Tensorflow 学习笔记

摘要

研二可能搞大数据，Spark和数据挖掘、机器学习什么的可能都要用到，做个笔记，顺带分享

Reference [1]. http://wiki.jikexueyuan.com/project/tensorflow-zh/tutorials/deep_cnn.html [2]. http://v.youku.com/v_show/id_XMjc3MzA3OTM0NA==.html?spm=a2hzp.8253876.0.0&f=27327189

基于Win10 Tensorflow 0.12(CPU)

简单的实例

(1) 一个最简单的直线方程

import tensorflow as tf import numpy as np # create data print('create data...') x_data = np.random.rand(100).astype(np.float32) y_data = x_data * 0.1 + 0.3 # create tensorflow structure start Weights = tf.Variable(tf.random_uniform([1],-1.0,1.0)) biases = tf.Variable(tf.zeros([1])) y = Weights*x_data + biases loss = tf.reduce_mean(tf.square(y-y_data)) learn_effciency = 0.5 #学习效率一般小于1 optimizer = tf.train.GradientDescentOptimizer(learn_effciency) train = optimizer.minimize(loss) init = tf.initialize_all_variables() #初始化所有变量 # Start the neutral network sess = tf.Session() sess.run(init) for step in range(201): sess.run(train) if step%20==0 : print(step,sess.run(Weights),sess.run(biases))

(2) Session的两种使用方式

import tensorflow as tf import numpy as np matrix1 = tf.constant([[3,3]]) matrix2 = tf.constant([[2],[2]]) product = tf.matmul(matrix1,matrix2) # matrix multiply == np.dot(m1,m2) # # Session usage type 1 # sess = tf.Session() # result = sess.run(product) # print(result) # sess.close() # Session usage type 2 with tf.Session() as sess: # 类似于一个auto_ptr result2 = sess.run(product) print(result2)

(3) 使用变量一定要记得run初始化哦

import tensorflow as tf state = tf.Variable(0, name='counter') one = tf.constant(1) new_value = tf.add(state,one) update = tf.assign(state, new_value) # 必须初始化所有变量 # Deprecated and will be removed after 2017-03-02. # init = tf.initialize_all_variables() # with tf.Session() as sess: # sess.run(init) # for _ in range(3): # sess.run(update) # print(sess.run(state)) # Latest solution init_op = tf.global_variables_initializer() local_init_op = tf.local_variables_initializer() # local variables like epoch_num, batch_size 可以不初始化local with tf.Session() as sess: sess.run(init_op) sess.run(local_init_op) for _ in range(3): sess.run(update) print(sess.run(state))

(4) 使用placeholder预定义变量

import tensorflow as tf input1 = tf.placeholder(tf.float32) input2 = tf.placeholder(tf.float32) output = tf.multiply(input1,input2) # tf.mul已经用不了了 with tf.Session() as sess: print(sess.run(output,feed_dict={input1:[7.], input2:[2.]})) # 补上数据

(5) 添加神经层

import tensorflow as tf # 添加神经层 def add_layer(inputs, in_size, out_size, activation_function=None): Weights = tf.Variable(tf.random_normal([in_size, out_size])) biases = tf.Variable(tf.zeros([1, out_size]) + 0.1) Wx_plus_b = tf.matmul(inputs, Weights) + biases # 没有被激活的值 if activation_function is None: # 说明是线性方程 outputs = Wx_plus_b else: outputs = activation_function(Wx_plus_b) return outputs

(6) 一个只有一层hidden layer的简单学习

import tensorflow as tf import numpy as np # 添加神经层 def add_layer(inputs, in_size, out_size, activation_function=None): Weights = tf.Variable(tf.random_normal([in_size, out_size])) biases = tf.Variable(tf.zeros([1, out_size]) + 0.1) Wx_plus_b = tf.matmul(inputs, Weights) + biases # 没有被激活的值 if activation_function is None: # 说明是线性方程 outputs = Wx_plus_b else: outputs = activation_function(Wx_plus_b) return outputs x_data = np.linspace(-1,1,300)[:,np.newaxis] # 后面的中括号用于加维度 noise = np.random.normal(0,0.05,x_data.shape) # 噪声，用来模拟真实数据 y_data = np.square(x_data) - 0.5 + noise xs = tf.placeholder(tf.float32, [None, 1]) # None表示无论给多少个sample都可以 ys = tf.placeholder(tf.float32, [None, 1]) # 1 -> 10 -> 1 l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu) prediction = add_layer(l1, 10, 1, activation_function=None) loss = tf.reduce_mean( # 求平均数 tf.reduce_sum( # 对例子的差进行求和 tf.square(ys - prediction), reduction_indices=[1])) train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) for i in range(1000): sess.run(train_step,feed_dict={xs:x_data, ys:y_data}) if i% 50==0: print(sess.run(loss,feed_dict={xs:x_data, ys:y_data}))

(7) 训练过程可视化

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt # 添加神经层 def add_layer(inputs, in_size, out_size, activation_function=None): Weights = tf.Variable(tf.random_normal([in_size, out_size])) biases = tf.Variable(tf.zeros([1, out_size]) + 0.1) Wx_plus_b = tf.matmul(inputs, Weights) + biases # 没有被激活的值 if activation_function is None: # 说明是线性方程 outputs = Wx_plus_b else: outputs = activation_function(Wx_plus_b) return outputs x_data = np.linspace(-1,1,300)[:,np.newaxis] # 后面的中括号用于加维度 noise = np.random.normal(0,0.05,x_data.shape) y_data = np.square(x_data) - 0.5 + noise xs = tf.placeholder(tf.float32, [None, 1]) # None表示无论给多少个sample都可以 ys = tf.placeholder(tf.float32, [None, 1]) # 1 -> 10 -> 1 l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu) prediction = add_layer(l1, 10, 1, activation_function=None) loss = tf.reduce_mean( # 求平均数 tf.reduce_sum( # 对10个例子的差进行求和 tf.square(ys - prediction), reduction_indices=[1])) train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) fig = plt.figure() # 规定坐标轴上下界 v = [-2, 2, -2, 2] plt.axis(v) ax = fig.add_subplot(1,1,1) ax.scatter(x_data,y_data) plt.ion() # python 3.5+ 防止show()阻塞屏幕 plt.show() for i in range(1000): sess.run(train_step,feed_dict={xs:x_data, ys:y_data}) if i% 5==0: # print(sess.run(loss,feed_dict={xs:x_data, ys:y_data})) try: ax.lines.remove(lines[0]) except Exception: pass prediction_value = sess.run(prediction,feed_dict={xs:x_data}) lines = ax.plot(x_data,prediction_value,'r-',lw=5) plt.pause(0.1)

(8) 过拟合以及tensorboard

// tensorboard 使用方法 // 1. cd到logs文件夹的父目录 // 2. 使用以下命令 tensorboard --logdir='logs' // 3. 用浏览器打开指定的网页地址 Starting TensorBoard b'47' at http://0.0.0.0:6006 // 也就是http://0.0.0.0:6006 // 注意：Windows中tensorboard经常gg, 推荐使用Mac import tensorflow as tf from sklearn.datasets import load_digits from sklearn.cross_validation import train_test_split from sklearn.preprocessing import LabelBinarizer # load data digits = load_digits() X = digits.data y = digits.target y = LabelBinarizer().fit_transform(y) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3) # 添加神经层 def add_layer(inputs, in_size, out_size, layer_name, activation_function=None): Weights = tf.Variable(tf.random_normal([in_size, out_size])) biases = tf.Variable(tf.zeros([1, out_size]) + 0.1) Wx_plus_b = tf.matmul(inputs, Weights) + biases # 没有被激活的值 # Dropout 一部分的神经元，减小过拟合 Wx_plus_b = tf.nn.dropout(Wx_plus_b, keep_prob) if activation_function is None: # 说明是线性方程 outputs = Wx_plus_b else: outputs = activation_function(Wx_plus_b) tf.summary.histogram(layer_name+'/outputs', outputs) # ver 1.0 以后必须这么写 return outputs keep_prob = tf.placeholder(tf.float32) # 每次保留多少百分百的值 xs = tf.placeholder(tf.float32, [None, 64]) # None表示无论给多少个sample都可以 ys = tf.placeholder(tf.float32, [None, 10]) # add output layer l1 = add_layer(xs, 64, 50, 'l1', activation_function=tf.nn.tanh) prediction = add_layer(l1, 50, 10, 'l2', activation_function=tf.nn.softmax) # the loss between prediction and real data cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction), reduction_indices=[1])) # loss tf.summary.scalar('loss', cross_entropy) train_step = tf.train.GradientDescentOptimizer(0.6).minimize(cross_entropy) with tf.Session() as sess: merged = tf.summary.merge_all() # summary writer goes in here train_writer = tf.summary.FileWriter("logs/train",sess.graph) test_writer = tf.summary.FileWriter("logs/test",sess.graph) sess.run(tf.global_variables_initializer()) for i in range(1000): sess.run(train_step,feed_dict={xs:X_train, ys:y_train, keep_prob: 0.5}) if i% 50==0: # record loss train_result = sess.run(merged, feed_dict={xs:X_train, ys:y_train, keep_prob: 1}) #记录的时候不用drop任何东西 test_result = sess.run(merged, feed_dict={xs:X_test, ys:y_test, keep_prob: 1}) train_writer.add_summary(train_result,i) test_writer.add_summary(test_result,i)

(9) 卷积神经网络

参考以下文章 http://blog.csdn.net/wang_junjie/article/details/51317348

import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data # number 1 to 10 data mnist = input_data.read_data_sets('MNIST_data',one_hot=True) def compute_accuracy(v_xs, v_ys): global prediction y_pre = sess.run(prediction, feed_dict={xs:v_xs, keep_prob:1}) correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) result = sess.run(accuracy, feed_dict={xs:v_xs, ys:v_ys, keep_prob:1}) return result def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) # 产生随机变量 return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME') # x:图片的所有信息， W:权值， strides:卷积步长 # strides规定为一个[1, x_movement, y_movement, 1], # 第一个和第四个值必须为1，第二个值为X方向步长，第三个值为Y方向的步长 # padding : (1)SAME : 抽取包括边缘值以外的值，以0填充，卷积后图片大小和原图片一致 # (2)VALID : 只抽取边缘内的值，卷积后图片大小比原图片小 def max_pool_2x2(x): # Pooling技术保留更多的图片信息 return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME') # ksize第一个和第四个值必须为1 # define placeholder for inputs to nerwork xs = tf.placeholder(tf.float32,[None,784]) ys = tf.placeholder(tf.float32,[None,10]) keep_prob = tf.placeholder(tf.float32) x_image = tf.reshape(xs,[-1,28,28,1]) # -1:表示不管这个维度, 1:图片高度 ## conv1 layer ## W_conv1 = weight_variable([5,5,1,32]) # patch 5x5 卷积器， in_size 1 图片的输入高度, output_size 32 b_conv1 = bias_variable([32]) h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1) + b_conv1) # relu:非线性化, output_size 28x28x32 采用SAME所以长宽不变 而卷积输出高度为32 h_pool1 = max_pool_2x2(h_conv1) # output_size 14x14x32 ## conv2 layer ## W_conv2 = weight_variable([5,5,32,64]) # patch 5x5 卷积器， in_size 32 图片的输入高度, output_size 64 b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2) + b_conv2) # relu:非线性化, output_size 14x14x64 采用SAME所以长宽不变 而卷积输出高度为32 h_pool2 = max_pool_2x2(h_conv2) # output_size 7x7x64 ## func1 layer ## W_fc1 = weight_variable([7*7*64,1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64]) # 数据一维化 [n_samples,7,7,64] -> [n_samples,7*7*64] h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1)+b_fc1) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) ## func2 layer ## W_fc2 = weight_variable([1024,10]) b_fc2 = bias_variable([10]) prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2)+b_fc2) # softmax classification算概率 # the error between prediction and real data cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction), reduction_indices=[1])) train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(train_step, feed_dict={xs:batch_xs, ys:batch_ys, keep_prob:0.5}) if i % 50 == 0: print(compute_accuracy(mnist.test.images, mnist.test.labels))

(10) 保存读取Weights和Bias

import tensorflow as tf import numpy as np ## Save to file W = tf.Variable([[1,2,3],[3,4,5]], dtype=tf.float32, name='weights') b = tf.Variable([[1,2,3]], dtype=tf.float32, name='bias') saver = tf.train.Saver() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) # 一定要先建立一个my_net文件夹 save_path = saver.save(sess, "my_net/save_net.ckpt") print("Save to path:", save_path) ## Restore variables # restore时一定要定义dtype 和 shape W = tf.Variable(np.arange(6).reshape((2,3)), dtype=tf.float32, name='weights') b = tf.Variable(np.arange(3).reshape((1,3)), dtype=tf.float32, name='bias') # not need init step saver = tf.train.Saver() with tf.Session() as sess: saver.restore(sess,"my_net/save_net.ckpt") print("weights:",sess.run(W)) print("bias:",sess.run(b))

(11) RNN

import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data # number 1 to 10 data mnist = input_data.read_data_sets('MNIST_data',one_hot=True) #hyper parameters lr = 0.001 training_iters = 100000 batch_size = 128 display_step = 10 n_inputs = 28 # MNIST data input (img shape:28x28) n_steps = 28 # time steps n_hidden_units = 128 # neurons in hidden layer n_classes = 10 # MNIST classes (0-9) 分为10个类 # tf Graph input x = tf.placeholder(tf.float32,[None, n_steps, n_inputs]) y = tf.placeholder(tf.float32,[None, n_classes]) # Define weights weights = { #(28,128) 'in':tf.Variable(tf.random_normal([n_inputs, n_hidden_units])), #(128,10) 'out':tf.Variable(tf.random_normal([n_hidden_units, n_classes])) } biases = { #(128,) 'in':tf.Variable(tf.constant(0.1, shape=[n_hidden_units,])), #(10,) 'out':tf.Variable(tf.constant(0.1, shape=[n_classes,])) } def RNN(X, weights, biases): # hidden layer for input to cell # X(128batch, 28 steps, 28 inputs) # ==> (128*28, 28 inputs) X = tf.reshape(X,[-1,n_inputs]) # ==>(128batch * 28steps, 128 hidden) X_in = tf.matmul(X, weights['in']) + biases['in'] # ==>(128batch , 28steps, 128 hidden) X_in= tf.reshape(X_in,[-1,n_steps,n_hidden_units]) # cell lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden_units, forget_bias=1.0, state_is_tuple=True) _init_state = lstm_cell.zero_state(batch_size, dtype=tf.float32) outputs, states = tf.nn.dynamic_rnn(lstm_cell, X_in, initial_state=_init_state, time_major=False) # hidden layer for output as the final results results = tf.matmul(states[1], weights['out']) + biases['out'] return results pred = RNN(x, weights, biases) cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) train_op = tf.train.AdamOptimizer(lr).minimize(cost) correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y,1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) step = 0 while step*batch_size < training_iters: batch_xs, batch_ys = mnist.train.next_batch(batch_size) batch_xs = batch_xs.reshape([batch_size, n_steps, n_inputs]) sess.run([train_op],feed_dict={ x: batch_xs, y: batch_ys }) if step % 100 == 0: print(sess.run(accuracy,feed_dict={ x: batch_xs, y: batch_ys }))