优达学城深度学习之六——TensorFlow卷积神经网络
【摘要】
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 depthk_output = 64 # Image Propertiesimage_width = 10image_he...
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。
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# Output depth
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k_output = 64
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# Image Properties
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image_width = 10
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image_height = 10
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color_channels = 3
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# Convolution filter
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filter_size_width = 5
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filter_size_height = 5
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# Input/Image
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input = tf.placeholder(
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tf.float32,
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shape=[None, image_height, image_width, color_channels])
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# Weight and bias
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weight = tf.Variable(tf.truncated_normal(
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[filter_size_height, filter_size_width, color_channels, k_output]))
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bias = tf.Variable(tf.zeros(k_output))
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# Apply Convolution
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conv_layer = tf.nn.conv2d(input, weight, strides=[1, 2, 2, 1], padding='SAME')
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# Add bias
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conv_layer = tf.nn.bias_add(conv_layer, bias)
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# Apply activation function
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conv_layer = tf.nn.relu(conv_layer)
TensorFlow最大池化提供函数：tf.nn.max_pool()
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conv_layer = tf.nn.conv2d(input, weight, strides=[1, 2, 2, 1], padding='SAME')
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conv_layer = tf.nn.bias_add(conv_layer, bias)
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conv_layer = tf.nn.relu(conv_layer)
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# Apply Max Pooling
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conv_layer = tf.nn.max_pool(
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conv_layer,
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ksize=[1, 2, 2, 1],
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strides=[1, 2, 2, 1],
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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：
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input = tf.placeholder(tf.float32, (None, 4, 4, 5))
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filter_shape = [1, 2, 2, 1]
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strides = [1, 2, 2, 1]
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padding = 'VALID'
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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。
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from tensorflow.examples.tutorials.mnist import input_data
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mnist = input_data.read_data_sets(".", one_hot=True, reshape=False)
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import tensorflow as tf
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# Parameters
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# 参数
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learning_rate = 0.00001
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epochs = 10
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batch_size = 128
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# Number of samples to calculate validation and accuracy
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# Decrease this if you're running out of memory to calculate accuracy
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# 用来验证和计算准确率的样本数
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# 如果内存不够，可以调小这个数字
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test_valid_size = 256
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# Network Parameters
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# 神经网络参数
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n_classes = 10 # MNIST total classes (0-9 digits)
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dropout = 0.75 # Dropout, probability to keep units
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Weights and Biases
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# Store layers weight & bias
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weights = {
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'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
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'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
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'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),
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'out': tf.Variable(tf.random_normal([1024, n_classes]))}
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biases = {
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'bc1': tf.Variable(tf.random_normal([32])),
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'bc2': tf.Variable(tf.random_normal([64])),
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'bd1': tf.Variable(tf.random_normal([1024])),
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'out': tf.Variable(tf.random_normal([n_classes]))}
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def conv2d(x,w,b,strides=1):
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x=tf.nn.conv2d(tf.Variable(x,w,strides=[1, strides, strides, 1], padding='SAME')
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x=tf.nn.add_add(x,b)
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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
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def conv_net(x, weights, biases, dropout):
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# Layer 1 - 28*28*1 to 14*14*32
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conv1 = conv2d(x, weights['wc1'], biases['bc1'])
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conv1 = maxpool2d(conv1, k=2)
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# Layer 2 - 14*14*32 to 7*7*64
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conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
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conv2 = maxpool2d(conv2, k=2)
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# Fully connected layer - 7*7*64 to 1024
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fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
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fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
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fc1 = tf.nn.relu(fc1)
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fc1 = tf.nn.dropout(fc1, dropout)
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# Output Layer - class prediction - 1024 to 10
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out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
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return out
session运行
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# tf Graph input
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x = tf.placeholder(tf.float32, [None, 28, 28, 1])
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y = tf.placeholder(tf.float32, [None, n_classes])
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keep_prob = tf.placeholder(tf.float32)
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# Model
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logits = conv_net(x, weights, biases, keep_prob)
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# Define loss and optimizer
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cost = tf.reduce_mean(\
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tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
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optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\
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.minimize(cost)
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# Accuracy
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correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
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accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
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# Initializing the variables
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init = tf. global_variables_initializer()
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# Launch the graph
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with tf.Session() as sess:
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sess.run(init)
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for epoch in range(epochs):
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for batch in range(mnist.train.num_examples//batch_size):
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batch_x, batch_y = mnist.train.next_batch(batch_size)
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sess.run(optimizer, feed_dict={
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x: batch_x,
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y: batch_y,
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keep_prob: dropout})
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# Calculate batch loss and accuracy
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loss = sess.run(cost, feed_dict={
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x: batch_x,
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y: batch_y,
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keep_prob: 1.})
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valid_acc = sess.run(accuracy, feed_dict={
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x: mnist.validation.images[:test_valid_size],
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y: mnist.validation.labels[:test_valid_size],
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keep_prob: 1.})
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print('Epoch {:>2}, Batch {:>3} -'
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'Loss: {:>10.4f} Validation Accuracy: {:.6f}'.format(
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epoch + 1,
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batch + 1,
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loss,
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valid_acc))
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# Calculate Test Accuracy
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test_acc = sess.run(accuracy, feed_dict={
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x: mnist.test.images[:test_valid_size],
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y: mnist.test.labels[:test_valid_size],
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keep_prob: 1.})
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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：
- TensorFlow 变量。
- 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。
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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：
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new_height = (input_height - filter_height + 2 * P)/S + 1
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new_width = (input_width - filter_width + 2 * P)/S + 1
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"""
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Setup the strides, padding and filter weight/bias such that
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the output shape is (1, 2, 2, 3).
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"""
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import tensorflow as tf
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import numpy as np
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# `tf.nn.conv2d` requires the input be 4D (batch_size, height, width, depth)
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# (1, 4, 4, 1)
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x = np.array([
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[0, 1, 0.5, 10],
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[2, 2.5, 1, -8],
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[4, 0, 5, 6],
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[15, 1, 2, 3]], dtype=np.float32).reshape((1, 4, 4, 1))
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X = tf.constant(x)
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def conv2d(input):
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# Filter (weights and bias)
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# The shape of the filter weight is (height, width, input_depth, output_depth)
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# The shape of the filter bias is (output_depth,)
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# TODO: Define the filter weights `F_W` and filter bias `F_b`.
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# NOTE: Remember to wrap them in `tf.Variable`, they are trainable parameters after all.
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F_W = tf.Variable(tf.truncated_normal((2,2,1,3)))
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F_b = tf.Variable(tf.zeros(3))
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# TODO: Set the stride for each dimension (batch_size, height, width, depth)
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strides = [1, 2, 2, 1]
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# TODO: set the padding, either 'VALID' or 'SAME'.
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padding = 'VALID'
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# https://www.tensorflow.org/versions/r0.11/api_docs/python/nn.html#conv2d
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# `tf.nn.conv2d` does not include the bias computation so we have to add it ourselves after.
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return tf.nn.conv2d(input, F_W, strides, padding) + F_b
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out = conv2d(X)
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在TensorFlow使用池化层
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"""
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Set the values to `strides` and `ksize` such that
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the output shape after pooling is (1, 2, 2, 1).
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"""
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import tensorflow as tf
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import numpy as np
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# `tf.nn.max_pool` requires the input be 4D (batch_size, height, width, depth)
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# (1, 4, 4, 1)
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x = np.array([
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[0, 1, 0.5, 10],
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[2, 2.5, 1, -8],
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[4, 0, 5, 6],
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[15, 1, 2, 3]], dtype=np.float32).reshape((1, 4, 4, 1))
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X = tf.constant(x)
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def maxpool(input):
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# TODO: Set the ksize (filter size) for each dimension (batch_size, height, width, depth)
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ksize = [1, 2, 2, 1]
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# TODO: Set the stride for each dimension (batch_size, height, width, depth)
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strides = [1, 2, 2, 1]
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# TODO: set the padding, either 'VALID' or 'SAME'.
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padding = 'SAME'
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# https://www.tensorflow.org/versions/r0.11/api_docs/python/nn.html#max_pool
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return tf.nn.max_pool(input, ksize, strides, padding)
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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
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