[译] TensorFlow 教程 #04 - 保存 & 恢复

本篇主要介绍如何保存和恢复神经网络变量以及Early-Stopping优化策略。
其中有大段以前教程的文字及代码，若是看过的朋友能够快速翻到下文Saver相关的部分。

01 - 简单线性模型 | 02 - 卷积神经网络 | 03 - PrettyTensor

by Magnus Erik Hvass Pedersen / GitHub / Videos on YouTube
中文翻译 thrillerist / Github

若有转载，请附上本文连接。

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介绍

这篇教程展现了如何保存以及恢复神经网络中的变量。在优化的过程当中，当验证集上分类准确率提升时，保存神经网络的变量。若是通过1000次迭代还不能提高性能时，就终止优化。而后咱们从新载入在验证集上表现最好的变量。

这种策略称为Early-Stopping。它用来避免神经网络的过拟合。（过拟合）会在神经网络训练时间太长时出现，此时神经网络开始学习训练集中的噪声，将致使它误分类新的图像。

这篇教程主要是用神经网络来识别MNIST数据集中的手写数字，过拟合在这里并非什么大问题。但本教程展现了Early Stopping的思想。

本文基于上一篇教程，你须要了解基本的TensorFlow和附加包Pretty Tensor。其中大量代码和文字与以前教程类似，若是你已经看过就能够快速地浏览本文。

流程图

下面的图表直接显示了以后实现的卷积神经网络中数据的传递。网络有两个卷积层和两个全链接层，最后一层是用来给输入图像分类的。关于网络和卷积的更多细节描述见教程 #02 。

from IPython.display import Image
Image('images/02_network_flowchart.png')复制代码

导入

%matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from sklearn.metrics import confusion_matrix
import time
from datetime import timedelta
import math
import os

# Use PrettyTensor to simplify Neural Network construction.
import prettytensor as pt复制代码

使用Python3.5.2（Anaconda）开发，TensorFlow版本是：

tf.__version__复制代码

'0.12.0-rc0'

PrettyTensor 版本:

pt.__version__复制代码

'0.7.1'

载入数据

MNIST数据集大约12MB，若是没在给定路径中找到就会自动下载。

from tensorflow.examples.tutorials.mnist import input_data
data = input_data.read_data_sets('data/MNIST/', one_hot=True)复制代码

Extracting data/MNIST/train-images-idx3-ubyte.gz
Extracting data/MNIST/train-labels-idx1-ubyte.gz
Extracting data/MNIST/t10k-images-idx3-ubyte.gz
Extracting data/MNIST/t10k-labels-idx1-ubyte.gz

如今已经载入了MNIST数据集，它由70,000张图像和对应的标签（好比图像的类别）组成。数据集分红三份互相独立的子集。咱们在教程中只用训练集和测试集。

print("Size of:")
print("- Training-set:\t\t{}".format(len(data.train.labels)))
print("- Test-set:\t\t{}".format(len(data.test.labels)))
print("- Validation-set:\t{}".format(len(data.validation.labels)))复制代码

Size of:
-Training-set: 55000
-Test-set: 10000
-Validation-set: 5000

类型标签使用One-Hot编码，这意外每一个标签是长为10的向量，除了一个元素以外，其余的都为零。这个元素的索引就是类别的数字，即相应图片中画的数字。咱们也须要测试数据集类别数字的整型值，用下面的方法来计算。

data.test.cls = np.argmax(data.test.labels, axis=1)
data.validation.cls = np.argmax(data.validation.labels, axis=1)复制代码

数据维度

在下面的源码中，有不少地方用到了数据维度。它们只在一个地方定义，所以咱们能够在代码中使用这些数字而不是直接写数字。

# We know that MNIST images are 28 pixels in each dimension.
img_size = 28

# Images are stored in one-dimensional arrays of this length.
img_size_flat = img_size * img_size

# Tuple with height and width of images used to reshape arrays.
img_shape = (img_size, img_size)

# Number of colour channels for the images: 1 channel for gray-scale.
num_channels = 1

# Number of classes, one class for each of 10 digits.
num_classes = 10复制代码

用来绘制图片的帮助函数

这个函数用来在3x3的栅格中画9张图像，而后在每张图像下面写出真实类别和预测类别。

def plot_images(images, cls_true, cls_pred=None):
    assert len(images) == len(cls_true) == 9

    # Create figure with 3x3 sub-plots.
    fig, axes = plt.subplots(3, 3)
    fig.subplots_adjust(hspace=0.3, wspace=0.3)

    for i, ax in enumerate(axes.flat):
        # Plot image.
        ax.imshow(images[i].reshape(img_shape), cmap='binary')

        # Show true and predicted classes.
        if cls_pred is None:
            xlabel = "True: {0}".format(cls_true[i])
        else:
            xlabel = "True: {0}, Pred: {1}".format(cls_true[i], cls_pred[i])

        # Show the classes as the label on the x-axis.
        ax.set_xlabel(xlabel)

        # Remove ticks from the plot.
        ax.set_xticks([])
        ax.set_yticks([])

    # Ensure the plot is shown correctly with multiple plots
    # in a single Notebook cell.
    plt.show()复制代码

绘制几张图像来看看数据是否正确

# Get the first images from the test-set.
images = data.test.images[0:9]

# Get the true classes for those images.
cls_true = data.test.cls[0:9]

# Plot the images and labels using our helper-function above.
plot_images(images=images, cls_true=cls_true)复制代码

TensorFlow图

TensorFlow的所有目的就是使用一个称之为计算图（computational graph）的东西，它会比直接在Python中进行相同计算量要高效得多。TensorFlow比Numpy更高效，由于TensorFlow了解整个须要运行的计算图，然而Numpy只知道某个时间点上惟一的数学运算。

TensorFlow也可以自动地计算须要优化的变量的梯度，使得模型有更好的表现。这是因为图是简单数学表达式的结合，所以整个图的梯度能够用链式法则推导出来。

TensorFlow还能利用多核CPU和GPU，Google也为TensorFlow制造了称为TPUs（Tensor Processing Units）的特殊芯片，它比GPU更快。

一个TensorFlow图由下面几个部分组成，后面会详细描述：

• 占位符变量（Placeholder）用来改变图的输入。
• 模型变量（Model）将会被优化，使得模型表现得更好。
• 模型本质上就是一些数学函数，它根据Placeholder和模型的输入变量来计算一些输出。
• 一个cost度量用来指导变量的优化。
• 一个优化策略会更新模型的变量。

另外，TensorFlow图也包含了一些调试状态，好比用TensorBoard打印log数据，本教程不涉及这些。

占位符 （Placeholder）变量

Placeholder是做为图的输入，咱们每次运行图的时候均可能改变它们。将这个过程称为feeding placeholder变量，后面将会描述这个。

首先咱们为输入图像定义placeholder变量。这让咱们能够改变输入到TensorFlow图中的图像。这也是一个张量（tensor），表明一个多维向量或矩阵。数据类型设置为float32，形状设为[None, img_size_flat]，None表明tensor可能保存着任意数量的图像，每张图象是一个长度为img_size_flat的向量。

x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')复制代码

卷积层但愿x被编码为4维张量，所以咱们须要将它的形状转换至[num_images, img_height, img_width, num_channels]。注意img_height == img_width == img_size，若是第一维的大小设为-1， num_images的大小也会被自动推导出来。转换运算以下：

x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])复制代码

接下来咱们为输入变量x中的图像所对应的真实标签订义placeholder变量。变量的形状是[None, num_classes]，这表明着它保存了任意数量的标签，每一个标签是长度为num_classes的向量，本例中长度为10。

y_true = tf.placeholder(tf.float32, shape=[None, 10], name='y_true')复制代码

咱们也能够为class-number提供一个placeholder，但这里用argmax来计算它。这里只是TensorFlow中的一些操做，没有执行什么运算。

y_true_cls = tf.argmax(y_true, dimension=1)复制代码

神经网络

这一节用PrettyTensor实现卷积神经网络，这要比直接在TensorFlow中实现来得简单，详见教程 #03。

基本思想就是用一个Pretty Tensor object封装输入张量x_image，它有一个添加新卷积层的帮助函数，以此来建立整个神经网络。Pretty Tensor负责变量分配等等。

x_pretty = pt.wrap(x_image)复制代码

如今咱们已经将输入图像装到一个PrettyTensor的object中，再用几行代码就能够添加卷积层和全链接层。

注意，在with代码块中，pt.defaults_scope(activation_fn=tf.nn.relu) 把 activation_fn=tf.nn.relu看成每一个的层参数，所以这些层都用到了 Rectified Linear Units (ReLU) 。defaults_scope使咱们能更方便地修改全部层的参数。

with pt.defaults_scope(activation_fn=tf.nn.relu):
    y_pred, loss = x_pretty.\
        conv2d(kernel=5, depth=16, name='layer_conv1').\
        max_pool(kernel=2, stride=2).\
        conv2d(kernel=5, depth=36, name='layer_conv2').\
        max_pool(kernel=2, stride=2).\
        flatten().\
        fully_connected(size=128, name='layer_fc1').\
        softmax_classifier(num_classes=num_classes, labels=y_true)复制代码

获取权重

下面，咱们要绘制神经网络的权重。当使用Pretty Tensor来建立网络时，层的全部变量都是由Pretty Tensoe间接建立的。所以咱们要从TensorFlow中获取变量。

咱们用layer_conv1 和 layer_conv2表明两个卷积层。这也叫变量做用域（不要与上面描述的defaults_scope混淆了）。PrettyTensor会自动给它为每一个层建立的变量命名，所以咱们能够经过层的做用域名称和变量名来取得某一层的权重。

函数实现有点笨拙，由于咱们不得不用TensorFlow函数get_variable()，它是设计给其余用途的，建立新的变量或重用现有变量。建立下面的帮助函数很简单。

def get_weights_variable(layer_name):
    # Retrieve an existing variable named 'weights' in the scope
    # with the given layer_name.
    # This is awkward because the TensorFlow function was
    # really intended for another purpose.

    with tf.variable_scope(layer_name, reuse=True):
        variable = tf.get_variable('weights')

    return variable复制代码

借助这个帮助函数咱们能够获取变量。这些是TensorFlow的objects。你须要相似的操做来获取变量的内容： contents = session.run(weights_conv1) ，下面会提到这个。

weights_conv1 = get_weights_variable(layer_name='layer_conv1')
weights_conv2 = get_weights_variable(layer_name='layer_conv2')复制代码

优化方法

PrettyTensor给咱们提供了预测类型标签(y_pred)以及一个须要最小化的损失度量，用来提高神经网络分类图片的能力。

PrettyTensor的文档并无说明它的损失度量是用cross-entropy仍是其余的。但如今咱们用AdamOptimizer来最小化损失。

优化过程并非在这里执行。实际上，还没计算任何东西，咱们只是往TensorFlow图中添加了优化器，以便后续操做。

optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)复制代码

性能度量

咱们须要另一些性能度量，来向用户展现这个过程。

首先咱们从神经网络输出的y_pred中计算出预测的类别，它是一个包含10个元素的向量。类别数字是最大元素的索引。

y_pred_cls = tf.argmax(y_pred, dimension=1)复制代码

而后建立一个布尔向量，用来告诉咱们每张图片的真实类别是否与预测类别相同。

correct_prediction = tf.equal(y_pred_cls, y_true_cls)复制代码

上面的计算先将布尔值向量类型转换成浮点型向量，这样子False就变成0，True变成1，而后计算这些值的平均数，以此来计算分类的准确度。

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))复制代码

Saver

为了保存神经网络的变量，咱们建立一个称为Saver-object的对象，它用来保存及恢复TensorFlow图的全部变量。在这里并未保存什么东西，（保存操做）在后面的optimize()函数中完成。

saver = tf.train.Saver()复制代码

因为（保存操做）常间隔着写在（代码）中，所以保存的文件一般称为checkpoints。

这是用来保存或恢复数据的文件夹。

save_dir = 'checkpoints/'复制代码

若是文件夹不存在则建立。

if not os.path.exists(save_dir):
    os.makedirs(save_dir)复制代码

这是保存checkpoint文件的路径。

save_path = os.path.join(save_dir, 'best_validation')复制代码

运行TensorFlow

建立TensorFlow会话（session）

一旦建立了TensorFlow图，咱们须要建立一个TensorFlow会话，用来运行图。

session = tf.Session()复制代码

初始化变量

变量weights和biases在优化以前须要先进行初始化。咱们写一个简单的封装函数，后面会再次调用。

def init_variables():
    session.run(tf.global_variables_initializer())复制代码

运行函数来初始化变量。

init_variables()复制代码

用来优化迭代的帮助函数

在训练集中有50,000张图。用这些图像计算模型的梯度会花不少时间。所以咱们利用随机梯度降低的方法，它在优化器的每次迭代里只用到了一小部分的图像。

若是内存耗尽致使电脑死机或变得很慢，你应该试着减小这些数量，但同时可能还须要更优化的迭代。

train_batch_size = 64复制代码

每迭代100次下面的优化函数，会计算一次验证集上的分类准确率。若是过了1000次迭代验证准确率仍是没有提高，就中止优化。咱们须要一些变量来跟踪这个过程。

# Best validation accuracy seen so far.
best_validation_accuracy = 0.0

# Iteration-number for last improvement to validation accuracy.
last_improvement = 0

# Stop optimization if no improvement found in this many iterations.
require_improvement = 1000复制代码

函数用来执行必定数量的优化迭代，以此来逐渐改善网络层的变量。在每次迭代中，会从训练集中选择新的一批数据，而后TensorFlow在这些训练样本上执行优化。每100次迭代会打印出（信息），同时计算验证准确率，若是效果有提高的话会将它保存至文件。

# Counter for total number of iterations performed so far.
total_iterations = 0

def optimize(num_iterations):
    # Ensure we update the global variables rather than local copies.
    global total_iterations
    global best_validation_accuracy
    global last_improvement

    # Start-time used for printing time-usage below.
    start_time = time.time()

    for i in range(num_iterations):

        # Increase the total number of iterations performed.
        # It is easier to update it in each iteration because
        # we need this number several times in the following.
        total_iterations += 1

        # Get a batch of training examples.
        # x_batch now holds a batch of images and
        # y_true_batch are the true labels for those images.
        x_batch, y_true_batch = data.train.next_batch(train_batch_size)

        # Put the batch into a dict with the proper names
        # for placeholder variables in the TensorFlow graph.
        feed_dict_train = {x: x_batch,
                           y_true: y_true_batch}

        # Run the optimizer using this batch of training data.
        # TensorFlow assigns the variables in feed_dict_train
        # to the placeholder variables and then runs the optimizer.
        session.run(optimizer, feed_dict=feed_dict_train)

        # Print status every 100 iterations and after last iteration.
        if (total_iterations % 100 == 0) or (i == (num_iterations - 1)):

            # Calculate the accuracy on the training-batch.
            acc_train = session.run(accuracy, feed_dict=feed_dict_train)

            # Calculate the accuracy on the validation-set.
            # The function returns 2 values but we only need the first.
            acc_validation, _ = validation_accuracy()

            # If validation accuracy is an improvement over best-known.
            if acc_validation > best_validation_accuracy:
                # Update the best-known validation accuracy.
                best_validation_accuracy = acc_validation

                # Set the iteration for the last improvement to current.
                last_improvement = total_iterations

                # Save all variables of the TensorFlow graph to file.
                saver.save(sess=session, save_path=save_path)

                # A string to be printed below, shows improvement found.
                improved_str = '*'
            else:
                # An empty string to be printed below.
                # Shows that no improvement was found.
                improved_str = ''

            # Status-message for printing.
            msg = "Iter: {0:>6}, Train-Batch Accuracy: {1:>6.1%}, Validation Acc: {2:>6.1%} {3}"

            # Print it.
            print(msg.format(i + 1, acc_train, acc_validation, improved_str))

        # If no improvement found in the required number of iterations.
        if total_iterations - last_improvement > require_improvement:
            print("No improvement found in a while, stopping optimization.")

            # Break out from the for-loop.
            break

    # Ending time.
    end_time = time.time()

    # Difference between start and end-times.
    time_dif = end_time - start_time

    # Print the time-usage.
    print("Time usage: " + str(timedelta(seconds=int(round(time_dif)))))复制代码

用来绘制错误样本的帮助函数

函数用来绘制测试集中被误分类的样本。

def plot_example_errors(cls_pred, correct):
    # This function is called from print_test_accuracy() below.

    # cls_pred is an array of the predicted class-number for
    # all images in the test-set.

    # correct is a boolean array whether the predicted class
    # is equal to the true class for each image in the test-set.

    # Negate the boolean array.
    incorrect = (correct == False)

    # Get the images from the test-set that have been
    # incorrectly classified.
    images = data.test.images[incorrect]

    # Get the predicted classes for those images.
    cls_pred = cls_pred[incorrect]

    # Get the true classes for those images.
    cls_true = data.test.cls[incorrect]

    # Plot the first 9 images.
    plot_images(images=images[0:9],
                cls_true=cls_true[0:9],
                cls_pred=cls_pred[0:9])复制代码

绘制混淆（confusion）矩阵的帮助函数

def plot_confusion_matrix(cls_pred):
    # This is called from print_test_accuracy() below.

    # cls_pred is an array of the predicted class-number for
    # all images in the test-set.

    # Get the true classifications for the test-set.
    cls_true = data.test.cls

    # Get the confusion matrix using sklearn.
    cm = confusion_matrix(y_true=cls_true,
                          y_pred=cls_pred)

    # Print the confusion matrix as text.
    print(cm)

    # Plot the confusion matrix as an image.
    plt.matshow(cm)

    # Make various adjustments to the plot.
    plt.colorbar()
    tick_marks = np.arange(num_classes)
    plt.xticks(tick_marks, range(num_classes))
    plt.yticks(tick_marks, range(num_classes))
    plt.xlabel('Predicted')
    plt.ylabel('True')

    # Ensure the plot is shown correctly with multiple plots
    # in a single Notebook cell.
    plt.show()复制代码

计算分类的帮助函数

这个函数用来计算图像的预测类别，同时返回一个表明每张图像分类是否正确的布尔数组。

因为计算可能会耗费太多内存，就分批处理。若是你的电脑死机了，试着下降batch-size。

# Split the data-set in batches of this size to limit RAM usage.
batch_size = 256

def predict_cls(images, labels, cls_true):
    # Number of images.
    num_images = len(images)

    # Allocate an array for the predicted classes which
    # will be calculated in batches and filled into this array.
    cls_pred = np.zeros(shape=num_images, dtype=np.int)

    # Now calculate the predicted classes for the batches.
    # We will just iterate through all the batches.
    # There might be a more clever and Pythonic way of doing this.

    # The starting index for the next batch is denoted i.
    i = 0

    while i < num_images:
        # The ending index for the next batch is denoted j.
        j = min(i + batch_size, num_images)

        # Create a feed-dict with the images and labels
        # between index i and j.
        feed_dict = {x: images[i:j, :],
                     y_true: labels[i:j, :]}

        # Calculate the predicted class using TensorFlow.
        cls_pred[i:j] = session.run(y_pred_cls, feed_dict=feed_dict)

        # Set the start-index for the next batch to the
        # end-index of the current batch.
        i = j

    # Create a boolean array whether each image is correctly classified.
    correct = (cls_true == cls_pred)

    return correct, cls_pred复制代码

计算测试集上的预测类别。

def predict_cls_test():
    return predict_cls(images = data.test.images,
                       labels = data.test.labels,
                       cls_true = data.test.cls)复制代码

计算验证集上的预测类别。

def predict_cls_validation():
    return predict_cls(images = data.validation.images,
                       labels = data.validation.labels,
                       cls_true = data.validation.cls)复制代码

分类准确率的帮助函数

这个函数计算了给定布尔数组的分类准确率，布尔数组表示每张图像是否被正确分类。好比， cls_accuracy([True, True, False, False, False]) = 2/5 = 0.4。

def cls_accuracy(correct):
    # Calculate the number of correctly classified images.
    # When summing a boolean array, False means 0 and True means 1.
    correct_sum = correct.sum()

    # Classification accuracy is the number of correctly classified
    # images divided by the total number of images in the test-set.
    acc = float(correct_sum) / len(correct)

    return acc, correct_sum复制代码

计算验证集上的分类准确率。

def validation_accuracy():
    # Get the array of booleans whether the classifications are correct
    # for the validation-set.
    # The function returns two values but we only need the first.
    correct, _ = predict_cls_validation()

    # Calculate the classification accuracy and return it.
    return cls_accuracy(correct)复制代码

展现性能的帮助函数

函数用来打印测试集上的分类准确率。

为测试集上的全部图片计算分类会花费一段时间，所以咱们直接从这个函数里调用上面的函数，这样就不用每一个函数都从新计算分类。

def print_test_accuracy(show_example_errors=False, show_confusion_matrix=False):

    # For all the images in the test-set,
    # calculate the predicted classes and whether they are correct.
    correct, cls_pred = predict_cls_test()

    # Classification accuracy and the number of correct classifications.
    acc, num_correct = cls_accuracy(correct)

    # Number of images being classified.
    num_images = len(correct)

    # Print the accuracy.
    msg = "Accuracy on Test-Set: {0:.1%} ({1} / {2})"
    print(msg.format(acc, num_correct, num_images))

    # Plot some examples of mis-classifications, if desired.
    if show_example_errors:
        print("Example errors:")
        plot_example_errors(cls_pred=cls_pred, correct=correct)

    # Plot the confusion matrix, if desired.
    if show_confusion_matrix:
        print("Confusion Matrix:")
        plot_confusion_matrix(cls_pred=cls_pred)复制代码

绘制卷积权重的帮助函数

def plot_conv_weights(weights, input_channel=0):
    # Assume weights are TensorFlow ops for 4-dim variables
    # e.g. weights_conv1 or weights_conv2.

    # Retrieve the values of the weight-variables from TensorFlow.
    # A feed-dict is not necessary because nothing is calculated.
    w = session.run(weights)

    # Print mean and standard deviation.
    print("Mean: {0:.5f}, Stdev: {1:.5f}".format(w.mean(), w.std()))

    # Get the lowest and highest values for the weights.
    # This is used to correct the colour intensity across
    # the images so they can be compared with each other.
    w_min = np.min(w)
    w_max = np.max(w)

    # Number of filters used in the conv. layer.
    num_filters = w.shape[3]

    # Number of grids to plot.
    # Rounded-up, square-root of the number of filters.
    num_grids = math.ceil(math.sqrt(num_filters))

    # Create figure with a grid of sub-plots.
    fig, axes = plt.subplots(num_grids, num_grids)

    # Plot all the filter-weights.
    for i, ax in enumerate(axes.flat):
        # Only plot the valid filter-weights.
        if i<num_filters:
            # Get the weights for the i'th filter of the input channel.
            # The format of this 4-dim tensor is determined by the
            # TensorFlow API. See Tutorial #02 for more details.
            img = w[:, :, input_channel, i]

            # Plot image.
            ax.imshow(img, vmin=w_min, vmax=w_max,
                      interpolation='nearest', cmap='seismic')

        # Remove ticks from the plot.
        ax.set_xticks([])
        ax.set_yticks([])

    # Ensure the plot is shown correctly with multiple plots
    # in a single Notebook cell.
    plt.show()复制代码

优化以前的性能

测试集上的准确度很低，这是因为模型只作了初始化，并没作任何优化，因此它只是对图像作随机分类。

print_test_accuracy()复制代码

Accuracy on Test-Set: 8.5% (849 / 10000)

卷积权重是随机的，但也很难把它与下面优化过的权重区分开来。这里也展现了平均值和标准差，所以咱们能够看看是否有差异。

plot_conv_weights(weights=weights_conv1)复制代码

Mean: 0.00880, Stdev: 0.28635

10,000次优化迭代后的性能

如今咱们进行了10,000次优化迭代，而且，当通过1000次迭代验证集上的性能却没有提高时就中止优化。

星号 * 表明验证集上的分类准确度有提高。

optimize(num_iterations=10000)复制代码

Iter: 100, Train-Batch Accuracy: 84.4%, Validation Acc: 85.2%
Iter: 200, Train-Batch Accuracy: 92.2%, Validation Acc: 91.5%
Iter: 300, Train-Batch Accuracy: 95.3%, Validation Acc: 93.7%
Iter: 400, Train-Batch Accuracy: 92.2%, Validation Acc: 94.3%
Iter: 500, Train-Batch Accuracy: 98.4%, Validation Acc: 94.7%
Iter: 600, Train-Batch Accuracy: 93.8%, Validation Acc: 94.7%
Iter: 700, Train-Batch Accuracy: 98.4%, Validation Acc: 95.6%
Iter: 800, Train-Batch Accuracy: 100.0%, Validation Acc: 96.3%
Iter: 900, Train-Batch Accuracy: 98.4%, Validation Acc: 96.4%
Iter: 1000, Train-Batch Accuracy: 100.0%, Validation Acc: 96.9%
Iter: 1100, Train-Batch Accuracy: 96.9%, Validation Acc: 97.0%
Iter: 1200, Train-Batch Accuracy: 93.8%, Validation Acc: 97.0%
Iter: 1300, Train-Batch Accuracy: 92.2%, Validation Acc: 97.2%
Iter: 1400, Train-Batch Accuracy: 100.0%, Validation Acc: 97.3%
Iter: 1500, Train-Batch Accuracy: 96.9%, Validation Acc: 97.4%
Iter: 1600, Train-Batch Accuracy: 100.0%, Validation Acc: 97.7%
Iter: 1700, Train-Batch Accuracy: 100.0%, Validation Acc: 97.8%
Iter: 1800, Train-Batch Accuracy: 98.4%, Validation Acc: 97.7%
Iter: 1900, Train-Batch Accuracy: 98.4%, Validation Acc: 98.1%
Iter: 2000, Train-Batch Accuracy: 95.3%, Validation Acc: 98.0%
Iter: 2100, Train-Batch Accuracy: 98.4%, Validation Acc: 97.9%
Iter: 2200, Train-Batch Accuracy: 100.0%, Validation Acc: 98.0%
Iter: 2300, Train-Batch Accuracy: 96.9%, Validation Acc: 98.1%
Iter: 2400, Train-Batch Accuracy: 93.8%, Validation Acc: 98.1%
Iter: 2500, Train-Batch Accuracy: 98.4%, Validation Acc: 98.2%
Iter: 2600, Train-Batch Accuracy: 98.4%, Validation Acc: 98.0%
Iter: 2700, Train-Batch Accuracy: 98.4%, Validation Acc: 98.0%
Iter: 2800, Train-Batch Accuracy: 96.9%, Validation Acc: 98.1%
Iter: 2900, Train-Batch Accuracy: 96.9%, Validation Acc: 98.2%
Iter: 3000, Train-Batch Accuracy: 98.4%, Validation Acc: 98.2%
Iter: 3100, Train-Batch Accuracy: 100.0%, Validation Acc: 98.1%
Iter: 3200, Train-Batch Accuracy: 100.0%, Validation Acc: 98.3%
Iter: 3300, Train-Batch Accuracy: 98.4%, Validation Acc: 98.4%
Iter: 3400, Train-Batch Accuracy: 95.3%, Validation Acc: 98.0%
Iter: 3500, Train-Batch Accuracy: 98.4%, Validation Acc: 98.3%
Iter: 3600, Train-Batch Accuracy: 100.0%, Validation Acc: 98.5%
Iter: 3700, Train-Batch Accuracy: 98.4%, Validation Acc: 98.3%
Iter: 3800, Train-Batch Accuracy: 96.9%, Validation Acc: 98.1%
Iter: 3900, Train-Batch Accuracy: 96.9%, Validation Acc: 98.5%
Iter: 4000, Train-Batch Accuracy: 100.0%, Validation Acc: 98.4%
Iter: 4100, Train-Batch Accuracy: 100.0%, Validation Acc: 98.5%
Iter: 4200, Train-Batch Accuracy: 100.0%, Validation Acc: 98.3%
Iter: 4300, Train-Batch Accuracy: 100.0%, Validation Acc: 98.6%
Iter: 4400, Train-Batch Accuracy: 96.9%, Validation Acc: 98.4%
Iter: 4500, Train-Batch Accuracy: 98.4%, Validation Acc: 98.5%
Iter: 4600, Train-Batch Accuracy: 98.4%, Validation Acc: 98.5%
Iter: 4700, Train-Batch Accuracy: 98.4%, Validation Acc: 98.4%
Iter: 4800, Train-Batch Accuracy: 100.0%, Validation Acc: 98.8% *
Iter: 4900, Train-Batch Accuracy: 100.0%, Validation Acc: 98.8%
Iter: 5000, Train-Batch Accuracy: 98.4%, Validation Acc: 98.6%
Iter: 5100, Train-Batch Accuracy: 98.4%, Validation Acc: 98.6%
Iter: 5200, Train-Batch Accuracy: 100.0%, Validation Acc: 98.6%
Iter: 5300, Train-Batch Accuracy: 96.9%, Validation Acc: 98.5%
Iter: 5400, Train-Batch Accuracy: 98.4%, Validation Acc: 98.7%
Iter: 5500, Train-Batch Accuracy: 98.4%, Validation Acc: 98.6%
Iter: 5600, Train-Batch Accuracy: 100.0%, Validation Acc: 98.4%
Iter: 5700, Train-Batch Accuracy: 100.0%, Validation Acc: 98.6%
Iter: 5800, Train-Batch Accuracy: 100.0%, Validation Acc: 98.7%
No improvement found in a while, stopping optimization.
Time usage: 0:00:28

print_test_accuracy(show_example_errors=True,
                    show_confusion_matrix=True)复制代码

Accuracy on Test-Set: 98.4% (9842 / 10000)
Example errors:

Confusion Matrix:
[[ 974 0 0 0 0 1 2 0 2 1]
[ 0 1127 2 2 0 0 1 0 3 0]
[ 4 4 1012 4 1 0 0 3 4 0]
[ 0 0 1 1005 0 2 0 0 2 0]
[ 1 0 1 0 961 0 2 0 3 14]
[ 2 0 1 6 0 880 1 0 1 1]
[ 4 2 0 1 3 4 942 0 2 0]
[ 1 1 8 6 1 0 0 994 1 16]
[ 6 0 1 4 1 1 1 2 952 6]
[ 3 3 0 3 2 2 0 0 1 995]]

如今卷积权重是通过优化的。将这些与上面的随机权重进行对比。它们看起来基本相同。实际上，一开始我觉得程序有bug，由于优化先后的权重看起来差很少。

但保存图像，并排着比较它们（你能够右键保存）。你会发现二者有细微的不一样。

平均值和标准差也有一点变化，所以优化过的权重确定是不同的。

plot_conv_weights(weights=weights_conv1)复制代码

Mean: 0.02895, Stdev: 0.29949

再次初始化变量

再一次用随机值来初始化全部神经网络变量。

init_variables()复制代码

这意味着神经网络又是彻底随机地对图片进行分类，因为只是随机的猜想因此分类准确率很低。

print_test_accuracy()复制代码

Accuracy on Test-Set: 13.4% (1341 / 10000)

卷积权重看起来应该与上面的不一样。

plot_conv_weights(weights=weights_conv1)复制代码

Mean: -0.01086, Stdev: 0.28023

恢复最好的变量

从新载入在优化过程当中保存到文件的全部变量。

saver.restore(sess=session, save_path=save_path)复制代码

使用以前保存的那些变量，分类准确率又提升了。

注意，准确率与以前相比可能会有细微的上升或降低，这是因为文件里的变量是用来最大化验证集上的分类准确率，但在保存文件以后，又进行了1000次的优化迭代，所以这是两组有轻微不一样的变量的结果。有时这会致使测试集上更好或更差的表现。

print_test_accuracy(show_example_errors=True,
                    show_confusion_matrix=True)复制代码

Accuracy on Test-Set: 98.3% (9826 / 10000)
Example errors:

Confusion Matrix:
[[ 973 0 0 0 0 0 2 0 3 2]
[ 0 1124 2 2 0 0 3 0 4 0]
[ 2 1 1027 0 0 0 0 1 1 0]
[ 0 0 1 1005 0 2 0 0 2 0]
[ 0 0 3 0 968 0 1 0 3 7]
[ 2 0 1 9 0 871 3 0 3 3]
[ 4 2 1 0 3 3 939 0 6 0]
[ 1 3 19 11 2 0 0 972 2 18]
[ 6 0 3 5 1 0 1 2 951 5]
[ 3 3 0 1 4 1 0 0 1 996]]

卷积权重也与以前显示的图几乎相同，一样，因为多作了1000次优化迭代，两者并不是彻底同样。

plot_conv_weights(weights=weights_conv1)复制代码

Mean: 0.02792, Stdev: 0.29822

关闭TensorFlow会话

如今咱们已经用TensorFlow完成了任务，关闭session，释放资源。

# This has been commented out in case you want to modify and experiment
# with the Notebook without having to restart it.
# session.close()复制代码

总结

这篇教程描述了在TensorFlow中如何保存并恢复神经网络的变量。它有许多用处。好比，当你用神经网络来识别图像的时候，只须要训练网络一次，而后能够在其余电脑上完成开发工做。

checkpoint的另外一个用处是，若是你有一个很是大的神经网络和数据集，就可能会在中间保存一些checkpoints来避免电脑死机，这样，你就能够在最近的checkpoint开始优化而不是重头开始。

本教程也展现了如何用验证集来进行所谓的Early Stopping，若是没有下降验证错误优化就会终止。这在神经网络出现过拟合以及开始学习训练集中的噪声时颇有用；不过这在本教程的神经网络和MNIST数据集中并非什么大问题。

还有一个有趣的现象，最优化时卷积权重（或者叫滤波）的变化很小，即便网络的性能从随机猜想提升到近乎完美的分类。奇怪的是随机的权重好像已经足够好了。你认为为何会有这种现象?

练习

下面使一些可能会让你提高TensorFlow技能的一些建议练习。为了学习如何更合适地使用TensorFlow，实践经验是很重要的。

在你对这个Notebook进行修改以前，可能须要先备份一下。

• 在通过1000次迭代而性能没有提高时，优化就终止了。这样够吗？你能想出一个更好地进行Early Stopping的方法么？试着实现它。
• 若是checkpoint文件已经存在了，载入它而不是作优化。
• 每100次优化迭代保存一次checkpoint。经过saver.latest_checkpoint()取回最新的（保存点）。为何保存多个checkpoints而不是只保存最近的一个？
• 试着改变神经网络，好比添加其余层。当你从不一样的网络中从新载入变量会出现什么问题？
• 用plot_conv_weights()函数在优化先后画出第二个卷积层的权重。它们几乎相同的么？
• 你认为优化过的卷积权重为何与随机初始化的（权重）几乎相同？
• 不看源码，本身重写程序。
• 向朋友解释程序如何工做。