TensorFlow手写数字识别mnist example源码分析

TensorFlow手写数字识别mnist example源码分析
TensorFlow 默认安装在 /usr/lib/Python/site-packages/tensorflow/

实例文件位于tensorflow/models/image/mnist/convolutional.py，为TensorFlow自带的example文件。

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.## Licensed under the Apache License, Version 2.0 (the "License");# you may not use this file except in compliance with the License.# You may obtain a copy of the License at##     http://www.apache.org/licenses/LICENSE-2.0## Unless required by applicable law or agreed to in writing, software# distributed under the License is distributed on an "AS IS" BASIS,# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.# See the License for the specific language governing permissions and# limitations under the License.# =============================================================================="""Simple, end-to-end, LeNet-5-like convolutional MNIST model example.This should achieve a test error of 0.7%. Please keep this model as simple andlinear as possible, it is meant as a tutorial for simple convolutional models.Run with --self_test on the command line to execute a short self-test."""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport argparseimport gzipimport osimport sysimport timeimport numpyfrom six.moves import urllibfrom six.moves import xrange  # pylint: disable=redefined-builtinimport tensorflow as tfSOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'  # 数据源WORK_DIRECTORY = 'data'  # 工作目录，存放下载的数据# MNIST 数据集特征：#     图像尺寸 28x28IMAGE_SIZE = 28NUM_CHANNELS = 1  # 黑白图像PIXEL_DEPTH = 255  # 像素值0~255NUM_LABELS = 10  # 标签分10个类别VALIDATION_SIZE = 5000  # 验证集大小SEED = 66478  # 随机数种子，可设为 None 表示真的随机BATCH_SIZE = 64  # 批处理大小为64NUM_EPOCHS = 10  # 数据全集一共过10遍网络EVAL_BATCH_SIZE = 64  # 验证集批处理大小也是64# 验证时间间隔，每训练100个批处理，做一次评估EVAL_FREQUENCY = 100  # Number of steps between evaluations.FLAGS = Nonedef data_type():    """Return the type of the activations, weights, and placeholder variables."""    if FLAGS.use_fp16:        return tf.float16    else:        return tf.float32def maybe_download(filename):    """如果下载过了数据，就不再重复下载"""    if not tf.gfile.Exists(WORK_DIRECTORY):        tf.gfile.MakeDirs(WORK_DIRECTORY)    filepath = os.path.join(WORK_DIRECTORY, filename)    if not tf.gfile.Exists(filepath):        filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)        with tf.gfile.GFile(filepath) as f:            size = f.size()        print('Successfully downloaded', filename, size, 'bytes.')    return filepathdef extract_data(filename, num_images):    # 抽取数据，变为 4维张量[图像索引，y, x, c]    # 去均值、做归一化，范围变到[-0.5, 0.5]    print('Extracting', filename)    with gzip.open(filename) as bytestream:        bytestream.read(16)        buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images * NUM_CHANNELS)        data = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.float32)        data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH        data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS)        return datadef extract_labels(filename, num_images):    """Extract the labels into a vector of int64 label IDs."""    print('Extracting', filename)    with gzip.open(filename) as bytestream:        bytestream.read(8)        buf = bytestream.read(1 * num_images)        labels = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.int64)    return labelsdef fake_data(num_images):    """Generate a fake dataset that matches the dimensions of MNIST."""    """生成一个匹配MNIST数据集大小的伪造数据集"""    data = numpy.ndarray(        shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS),        dtype=numpy.float32)    labels = numpy.zeros(shape=(num_images,), dtype=numpy.int64)    for image in xrange(num_images):        label = image % 2        data[image, :, :, 0] = label - 0.5        labels[image] = label    return data, labelsdef error_rate(predictions, labels):    """Return the error rate based on dense predictions and sparse labels."""    return 100.0 - (        100.0 *        numpy.sum(numpy.argmax(predictions, 1) == labels) /        predictions.shape[0])# 主函数def main(_):    if FLAGS.self_test:        print('Running self-test.')        train_data, train_labels = fake_data(256)        validation_data, validation_labels = fake_data(EVAL_BATCH_SIZE)        test_data, test_labels = fake_data(EVAL_BATCH_SIZE)        num_epochs = 1    else:        # 下载数据        train_data_filename = maybe_download('train-images-idx3-ubyte.gz')        train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')        test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')        test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')        # 载入数据到numpy        train_data = extract_data(train_data_filename, 60000)        train_labels = extract_labels(train_labels_filename, 60000)        test_data = extract_data(test_data_filename, 10000)        test_labels = extract_labels(test_labels_filename, 10000)        # 产生评测集        validation_data = train_data[:VALIDATION_SIZE, ...]        validation_labels = train_labels[:VALIDATION_SIZE]        train_data = train_data[VALIDATION_SIZE:, ...]        train_labels = train_labels[VALIDATION_SIZE:]        num_epochs = NUM_EPOCHS # 数据全集一共过10遍网络    train_size = train_labels.shape[0]    # 训练样本和标签将从这里送入网络。    # 每训练迭代步，占位符节点将被送入一个批处理数据    # 训练数据节点    train_data_node = tf.placeholder(        data_type(),        shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))    # 训练标签节点    train_labels_node = tf.placeholder(tf.int64, shape=(BATCH_SIZE,))    # 评测数据节点    eval_data = tf.placeholder(        data_type(),        shape=(EVAL_BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))    # 下面这些变量是网络的可训练权值    # conv1 权值维度为 32 x channels x 5 x 5, 32 为特征图数目    conv1_weights = tf.Variable(        tf.truncated_normal([5, 5, NUM_CHANNELS, 32],  # 5x5 filter, depth 32.                            stddev=0.1,                            seed=SEED, dtype=data_type()))    # conv1 偏置    conv1_biases = tf.Variable(tf.zeros([32], dtype=data_type()))    # conv2 权值维度为 64 x 32 x 5 x 5    conv2_weights = tf.Variable(tf.truncated_normal(        [5, 5, 32, 64], stddev=0.1,        seed=SEED, dtype=data_type()))    # conv2 偏置    conv2_biases = tf.Variable(tf.constant(0.1, shape=[64], dtype=data_type()))    # 全连接层 fc1 权值，神经元数目为512    fc1_weights = tf.Variable(  # fully connected, depth 512.        tf.truncated_normal([IMAGE_SIZE // 4 * IMAGE_SIZE // 4 * 64, 512],                            stddev=0.1,                            seed=SEED,                            dtype=data_type()))    fc1_biases = tf.Variable(tf.constant(0.1, shape=[512], dtype=data_type()))    # fc2 权值，维度与标签类数目一致    fc2_weights = tf.Variable(tf.truncated_normal([512, NUM_LABELS],                                                  stddev=0.1,                                                  seed=SEED,                                                  dtype=data_type()))    fc2_biases = tf.Variable(tf.constant(        0.1, shape=[NUM_LABELS], dtype=data_type()))    # 两个网络：训练网络和评测网络    # 它们共享权值    # 实现 LeNet-5 模型，该函数输入为数据，输出为fc2的响应    # 第二个参数区分训练网络还是评测网络    def model(data, train=False):        # 二维卷积，使用“不变形”补零（即输出特征图与输入尺寸一致）。        conv = tf.nn.conv2d(data,                            conv1_weights,                            strides=[1, 1, 1, 1],                            padding='SAME')        # 加偏置、过激活函数一块完成        relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))        # 最大值下采样        pool = tf.nn.max_pool(relu,                              ksize=[1, 2, 2, 1],                              strides=[1, 2, 2, 1],                              padding='SAME')        # 第二个卷积层，步长为1        conv = tf.nn.conv2d(pool,                            conv2_weights,                            strides=[1, 1, 1, 1],                            padding='SAME')        relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases))        pool = tf.nn.max_pool(relu,                              ksize=[1, 2, 2, 1],                              strides=[1, 2, 2, 1],                              padding='SAME')        # 特征图变形为2维矩阵，便于送入全连接层        pool_shape = pool.get_shape().as_list()        reshape = tf.reshape(            pool,            [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])        # 全连接层，注意“+”运算自动广播偏置        hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)        # 训练阶段，增加 50% dropout；而评测阶段无需该操作        if train:            hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)        return tf.matmul(hidden, fc2_weights) + fc2_biases        # 训练阶段计算： 对数+交叉熵 损失函数    logits = model(train_data_node, True)    loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(        logits, train_labels_node))    # 全连接层参数进行 L2 正则化    regularizers = (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) +                    tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases))    # 将正则项加入损失函数    loss += 5e-4 * regularizers    # 优化器： 设置一个变量，每个批处理递增，控制学习速率衰减    batch = tf.Variable(0, dtype=data_type())    # Decay once per epoch, using an exponential schedule starting at 0.01.    #  指数衰减    learning_rate = tf.train.exponential_decay(        0.01,  # 基本学习速率        batch * BATCH_SIZE,  # 当前批处理在数据全集中的位置        train_size,  # Decay step.        0.95,  # 衰减率        staircase=True)    # Use simple momentum for the optimization.    optimizer = tf.train.MomentumOptimizer(learning_rate,                                           0.9).minimize(loss,                                                         global_step=batch)    # 用softmax 计算训练批处理的预测概率    train_prediction = tf.nn.softmax(logits)    # 用 softmax 计算评测批处理的预测概率    eval_prediction = tf.nn.softmax(model(eval_data))    # Small utility function to evaluate a dataset by feeding batches of data to    # {eval_data} and pulling the results from {eval_predictions}.    # Saves memory and enables this to run on smaller GPUs.    def eval_in_batches(data, sess):        """通过运行在小批量数据，得到所有预测结果."""        size = data.shape[0]        if size < EVAL_BATCH_SIZE:            raise ValueError("batch size for evals larger than dataset: %d" % size)        predictions = numpy.ndarray(shape=(size, NUM_LABELS), dtype=numpy.float32)        for begin in xrange(0, size, EVAL_BATCH_SIZE):            end = begin + EVAL_BATCH_SIZE            if end <= size:                predictions[begin:end, :] = sess.run(                    eval_prediction,                    feed_dict={eval_data: data[begin:end, ...]})            else:                batch_predictions = sess.run(                    eval_prediction,                    feed_dict={eval_data: data[-EVAL_BATCH_SIZE:, ...]})                predictions[begin:, :] = batch_predictions[begin - size:, :]        return predictions    # Create a local session to run the training.    start_time = time.time()    with tf.Session() as sess:        # 初始化操作准备参数        tf.global_variables_initializer().run()        print('Initialized!')        # 循环训练        for step in xrange(int(num_epochs * train_size) // BATCH_SIZE):            # 计算当前minibatch的offset            # Note that we could use better randomization across epochs.            offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE)            batch_data = train_data[offset:(offset + BATCH_SIZE), ...]            batch_labels = train_labels[offset:(offset + BATCH_SIZE)]            # This dictionary maps the batch data (as a numpy array) to the            # node in the graph it should be fed to.            feed_dict = {train_data_node: batch_data,                         train_labels_node: batch_labels}            # 运行优化器更新权重            sess.run(optimizer, feed_dict=feed_dict)            # print some extra information once reach the evaluation frequency            if step % EVAL_FREQUENCY == 0:                # fetch some extra nodes' data                l, lr, predictions = sess.run([loss, learning_rate, train_prediction],                                              feed_dict=feed_dict)                elapsed_time = time.time() - start_time                start_time = time.time()                print('Step %d (epoch %.2f), %.1f ms' %                      (step, float(step) * BATCH_SIZE / train_size,                       1000 * elapsed_time / EVAL_FREQUENCY))                print('Minibatch loss: %.3f, learning rate: %.6f' % (l, lr))                print('Minibatch error: %.1f%%' % error_rate(predictions, batch_labels))                print('Validation error: %.1f%%' % error_rate(                    eval_in_batches(validation_data, sess), validation_labels))                sys.stdout.flush()        # Finally print the result!        test_error = error_rate(eval_in_batches(test_data, sess), test_labels)        print('Test error: %.1f%%' % test_error)        if FLAGS.self_test:            print('test_error', test_error)            assert test_error == 0.0, 'expected 0.0 test_error, got %.2f' % (                test_error,)if __name__ == '__main__':    parser = argparse.ArgumentParser()    parser.add_argument(        '--use_fp16',        default=False,        help='Use half floats instead of full floats if True.',        action='store_true')    parser.add_argument(        '--self_test',        default=False,        action='store_true',        help='True if running a self test.')    FLAGS, unparsed = parser.parse_known_args()    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)