Res_net.py

import tensorflow as tf
import os
import pickle
import numpy as np

CIFAR_DIR = "./../cifar-10-batches-py"


def load_data(filename):
    """read data from data file."""
    with open(filename, 'rb') as f:
        data = pickle.load(f, encoding='bytes')
        data['data'] = data[b'data']
        data['labels'] = data[b'labels']
        data['batch_label'] = data[b'batch_label']
        data['filenames'] = data[b'filenames']
        return data['data'], data['labels']


# tensorflow.Dataset.
class CifarData:
    def __init__(self, filenames, need_shuffle):
        all_data = []
        all_labels = []
        for filename in filenames:
            data, labels = load_data(filename)
            all_data.append(data)
            all_labels.append(labels)
        self._data = np.vstack(all_data)
        self._data = self._data / 127.5 - 1
        self._labels = np.hstack(all_labels)

        self._num_examples = self._data.shape[0]
        self._need_shuffle = need_shuffle
        self._indicator = 0
        if self._need_shuffle:
            self._shuffle_data()

    def _shuffle_data(self):
        # [0,1,2,3,4,5] -> [5,3,2,4,0,1]
        p = np.random.permutation(self._num_examples)
        self._data = self._data[p]
        self._labels = self._labels[p]

    def next_batch(self, batch_size):
        """return batch_size examples as a batch."""
        end_indicator = self._indicator + batch_size
        if end_indicator > self._num_examples:
            if self._need_shuffle:
                self._shuffle_data()
                self._indicator = 0
                end_indicator = batch_size
            else:
                raise Exception("have no more examples")
        if end_indicator > self._num_examples:
            raise Exception("batch size is larger than all examples")
        batch_data = self._data[self._indicator: end_indicator]
        batch_labels = self._labels[self._indicator: end_indicator]
        self._indicator = end_indicator
        return batch_data, batch_labels


train_filenames = [os.path.join(CIFAR_DIR, 'data_batch_%d' % i) for i in range(1, 6)]
test_filenames = [os.path.join(CIFAR_DIR, 'test_batch')]

train_data = CifarData(train_filenames, True)
test_data = CifarData(test_filenames, False)

def residual_block(x, output_channel):
    """residual connection implementation"""
    input_channel = x.get_shape().as_list()[-1]
    if input_channel * 2 == output_channel:
        increase_dim = True
        strides = (2, 2)
    elif input_channel == output_channel:
        increase_dim = False
        strides = (1, 1)
    else:
        raise Exception("input channel can't match output channel")
    conv1 = tf.layers.conv2d(x,
                             output_channel,
                             (3,3),
                             strides = strides,
                             padding = 'same',
                             activation = tf.nn.relu,
                             name = 'conv1')
    conv2 = tf.layers.conv2d(conv1,
                             output_channel,
                             (3, 3),
                             strides = (1, 1),
                             padding = 'same',
                             activation = tf.nn.relu,
                             name = 'conv2')
    if increase_dim:
        # [None, image_width, image_height, channel] -> [,,,channel*2]
        pooled_x = tf.layers.average_pooling2d(x,
                                               (2, 2),
                                               (2, 2),
                                               padding = 'valid')
        padded_x = tf.pad(pooled_x,
                          [[0,0],
                           [0,0],
                           [0,0],
                           [input_channel // 2, input_channel // 2]])
    else:
        padded_x = x
    output_x = conv2 + padded_x
    return output_x

def res_net(x,
            num_residual_blocks,
            num_filter_base,
            class_num):
    """residual network implementation"""
    """
    Args:
    - x:
    - num_residual_blocks: eg: [3, 4, 6, 3]
    - num_filter_base:
    - class_num:
    """
    num_subsampling = len(num_residual_blocks)
    layers = []
    # x: [None, width, height, channel] -> [width, height, channel]
    input_size = x.get_shape().as_list()[1:]
    with tf.variable_scope('conv0'):
        conv0 = tf.layers.conv2d(x,
                                 num_filter_base,
                                 (3, 3),
                                 strides = (1, 1),
                                 padding = 'same',
                                 activation = tf.nn.relu,
                                 name = 'conv0')
        layers.append(conv0)
    # eg:num_subsampling = 4, sample_id = [0,1,2,3]
    for sample_id in range(num_subsampling):
        for i in range(num_residual_blocks[sample_id]):
            with tf.variable_scope("conv%d_%d" % (sample_id, i)):
                conv = residual_block(
                    layers[-1],
                    num_filter_base * (2 ** sample_id))
                layers.append(conv)
    multiplier = 2 ** (num_subsampling - 1)
    assert layers[-1].get_shape().as_list()[1:] \
        == [input_size[0] / multiplier,
            input_size[1] / multiplier,
            num_filter_base * multiplier]
    with tf.variable_scope('fc'):
        # layer[-1].shape : [None, width, height, channel]
        # kernal_size: image_width, image_height
        global_pool = tf.reduce_mean(layers[-1], [1,2])
        logits = tf.layers.dense(global_pool, class_num)
        layers.append(logits)
    return layers[-1]

x = tf.placeholder(tf.float32, [None, 3072])
y = tf.placeholder(tf.int64, [None])
# [None], eg: [0,5,6,3]
x_image = tf.reshape(x, [-1, 3, 32, 32])
# 32*32
x_image = tf.transpose(x_image, perm=[0, 2, 3, 1])

y_ = res_net(x_image, [2,3,2], 32, 10)

loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)
# y_ -> sofmax
# y -> one_hot
# loss = ylogy_

# indices
predict = tf.argmax(y_, 1)
# [1,0,1,1,1,0,0,0]
correct_prediction = tf.equal(predict, y)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))

with tf.name_scope('train_op'):
    train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)

init = tf.global_variables_initializer()
batch_size = 20
train_steps = 1000
test_steps = 100

# train 7000: 67% after fine-tune -> 94%
with tf.Session() as sess:
    sess.run(init)
    for i in range(train_steps):
        batch_data, batch_labels = train_data.next_batch(batch_size)
        loss_val, acc_val, _ = sess.run(
            [loss, accuracy, train_op],
            feed_dict={
                x: batch_data,
                y: batch_labels})
        if (i + 1) % 100 == 0:
            print('[Train] Step: %d, loss: %4.5f, acc: %4.5f' \
                  % (i + 1, loss_val, acc_val))
        if (i + 1) % 1000 == 0:
            test_data = CifarData(test_filenames, False)
            all_test_acc_val = []
            for j in range(test_steps):
                test_batch_data, test_batch_labels \
                    = test_data.next_batch(batch_size)
                test_acc_val = sess.run(
                    [accuracy],
                    feed_dict={
                        x: test_batch_data,
                        y: test_batch_labels
                    })
                all_test_acc_val.append(test_acc_val)
            test_acc = np.mean(all_test_acc_val)
            print('[Test ] Step: %d, acc: %4.5f' % (i + 1, test_acc))