优达学城深度学习之六——TensorFlow卷积神经网络

TensorFlow卷积层

TensorFlow 提供了 tf.nn.conv2d() 和 tf.nn.bias_add() 函数来创建你自己的卷积层。

      # Output depth
      k_output = 64
      # Image Properties
      image_width = 10
      image_height = 10
      color_channels = 3
      # Convolution filter
      filter_size_width = 5
      filter_size_height = 5
      # Input/Image
      input = tf.placeholder(
          tf.float32,
          shape=[None, image_height, image_width, color_channels])
      # Weight and bias
      weight = tf.Variable(tf.truncated_normal(
          [filter_size_height, filter_size_width, color_channels, k_output]))
      bias = tf.Variable(tf.zeros(k_output))
      # Apply Convolution
      conv_layer = tf.nn.conv2d(input, weight, strides=[1, 2, 2, 1], padding='SAME')
      # Add bias
      conv_layer = tf.nn.bias_add(conv_layer, bias)
      # Apply activation function
      conv_layer = tf.nn.relu(conv_layer)
  
 

TensorFlow最大池化提供函数：tf.nn.max_pool()

      conv_layer = tf.nn.conv2d(input, weight, strides=[1, 2, 2, 1], padding='SAME')
      conv_layer = tf.nn.bias_add(conv_layer, bias)
      conv_layer = tf.nn.relu(conv_layer)
      # Apply Max Pooling
      conv_layer = tf.nn.max_pool(
          conv_layer,
          ksize=[1, 2, 2, 1],
          strides=[1, 2, 2, 1],
          padding='SAME')
  
 

tf.nn.max_pool() 函数实现最大池化时， ksize参数是滤波器大小，strides参数是步长。2x2 的滤波器配合 2x2 的步长是常用设定。

ksize 和 strides 参数也被构建为四个元素的列表，每个元素对应 input tensor 的一个维度 ([batch, height, width, channels])，对 ksize 和 strides 来说，batch 和 channel 通常都设置成 1

注意：池化层的输出深度与输入的深度相同。另外池化操作是分别应用到每一个深度切片层。

池化对应的代码：

      input = tf.placeholder(tf.float32, (None, 4, 4, 5))
      filter_shape = [1, 2, 2, 1]
      strides = [1, 2, 2, 1]
      padding = 'VALID'
      pool = tf.nn.max_pool(input, filter_shape, strides, padding)
  
 

pool 的输出维度是 [1, 2, 2, 5]，即使把 padding 改成 'SAME' 也是一样。

TensorFlow中的卷积神经网络

你从之前的课程中见过这节课的代码。这里我们导入 MNIST 数据集，用一个方便的函数完成对数据集的 batch，scale 和 One-Hot编码。

      from tensorflow.examples.tutorials.mnist import input_data
      mnist = input_data.read_data_sets(".", one_hot=True, reshape=False)
      import tensorflow as tf
      # Parameters
      # 参数
      learning_rate = 0.00001
      epochs = 10
      batch_size = 128
      # Number of samples to calculate validation and accuracy
      # Decrease this if you're running out of memory to calculate accuracy
      # 用来验证和计算准确率的样本数
      # 如果内存不够，可以调小这个数字
      test_valid_size = 256
      # Network Parameters
      # 神经网络参数
      n_classes = 10  # MNIST total classes (0-9 digits)
      dropout = 0.75  # Dropout, probability to keep units
      Weights and Biases
      # Store layers weight & bias
      weights = {
         'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
         'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
         'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),
         'out': tf.Variable(tf.random_normal([1024, n_classes]))}
      biases = {
         'bc1': tf.Variable(tf.random_normal([32])),
         'bc2': tf.Variable(tf.random_normal([64])),
         'bd1': tf.Variable(tf.random_normal([1024])),
         'out': tf.Variable(tf.random_normal([n_classes]))}
      def conv2d(x,w,b,strides=1):
          x=tf.nn.conv2d(tf.Variable(x,w,strides=[1, strides, strides, 1], padding='SAME')
          x=tf.nn.add_add(x,b)
         return tf.nn.relu(x)
  
 

在 TensorFlow 中，strides 是一个4个元素的序列；第一个位置表示 stride 的 batch 参数，最后一个位置表示 stride 的特征(feature)参数。最好的移除 batch 和特征(feature)的方法是你直接在数据集中把他们忽略，而不是使用 stride。要使用所有的 batch 和特征(feature)，你可以把第一个和最后一个元素设成1。

中间两个元素指纵向(height)和横向(width)的 stride，之前也提到过 stride 通常是正方形，height = width。当别人说 stride 是 3 的时候，他们意思是 tf.nn.conv2d(x, W, strides=[1, 3, 3, 1])。

为了更简洁，这里的代码用了tf.nn.bias_add() 来添加偏置。 tf.add() 这里不能使用，因为 tensors 的维度不同。

模型建立

      def conv_net(x, weights, biases, dropout):
         # Layer 1 - 28*28*1 to 14*14*32
          conv1 = conv2d(x, weights['wc1'], biases['bc1'])
          conv1 = maxpool2d(conv1, k=2)
         # Layer 2 - 14*14*32 to 7*7*64
          conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
          conv2 = maxpool2d(conv2, k=2)
         # Fully connected layer - 7*7*64 to 1024
          fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
          fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
          fc1 = tf.nn.relu(fc1)
          fc1 = tf.nn.dropout(fc1, dropout)
         # Output Layer - class prediction - 1024 to 10
          out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
         return out
  
 

session运行

      # tf Graph input
      x = tf.placeholder(tf.float32, [None, 28, 28, 1])
      y = tf.placeholder(tf.float32, [None, n_classes])
      keep_prob = tf.placeholder(tf.float32)
      # Model
      logits = conv_net(x, weights, biases, keep_prob)
      # Define loss and optimizer
      cost = tf.reduce_mean(\
          tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
      optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\
          .minimize(cost)
      # Accuracy
      correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
      accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
      # Initializing the variables
      init = tf. global_variables_initializer()
      # Launch the graph
      with tf.Session() as sess:
          sess.run(init)
         for epoch in range(epochs):
             for batch in range(mnist.train.num_examples//batch_size):
                  batch_x, batch_y = mnist.train.next_batch(batch_size)
                  sess.run(optimizer, feed_dict={
                      x: batch_x,
                      y: batch_y,
                      keep_prob: dropout})
                 # Calculate batch loss and accuracy
                  loss = sess.run(cost, feed_dict={
                      x: batch_x,
                      y: batch_y,
                      keep_prob: 1.})
                  valid_acc = sess.run(accuracy, feed_dict={
                      x: mnist.validation.images[:test_valid_size],
                      y: mnist.validation.labels[:test_valid_size],
                      keep_prob: 1.})
                 print('Epoch {:>2}, Batch {:>3} -'
                       'Loss: {:>10.4f} Validation Accuracy: {:.6f}'.format(
                      epoch + 1,
                      batch + 1,
                      loss,
                      valid_acc))
         # Calculate Test Accuracy
          test_acc = sess.run(accuracy, feed_dict={
              x: mnist.test.images[:test_valid_size],
              y: mnist.test.labels[:test_valid_size],
              keep_prob: 1.})
         print('Testing Accuracy: {}'.format(test_acc))
  
 

使用tensor做卷积

让我们用所学知识在 TensorFlow 里构建真的 CNNs。在下面的练习中，你需要设定卷积核滤波器（filters）的维度，weight，bias。这在很大程度上来说是 TensorFlow CNNs 最难的部分。一旦你知道如何设置这些属性的大小，应用 CNNs 会很方便。

这些也是需要你回顾的：

1. TensorFlow 变量。
2. Truncated 正态分布 - 在 TensorFlow 中你需要在一个正态分布的区间中初始化你的权值。

3. 根据输入大小、滤波器大小，来决定输出维度（如下所示）。你用这个来决定滤波器应该是什么样：

      new_height = (input_height - filter_height + 2 * P)/S + 1
      new_width = (input_width - filter_width + 2 * P)/S + 1
  
 

      """
      Setup the strides, padding and filter weight/bias such that
      the output shape is (1, 2, 2, 3).
      """
      import tensorflow as tf
      import numpy as np
      # `tf.nn.conv2d` requires the input be 4D (batch_size, height, width, depth)
      # (1, 4, 4, 1)
      x = np.array([
          [0, 1, 0.5, 10],
          [2, 2.5, 1, -8],
          [4, 0, 5, 6],
          [15, 1, 2, 3]], dtype=np.float32).reshape((1, 4, 4, 1))
      X = tf.constant(x)
      def conv2d(input):
         # Filter (weights and bias)
         # The shape of the filter weight is (height, width, input_depth, output_depth)
         # The shape of the filter bias is (output_depth,)
         # TODO: Define the filter weights `F_W` and filter bias `F_b`.
         # NOTE: Remember to wrap them in `tf.Variable`, they are trainable parameters after all.
          F_W = tf.Variable(tf.truncated_normal((2,2,1,3)))
          F_b = tf.Variable(tf.zeros(3))
         # TODO: Set the stride for each dimension (batch_size, height, width, depth)
          strides = [1, 2, 2, 1]
         # TODO: set the padding, either 'VALID' or 'SAME'.
          padding = 'VALID'
         # https://www.tensorflow.org/versions/r0.11/api_docs/python/nn.html#conv2d
         # `tf.nn.conv2d` does not include the bias computation so we have to add it ourselves after.
         return tf.nn.conv2d(input, F_W, strides, padding) + F_b
      out = conv2d(X)
  
 

在TensorFlow使用池化层

      """
      Set the values to `strides` and `ksize` such that
      the output shape after pooling is (1, 2, 2, 1).
      """
      import tensorflow as tf
      import numpy as np
      # `tf.nn.max_pool` requires the input be 4D (batch_size, height, width, depth)
      # (1, 4, 4, 1)
      x = np.array([
          [0, 1, 0.5, 10],
          [2, 2.5, 1, -8],
          [4, 0, 5, 6],
          [15, 1, 2, 3]], dtype=np.float32).reshape((1, 4, 4, 1))
      X = tf.constant(x)
      def maxpool(input):
         # TODO: Set the ksize (filter size) for each dimension (batch_size, height, width, depth)
          ksize = [1, 2, 2, 1]
         # TODO: Set the stride for each dimension (batch_size, height, width, depth)
          strides = [1, 2, 2, 1]
         # TODO: set the padding, either 'VALID' or 'SAME'.
          padding = 'SAME'
         # https://www.tensorflow.org/versions/r0.11/api_docs/python/nn.html#max_pool
         return tf.nn.max_pool(input, ksize, strides, padding)
      out = maxpool(X)
  
 

自编码器

自编码器是一种执行数据压缩的网络架构。其中压缩和解压功能是从数据本身学习来，而非人工设计的。一般思路如下：

编码器一般用在图像降噪、JPG等文件中。

文章来源: blog.csdn.net，作者：小小谢先生，版权归原作者所有，如需转载，请联系作者。

原文链接：blog.csdn.net/xiewenrui1996/article/details/89929940