorical',
    batch_size=1)

# checkpoint
weights_filepath = "weights.VGG-16.phase_2.{epoch:02d}.hdf5"
checkpoint = ModelCheckpoint(weights_filepath, monitor='val_acc', verbose=1)
history = HistoryLog()

# fine-tune the model
model.fit_generator(
    train_generator,
    steps_per_epoch=36095,
    epochs=10,
    callbacks=[checkpoint, history],
    validation_data=validation_generator,
    validation_steps=2582,
    initial_epoch=0)

ts = time()
timestamp = datetime.fromtimestamp(ts).strftime('%Y-%m-%d_%H:%M:%S')

loss_filepath = "vgg16.phase_2.loss.{}.log".format(timestamp)
history.log_training_loss(loss_filepath)

epoch_filepath = "vgg16.phase_2.epoch.{}.log".format(timestamp)
history.log_epoch(epoch_filepath)

if K.backend() == 'tensorflow':
    K.clear_session()