学习笔记TF057:TensorFlow MNIST，卷积神经网络、循环神经网络、无监督学习

MNIST 卷积神经网络。https://github.com/nlintz/TensorFlow-Tutorials/blob/master/05_convolutional_net.py 。
TensorFlow搭建卷积神经网络(CNN)模型，训练MNIST数据集。

构建模型。

定义输入数据，预处理数据。读取数据MNIST，得到训练集图片、标记矩阵，测试集图片标记矩阵。trX、trY、teX、teY 数据矩阵表现。trX、teX形状变为[-1,28,28,1]，-1 不考虑输入图片数量，28x28 图片长、宽像素数，1 通道(channel)数量。MNIST 黑白图片，通道1。RGB彩色图像，通道3。
初始化权重，定义网络结构。卷积神经网络，3个卷积层、3个池化层、1个全连接层、1个输出层。
定义dropout占位符keep_conv，神经元保留比例。生成网络模型，得到预测值。
定义损失函数，tf.nn.softmax_cross_entropy_with_logits 比较预测值、真实值差异，做均值处理。
定义训练操作(train_op)，RMSProp算法优化器tf.train.RMSPropOptimizer，学习率0.001,衰减值0.9，优化损失。
定义预测操作(predict_op)。
会话启动图，训练、评估。

#!/usr/bin/env pythonimport tensorflow as tfimport numpy as npfrom tensorflow.examples.tutorials.mnist import input_databatch_size = 128 # 训练批次大小test_size = 256 # 评估批次大小# 定义初始化权重函数def init_weights(shape):    return tf.Variable(tf.random_normal(shape, stddev=0.01))# 定义神经网络模型函数# 入参：X 输入数据，w 每层权重，p_keep_conv、p_keep_hidden dropout保留神经元比例def model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden):    # 第一组卷积层及池化层，dropout部分神经元    l1a = tf.nn.relu(tf.nn.conv2d(X, w,                       # l1a shape=(?, 28, 28, 32)                        strides=[1, 1, 1, 1], padding='SAME'))    l1 = tf.nn.max_pool(l1a, ksize=[1, 2, 2, 1],              # l1 shape=(?, 14, 14, 32)                        strides=[1, 2, 2, 1], padding='SAME')    l1 = tf.nn.dropout(l1, p_keep_conv)    # 第二组卷积层及池化层，dropout部分神经元    l2a = tf.nn.relu(tf.nn.conv2d(l1, w2,                     # l2a shape=(?, 14, 14, 64)                        strides=[1, 1, 1, 1], padding='SAME'))    l2 = tf.nn.max_pool(l2a, ksize=[1, 2, 2, 1],              # l2 shape=(?, 7, 7, 64)                        strides=[1, 2, 2, 1], padding='SAME')    l2 = tf.nn.dropout(l2, p_keep_conv)    # 第三组卷积层及池化层，dropout部分神经元    l3a = tf.nn.relu(tf.nn.conv2d(l2, w3,                     # l3a shape=(?, 7, 7, 128)                        strides=[1, 1, 1, 1], padding='SAME'))    l3 = tf.nn.max_pool(l3a, ksize=[1, 2, 2, 1],              # l3 shape=(?, 4, 4, 128)                        strides=[1, 2, 2, 1], padding='SAME')    l3 = tf.reshape(l3, [-1, w4.get_shape().as_list()[0]])    # reshape to (?, 2048)    l3 = tf.nn.dropout(l3, p_keep_conv)    # 全连接层，dropout部分神经元    l4 = tf.nn.relu(tf.matmul(l3, w4))    l4 = tf.nn.dropout(l4, p_keep_hidden)    # 输出层    pyx = tf.matmul(l4, w_o)    return pyx # 返回预测值mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels# 数据预处理trX = trX.reshape(-1, 28, 28, 1)  # 28x28x1 input imgteX = teX.reshape(-1, 28, 28, 1)  # 28x28x1 input imgX = tf.placeholder("float", [None, 28, 28, 1])Y = tf.placeholder("float", [None, 10])# 卷积核大小 3x3# patch大小3x3，输入维度1，输出维度32w = init_weights([3, 3, 1, 32])       # 3x3x1 conv, 32 outputs# patch大小3x3，输入维度32，输出维度64w2 = init_weights([3, 3, 32, 64])     # 3x3x32 conv, 64 outputs# patch大小3x3，输入维度64，输出维度128w3 = init_weights([3, 3, 64, 128])    # 3x3x32 conv, 128 outputs# 全连接层，输入维度128*4*4 上层输数据三维转一维，输出维度625w4 = init_weights([128 * 4 * 4, 625]) # FC 128 * 4 * 4 inputs, 625 outputs# 输出层，输入维度625，输出维度10 代表10类(labels)w_o = init_weights([625, 10])         # FC 625 inputs, 10 outputs (labels)# 定义dropout占位符p_keep_conv = tf.placeholder("float")p_keep_hidden = tf.placeholder("float")py_x = model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden) # 得到预测值# 定义损失函数cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y))# 定义训练操作train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)# 定义预测操作predict_op = tf.argmax(py_x, 1)# Launch the graph in a session#会话启动图with tf.Session() as sess:    # you need to initialize all variables    tf.global_variables_initializer().run()    for i in range(100):        # 训练模型        training_batch = zip(range(0, len(trX), batch_size),                             range(batch_size, len(trX)+1, batch_size))        for start, end in training_batch:            sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end],                                          p_keep_conv: 0.8, p_keep_hidden: 0.5})        # 评估模型        test_indices = np.arange(len(teX)) # Get A Test Batch        np.random.shuffle(test_indices)        test_indices = test_indices[0:test_size]        print(i, np.mean(np.argmax(teY[test_indices], axis=1) ==                         sess.run(predict_op, feed_dict={X: teX[test_indices],                                                         p_keep_conv: 1.0,                                                         p_keep_hidden: 1.0})))

MNIST 循环神经网络。 https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py 。

RNN 自然语言处理领域成功应用，机器翻译、语音识别、图像描述生成(图像特征生成描述)、语言模型与文本生成(生成模型预测下一单词概率)。Alex Graves《Supervised Sequence Labelling with Recurrent Neural Networks》 http://www.cs.toronto.edu/~graves/preprint.pdf 。

构建模型。设置训练超参数，设置学习率、训练次数、每轮训练数据大小。
RNN分类图片，每张图片行，像素序列(sequence)。MNIST图片大小28x28，28个元素序列 X 28行，每步输入序列长度28，输入步数28步。
定义输入数据、权重。
定义RNN模型。
定义损失函数、优化器(AdamOptimizer)。
定义模型预测结果、准确率计算方法。
会话启动图，开始训练，每20次输出1次准确率大小。

from __future__ import print_functionimport tensorflow as tffrom tensorflow.contrib import rnn# Import MNIST datafrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("/tmp/data/", one_hot=True)# Training Parameters# 设置训练超参数learning_rate = 0.001training_steps = 10000batch_size = 128display_step = 200# Network Parameters# 神经网络参数num_input = 28 # MNIST data input (img shape: 28*28) 输入层timesteps = 28 # timesteps 28 长度num_hidden = 128 # hidden layer num of features 隐藏层神经元数num_classes = 10 # MNIST total classes (0-9 digits) 输出数量，分类类别 0~9# tf Graph input# 输入数据占位符X = tf.placeholder("float", [None, timesteps, num_input])Y = tf.placeholder("float", [None, num_classes])# Define weights# 定义权重weights = {    'out': tf.Variable(tf.random_normal([num_hidden, num_classes]))}biases = {    'out': tf.Variable(tf.random_normal([num_classes]))}# 定义RNN模型def RNN(x, weights, biases):    # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input)    # 输入x转换成(128 batch * 28 steps, 28 inputs)    x = tf.unstack(x, timesteps, 1)    # Define a lstm cell with tensorflow    # 基本LSTM循环网络单元 BasicLSTMCell    lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)    # Get lstm cell output    outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)    # Linear activation, using rnn inner loop last output    return tf.matmul(outputs[-1], weights['out']) + biases['out']logits = RNN(X, weights, biases)prediction = tf.nn.softmax(logits)# Define loss and optimizer# 定义损失函数loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(    logits=logits, labels=Y))# 定义优化器optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)train_op = optimizer.minimize(loss_op)# Evaluate model (with test logits, for dropout to be disabled)correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))# Initialize the variables (i.e. assign their default value)init = tf.global_variables_initializer()# Start trainingwith tf.Session() as sess:    # Run the initializer    sess.run(init)    for step in range(1, training_steps+1):        batch_x, batch_y = mnist.train.next_batch(batch_size)        # Reshape data to get 28 seq of 28 elements        batch_x = batch_x.reshape((batch_size, timesteps, num_input))        # Run optimization op (backprop)        sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})        if step % display_step == 0 or step == 1:            # Calculate batch loss and accuracy            loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,                                                                 Y: batch_y})            print("Step " + str(step) + ", Minibatch Loss= " + \                  "{:.4f}".format(loss) + ", Training Accuracy= " + \                  "{:.3f}".format(acc))    print("Optimization Finished!")    # Calculate accuracy for 128 mnist test images    test_len = 128    test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input))    test_label = mnist.test.labels[:test_len]    print("Testing Accuracy:", \        sess.run(accuracy, feed_dict={X: test_data, Y: test_label}))

MNIST 无监督学习。自编码器(autoencoder)。

自编码网络。UFLDL http://ufldl.stanford.edu/wiki/index.php/Autoencoders_and_Sparsity 。
监督学习数据有标记。
自编码网络，输入样本压缩到隐藏层，解压，输出端重建样本。最终输出层神经元数量等于输入层神经元数据量。压缩，输入数据(图像、文本、声音)存在不同程度冗余信息，自动编码网络学习去掉冗余信息，有用特征输入到隐藏层。找到可以代表源数据的主要成分。激活函数不使用sigmoid等非线性函数，用线性函数，就是PCA模型。
主成分分析(principal components analysis, PCA)，分析、简化数据集技术。减少数据集维数，保持数据集方差贡献最大特征。保留低阶主成分，忽略高阶主成分。最常用线性降维方法。
压缩过程，限制隐藏神经元数量，学习有意义特征。希望神经元大部分时间被抑制。神经元输出接近1为被激活，接近0为被抑制。部分神经元处于被抑制状态，稀疏性限制。
多个隐藏层，输入数据图像，第一层学习识别边，第二层学习组合边，构成轮廓、角，更高层学习组合更有意义特征。

TensorFlow自编码网络实现。 https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py 。

构建模型。设置超参数，学习率、训练轮数(epoch)、每次训练数据多少、每隔多少轮显示一次训练结果。
定义输入数据，无监督学习只需要图片数据，不需要标记数据。
初始化权重，定义网络结构。2个隐藏层，第一个隐藏层神经元256个，第二个隐藏层神经元128个。包括压缩、解压过程。
构建损失函数、优化器。损失函数“最小二乘法”，原始数据集和输出数据集平方差取均值运算。优化器用RMSPropOptimizer。
训练数据、评估模型。对测试集应用训练好的自动编码网络。比较测试集原始图片和自动编码网络重建结果。

from __future__ import division, print_function, absolute_importimport tensorflow as tfimport numpy as npimport matplotlib.pyplot as plt# Import MNIST datafrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("/tmp/data/", one_hot=True)# Training Parameters# 设置训练超参数learning_rate = 0.01 # 学习率num_steps = 30000 # 训练轮数batch_size = 256 # 每次训练数据多少display_step = 1000 # 每隔多少轮显示训练结果examples_to_show = 10 # 测试集选10张图片验证自动编码器结果# Network Parameters# 网络参数# 第一个隐藏层神经元个数，特征值个数num_hidden_1 = 256 # 1st layer num features# 第二个隐藏层神经元个数，特征值个数num_hidden_2 = 128 # 2nd layer num features (the latent dim)# 输入数据特征值个数 28x28=784num_input = 784 # MNIST data input (img shape: 28*28)# tf Graph input (only pictures)# 定义输入数据，只需要图片，不要需要标记X = tf.placeholder("float", [None, num_input])# 初始化每层权重和偏置weights = {    'encoder_h1': tf.Variable(tf.random_normal([num_input, num_hidden_1])),    'encoder_h2': tf.Variable(tf.random_normal([num_hidden_1, num_hidden_2])),    'decoder_h1': tf.Variable(tf.random_normal([num_hidden_2, num_hidden_1])),    'decoder_h2': tf.Variable(tf.random_normal([num_hidden_1, num_input])),}biases = {    'encoder_b1': tf.Variable(tf.random_normal([num_hidden_1])),    'encoder_b2': tf.Variable(tf.random_normal([num_hidden_2])),    'decoder_b1': tf.Variable(tf.random_normal([num_hidden_1])),    'decoder_b2': tf.Variable(tf.random_normal([num_input])),}# Building the encoder# 定义压缩函数def encoder(x):    # Encoder Hidden layer with sigmoid activation #1    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),                                   biases['encoder_b1']))    # Encoder Hidden layer with sigmoid activation #2    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),                                   biases['encoder_b2']))    return layer_2# Building the decoder# 定义解压函数def decoder(x):    # Decoder Hidden layer with sigmoid activation #1    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),                                   biases['decoder_b1']))    # Decoder Hidden layer with sigmoid activation #2    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),                                   biases['decoder_b2']))    return layer_2# Construct model# 构建模型encoder_op = encoder(X)decoder_op = decoder(encoder_op)# Prediction# 得出预测值y_pred = decoder_op# Targets (Labels) are the input data.# 得出真实值，即输入值y_true = X# Define loss and optimizer, minimize the squared error# 定义损失函数、优化器loss = tf.reduce_mean(tf.pow(y_true - y_pred, 2))optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(loss)# Initialize the variables (i.e. assign their default value)init = tf.global_variables_initializer()# Start Training# Start a new TF sessionwith tf.Session() as sess:    # Run the initializer    sess.run(init)    # Training    # 开始训练    for i in range(1, num_steps+1):        # Prepare Data        # Get the next batch of MNIST data (only images are needed, not labels)        batch_x, _ = mnist.train.next_batch(batch_size)        # Run optimization op (backprop) and cost op (to get loss value)        _, l = sess.run([optimizer, loss], feed_dict={X: batch_x})        # Display logs per step        # 每一轮，打印出一次损失值        if i % display_step == 0 or i == 1:            print('Step %i: Minibatch Loss: %f' % (i, l))    # Testing    # Encode and decode images from test set and visualize their reconstruction.    n = 4    canvas_orig = np.empty((28 * n, 28 * n))    canvas_recon = np.empty((28 * n, 28 * n))    for i in range(n):        # MNIST test set        batch_x, _ = mnist.test.next_batch(n)        # Encode and decode the digit image        g = sess.run(decoder_op, feed_dict={X: batch_x})        # Display original images        for j in range(n):            # Draw the original digits            canvas_orig[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = \                batch_x[j].reshape([28, 28])        # Display reconstructed images        for j in range(n):            # Draw the reconstructed digits            canvas_recon[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = \                g[j].reshape([28, 28])    print("Original Images")    plt.figure(figsize=(n, n))    plt.imshow(canvas_orig, origin="upper", cmap="gray")    plt.show()    print("Reconstructed Images")    plt.figure(figsize=(n, n))    plt.imshow(canvas_recon, origin="upper", cmap="gray")    plt.show()

参考资料：
《TensorFlow技术解析与实战》

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