cifar10_alexnet_wd_rand_crop.py

#!/bin/python2
import numpy as np
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.callbacks import TensorBoard, EarlyStopping
from keras.optimizers import SGD
from keras.initializers import Constant
from keras.regularizers import l2
import keras.backend as K
import pickle
import time
from copy import deepcopy
from shutil import copy

model = Sequential()
model.add(Conv2D(96, (5, 5), input_shape=(28, 28, 3), kernel_initializer=
                 'glorot_normal', bias_initializer=Constant(0.1), padding=
                 'same', activation='relu')) 
model.add(MaxPooling2D((3, 3), padding='same'))
model.add(BatchNormalization())
model.add(Conv2D(256, (5, 5), kernel_initializer='glorot_normal',
                 bias_initializer=Constant(0.1), padding='same',
                 activation='relu')) 
model.add(MaxPooling2D((3, 3), padding='same'))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dense(384, kernel_initializer='glorot_normal',
                bias_initializer=Constant(0.1), kernel_regularizer=l2(1e-4),
                activation='relu'))
model.add(Dense(192, kernel_initializer='glorot_normal',
                bias_initializer=Constant(0.1), kernel_regularizer=l2(1e-4),
                activation='relu'))
model.add(Dense(10, kernel_initializer='glorot_normal',
                bias_initializer=Constant(0.1), activation='softmax'))

early_stop = EarlyStopping(monitor='loss', min_delta=0.0001, patience=5)
now = str(time.time())
tb_callback = TensorBoard(log_dir='../Tensorboard/alexnet/' + now)

img = tf.placeholder(tf.float32, [32, 32, 3])
norm_image = tf.image.per_image_standardization(img)
img_rand_crop = tf.random_crop(img, [28, 28, 3])

opt = SGD(lr=0.01, momentum=0.9)
model.compile(loss='mse', optimizer=opt, metrics=['accuracy'])

sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())

cifar10_train_images = []
cifar10_train_labels = []
print "Loading training images..."
for i in range(1, 6):
    train_file = open('../../cifar-10-batches-py/data_batch_' + str(i), 'r')
    train_dict = pickle.load(train_file)
    for image, label in zip(train_dict['data'], train_dict['labels']):
        image_red = np.reshape(image[:1024], (32, 32)) / 255.0
        image_red = np.reshape(image_red, (32, 32, 1))
        image_green = np.reshape(image[1024:2048], (32, 32)) / 255.0
        image_green = np.reshape(image_green, (32, 32, 1))
        image_blue = np.reshape(image[2048:3072], (32, 32)) / 255.0
        image_blue = np.reshape(image_blue, (32, 32, 1))
        image = np.concatenate([image_red, image_green, image_blue], axis=-1)
        image = norm_image.eval(feed_dict={img:image})
        cifar10_train_images.append(image)
        label = np.identity(10)[label]
        cifar10_train_labels.append(label)
    train_file.close()

def random_crop_gen(sess, train_images, train_labels, batch_size):
    while True:
        for i in range(0, len(train_labels), batch_size):
            batch_images = []
            batch_labels = train_labels[i:(i + batch_size)]
            for image in train_images[i:(i + batch_size)]:
                batch_images.append(sess.run(img_rand_crop, feed_dict={img:image}))
            yield (np.array(batch_images), np.array(batch_labels))

epochs = 100
batch_size = 16

prev_loss = 1e4
patience = deepcopy(early_stop.patience)
for epoch in range(epochs):
    hist = model.fit_generator(random_crop_gen(sess, cifar10_train_images,
                               cifar10_train_labels, batch_size),
                               len(cifar10_train_labels) // batch_size,
                               epochs=(epoch + 1), initial_epoch=epoch,
                               callbacks=[tb_callback])
    #hist = model.fit(np.array(cifar10_train_images), np.array(
                     #cifar10_train_labels), epochs=(epoch + 1),
                     #batch_size=batch_size, initial_epoch=epoch,
                     #callbacks=[tb_callback])
    K.set_value(opt.lr, 0.95 * K.get_value(opt.lr))
    if hist.history[early_stop.monitor][0] - prev_loss > early_stop.min_delta:
        patience -= 1
    else:
        patience = deepcopy(early_stop.patience)
    if patience <= 0:
        break
    else:
        prev_loss = hist.history[early_stop.monitor][0]

del cifar10_train_images, cifar10_train_labels
print "Loading test images..."
cifar10_test_images = []
cifar10_test_labels = []
test_file = open('../../cifar-10-batches-py/test_batch', 'r')
test_dict = pickle.load(test_file)
for image, label in zip(test_dict['data'], test_dict['labels']):
    image_red = np.reshape(image[:1024], (32, 32)) / 255.0
    image_red = np.reshape(image_red, (32, 32, 1))
    image_green = np.reshape(image[1024:2048], (32, 32)) / 255.0
    image_green = np.reshape(image_green, (32, 32, 1))
    image_blue = np.reshape(image[2048:3072], (32, 32)) / 255.0
    image_blue = np.reshape(image_blue, (32, 32, 1))
    image_blue = np.reshape(image_blue, (32, 32, 1))
    image = np.concatenate([image_red, image_green, image_blue], axis=-1)
    image = norm_image.eval(feed_dict={img:image})
    image = image[2:-2, 2:-2, :]
    cifar10_test_images.append(image)
    label = np.identity(10)[label]
    cifar10_test_labels.append(label)
test_file.close()

print(model.evaluate(np.array(cifar10_test_images),
                     np.array(cifar10_test_labels), batch_size=256))

response = raw_input("Do you want to save this model? (Y/n): ")
if response.lower() not in ['n', 'no', 'nah', 'nein', 'nahi', 'nope']:
    model.save('cifar10_alexnet_wd_rand_crop.h5')
    copy('./cifar10_alexnet_wd_rand_crop.py', '../Tensorboard/alexnet/' + now)
    print "Model saved"