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TensorFlow从1到2 - 5 - 非专家莫入！Tenso

2017-10-31  本文已影响828人  袁承兴

TensorFlow从0到N专题入口

当看到本篇时，根据TensorFlow官方标准《Deep MNIST for Experts》，你已经达到Expert Level，要恭喜了。

且不说是否夸大其词，换一种角度，假如能乘坐时光机仅往回飞5年，借此CNN实现，你也能在ImageNet上叱咤风云，战无不胜。就算飞不回去，它在今天依然是大杀伤力武器，大批大批老算法等着你去枪毙，大片大片垂直领域换代产品等着你去落地。这还不够么？

上一篇4 深入拆解CNN架构准备好了CNN的理论基础，本篇从代码层面，来看看TensorFlow如何搞定CNN，使识别精度达到99%以上。

TensorFlow

分析代码的方式

再次说明下分析代码的方式。

与逐行分析代码不同，我偏好先清理代码涉及到的语言、工具的知识点，然后再去扫描逻辑。所以“Python必知必会”、“TensorFlow必知必会”将是首先出现的章节。

当然你也可以直接跳到代码部分：

代码运行环境：

Python必知必会

With

在本篇所分析的代码中，用到了大量的With，值得一说。

With要搭配上下文管理器（Context Manager）对象使用。

所谓的上下文管理器对象，就是实现了上下文管理器协议（Context Manager Protocol）的对象。协议要求对象定义中必须实现__enter__()和__exit__()方法。

当看到下面语句时：

With Context Manager Object [as target]：
    Body

它有4个意思：

总的来说，With语句帮助上下文管理器对象实现了两个自动化的操作enter和exit，并充分考虑了异常情况。对于资源类对象（用完需要尽快释放）的使用，比如文件句柄、数据库连接等等，这无疑是一种简洁而完善的代码形式。

如果还想了解更多的细节，推荐阅读一篇老文章《浅谈Python的with语句》。

TensorFlow必知必会

上面说的with，主要是为了配合TensorFlow的tf.name_scope。

tf.name_scope

先来体会下我设计的“玩具”代码：

import tensorflow as tf

with tf.name_scope('V1'):
    a1 = tf.Variable([50])
    a2 = tf.Variable([100], name='a1')

assert a1.name == 'V1/Variable:0'
assert a2.name == 'V1/a1:0'

with tf.name_scope("V2"):
    a1 = tf.add(a1, a2, name="Add_Variable_a1")
    a2 = tf.multiply(a1, a2, name="Add_Variable_a1")

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    assert a1.name == 'V2/Add_Variable_a1:0'
    assert sess.run(a1) == 150
    assert a2.name == 'V2/Add_Variable_a1_1:0'
    assert sess.run(a2) == 15000

a2 = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='V1/a1:0')[0]
assert a2.name == 'V1/a1:0'

可以看到，其中有两类与With的搭配。

一种是资源类的tf.Session，手工使用时总要记得在使用后调用tf.Session.close方法释放，而与With搭配使用，则会自动调用其__exit__()进行释放。

另一种是本节的重点，与With搭配的并不是“资源”，而是tf.name_scope()方法返回的对象，此时在With块中定义的节点，都会自动在属性name上添加name scope前缀：

注意：通过tf.get_variable定义的节点，其属性name不受影响，tf.get_variable需要与tf.variable_scope搭配产生类似效果。

TensorFlow的name scope有什么作用呢？主要是两点：

节点折叠

如果对上述介绍仍有疑问，请仔细读读下面我为此准备的：

CNN架构

扫清了障碍，终于可以开始构建CNN了。

TensorFlow官方《Deep MNIST for Experts》中构建的CNN与LeNet-5的深度规模相当，具有5个隐藏层，但是卷积层滤波器的数量可多了不少：

计算下网络中权重的数量：

5x5x1x32 + 5x5x32x64 + 7x7x64x1024 + 1024x10 = 800 + 51200 + 3211264 + 10240 = 3273504

这个并不算深的CNN有三百多万个参数，比之前识别MNIST所用的浅层神经网络，多了两个数量级。不过再仔细看下，两个卷积层包含的权重数量所占比例极小，导致参数量激增的是全连接网络层fc1。

下图是构建好的计算图（Computational Graph），得益于name scope的使用，它能够以“层”为粒度，清晰的显示出网络的骨架：

CNN

Tensors和Filters

示例代码中，有了更多工程化的考虑，对CNN的构建进行了封装，形成了函数deepnn，在函数内部，With代码块的使用，使得网络的前馈路径也相当清楚，这部分就不再赘述了。

本节的重点是我们构建的计算图节点上流动的Tensors，以及参与运算的Filters：

deepnn函数定义如下（省略处用……代替）：

def deepnn(x):
    with tf.name_scope('reshape'):
        x_image = tf.reshape(x, [-1, 28, 28, 1])

    with tf.name_scope('conv1'):
        W_conv1 = weight_variable([5, 5, 1, 32])
        ……

    with tf.name_scope('pool1'):
        h_pool1 = max_pool_2x2(h_conv1)

    with tf.name_scope('conv2'):
        W_conv2 = weight_variable([5, 5, 32, 64])
        ……

    with tf.name_scope('pool2'):
        h_pool2 = max_pool_2x2(h_conv2)

    with tf.name_scope('fc1'):
        W_fc1 = weight_variable([7 * 7 * 64, 1024])
        b_fc1 = bias_variable([1024])

        h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
        h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

    with tf.name_scope('dropout'):
        ……

    with tf.name_scope('fc2'):
        W_fc2 = weight_variable([1024, 10])
        b_fc2 = bias_variable([10])

        y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
    return y_conv, keep_prob

Tensors-[batch, width, height, channels]：

Filters-[width, height, channels，F-amount]：

跨距strides

为防止代码重复，卷积和池化这两项操作也进行了封装，前面缺失的滤波器的跨距（strides）定义，包含在这里。

conv2d定义：

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

max_pool_2x2定义：

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                          strides=[1, 2, 2, 1], padding='SAME')

滤波器还有一个padding参数，官方文档给出的计算方法见下：

测试结果

运行代码进行实测，与TensorFlow官方基本一致：

相同架构下，基于Fashion MNIST数据集对网络重新进行了训练，验证集识别精度达到了92.64%。CNN的全能型，由此可见一斑。

Fashion MNIST训练过程输出

附完整代码

import argparse
import sys
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

FLAGS = None


def deepnn(x):
    """deepnn builds the graph for a deep net for classifying digits.
    Args:
      x: an input tensor with the dimensions (N_examples, 784), where 784 is
      the number of pixels in a standard MNIST image.
    Returns:
      A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with
      values equal to the logits of classifying the digit into one of 10
      classes (the digits 0-9). keep_prob is a scalar placeholder for the
      probability of dropout.
    """
    # Reshape to use within a convolutional neural net.
    # Last dimension is for "features" - there is only one here, since images
    # are grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
    with tf.name_scope('reshape'):
        x_image = tf.reshape(x, [-1, 28, 28, 1])

    # First convolutional layer - maps one grayscale image to 32 feature maps.
    with tf.name_scope('conv1'):
        W_conv1 = weight_variable([5, 5, 1, 32])
        b_conv1 = bias_variable([32])
        h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

    # Pooling layer - downsamples by 2X.
    with tf.name_scope('pool1'):
        h_pool1 = max_pool_2x2(h_conv1)

    # Second convolutional layer -- maps 32 feature maps to 64.
    with tf.name_scope('conv2'):
        W_conv2 = weight_variable([5, 5, 32, 64])
        b_conv2 = bias_variable([64])
        h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

    # Second pooling layer.
    with tf.name_scope('pool2'):
        h_pool2 = max_pool_2x2(h_conv2)

    # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
    # is down to 7x7x64 feature maps -- maps this to 1024 features.
    with tf.name_scope('fc1'):
        W_fc1 = weight_variable([7 * 7 * 64, 1024])
        b_fc1 = bias_variable([1024])

        h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
        h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

    # Dropout - controls the complexity of the model, prevents co-adaptation of
    # features.
    with tf.name_scope('dropout'):
        keep_prob = tf.placeholder(tf.float32)
        h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    # Map the 1024 features to 10 classes, one for each digit
    with tf.name_scope('fc2'):
        W_fc2 = weight_variable([1024, 10])
        b_fc2 = bias_variable([10])

        y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
    return y_conv, keep_prob


def conv2d(x, W):
    """conv2d returns a 2d convolution layer with full stride."""
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):
    """max_pool_2x2 downsamples a feature map by 2X."""
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                          strides=[1, 2, 2, 1], padding='SAME')


def weight_variable(shape):
    """weight_variable generates a weight variable of a given shape."""
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)


def bias_variable(shape):
    """bias_variable generates a bias variable of a given shape."""
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


def main(_):
    # Import data
    mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True,
                                      validation_size=10000)

    # Create the model
    x = tf.placeholder(tf.float32, [None, 784])

    # Define loss and optimizer
    y_ = tf.placeholder(tf.float32, [None, 10])

    # Build the graph for the deep net
    y_conv, keep_prob = deepnn(x)

    with tf.name_scope('loss'):
        cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,
                                                                logits=y_conv)
        cross_entropy = tf.reduce_mean(cross_entropy)

    with tf.name_scope('adam_optimizer'):
        train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

    with tf.name_scope('accuracy'):
        correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
        correct_prediction = tf.cast(correct_prediction, tf.float32)
        accuracy = tf.reduce_mean(correct_prediction)

    graph_location = 'MNIST/logs/tf2-4/train'
    print('Saving graph to: %s' % graph_location)
    train_writer = tf.summary.FileWriter(graph_location)
    train_writer.add_graph(tf.get_default_graph())

    best = 0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch in range(60):
            for _ in range(1000):
                batch = mnist.train.next_batch(50)
                train_step.run(
                    feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
            accuracy_validation = accuracy.eval(
                feed_dict={
                    x: mnist.validation.images,
                    y_: mnist.validation.labels,
                    keep_prob: 1.0})
            print('epoch %d, validation accuracy %s' % (
                epoch, accuracy_validation))
            best = (best, accuracy_validation)[
                best <= accuracy_validation]

    # Test trained model
    print("best: %s" % best)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--data_dir', type=str, default='../MNIST/',
                        help='Directory for storing input data')
    FLAGS, unparsed = parser.parse_known_args()
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

下载tf_2-5_cnn.py

上一篇 4 - 深入拆解CNN架构

共享协议：署名-非商业性使用-禁止演绎（CC BY-NC-ND 3.0 CN）
转载请注明：作者黑猿大叔（简书）

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