标签：python 最小 truncate ack splay learning play 效果 最小二乘法
#!/usr/bin/env python import tensorflow as tf import numpy as np import matplotlib.pyplot as plt # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) # Visualize decoder setting # Parameters learning_rate = 0.01 batch_size = 256 display_step = 1 examples_to_show = 10 # Network Parameters n_input = 784 # 28x28 pix，即 784 Features # tf Graph input (only pictures) X = tf.placeholder("float", [None, n_input]) X_noise = tf.placeholder("float", [None, n_input]) # hidden layer settings n_hidden_1 = 256 # 经过第一个隐藏层压缩至256个 n_hidden_2 = 128 # 经过第二个压缩至128个 # 两个隐藏层的 weights 和 biases 的定义 weights = { ‘encoder_h1‘: tf.Variable(tf.random_normal([n_input, n_hidden_1])), ‘encoder_h2‘: tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), ‘decoder_h1‘: tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])), ‘decoder_h2‘: tf.Variable(tf.random_normal([n_hidden_1, n_input])), } biases = { ‘encoder_b1‘: tf.Variable(tf.random_normal([n_hidden_1])), ‘encoder_b2‘: tf.Variable(tf.random_normal([n_hidden_2])), ‘decoder_b1‘: tf.Variable(tf.random_normal([n_hidden_1])), ‘decoder_b2‘: tf.Variable(tf.random_normal([n_input])), } # Building the encoder def encoder(x): # Encoder Hidden layer 使用的 Activation function 是 sigmoid #1 scale = 0.02 layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x , weights[‘encoder_h1‘]), biases[‘encoder_b1‘])) # Decoder 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 def decoder(x): # Encoder 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 ‘‘‘ # Visualize encoder setting # 只显示解压后的数据 learning_rate = 0.01 # 0.01 this learning rate will be better! Tested training_epochs = 10 batch_size = 256 display_step = 1 # Network Parameters n_input = 784 # MNIST data input (img shape: 28*28) # tf Graph input (only pictures) X = tf.placeholder("float", [None, n_input]) # hidden layer settings n_hidden_1 = 128 n_hidden_2 = 64 n_hidden_3 = 10 n_hidden_4 = 2 #将原有784Features 的数据压缩成2 Features数据 weights = { ‘encoder_h1‘: tf.Variable(tf.truncated_normal([n_input, n_hidden_1],)), ‘encoder_h2‘: tf.Variable(tf.truncated_normal([n_hidden_1, n_hidden_2],)), ‘encoder_h3‘: tf.Variable(tf.truncated_normal([n_hidden_2, n_hidden_3],)), ‘encoder_h4‘: tf.Variable(tf.truncated_normal([n_hidden_3, n_hidden_4],)), ‘decoder_h1‘: tf.Variable(tf.truncated_normal([n_hidden_4, n_hidden_3],)), ‘decoder_h2‘: tf.Variable(tf.truncated_normal([n_hidden_3, n_hidden_2],)), ‘decoder_h3‘: tf.Variable(tf.truncated_normal([n_hidden_2, n_hidden_1],)), ‘decoder_h4‘: tf.Variable(tf.truncated_normal([n_hidden_1, n_input],)), } biases = { ‘encoder_b1‘: tf.Variable(tf.random_normal([n_hidden_1])), ‘encoder_b2‘: tf.Variable(tf.random_normal([n_hidden_2])), ‘encoder_b3‘: tf.Variable(tf.random_normal([n_hidden_3])), ‘encoder_b4‘: tf.Variable(tf.random_normal([n_hidden_4])), ‘decoder_b1‘: tf.Variable(tf.random_normal([n_hidden_3])), ‘decoder_b2‘: tf.Variable(tf.random_normal([n_hidden_2])), ‘decoder_b3‘: tf.Variable(tf.random_normal([n_hidden_1])), ‘decoder_b4‘: tf.Variable(tf.random_normal([n_input])),#注意：在第四层时，输出量不再是 [0,1] 范围内的数， #而是将数据通过默认的 Linear activation function 调整为 (-∞,∞) } def encoder(x): layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights[‘encoder_h1‘]), biases[‘encoder_b1‘])) layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights[‘encoder_h2‘]), biases[‘encoder_b2‘])) layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights[‘encoder_h3‘]), biases[‘encoder_b3‘])) layer_4 = tf.add(tf.matmul(layer_3, weights[‘encoder_h4‘]), biases[‘encoder_b4‘]) return layer_4 def decoder(x): layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights[‘decoder_h1‘]), biases[‘decoder_b1‘])) layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights[‘decoder_h2‘]), biases[‘decoder_b2‘])) layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights[‘decoder_h3‘]), biases[‘decoder_b3‘])) layer_4 = tf.nn.sigmoid(tf.add(tf.matmul(layer_3, weights[‘decoder_h4‘]), biases[‘decoder_b4‘])) return layer_4 ‘‘‘ # Construct model encoder_op = encoder(X) decoder_op = decoder(encoder_op) # Prediction y_pred = decoder_op # Targets (Labels) are the input data. y_true = X # Define loss and optimizer, minimize the squared error # 比较原始数据与还原后的拥有 784 Features 的数据进行 cost 的对比， # 根据 cost 来提升我的 Autoencoder 的准确率 loss = tf.reduce_mean(tf.pow(y_true - y_pred, 2)) # 进行最小二乘法的计算(y_true - y_pred)^2 # loss = tf.reduce_mean(tf.square(y_true - y_pred)) optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss) def corruptoin(x,noise_factor = 0.03): noisy_imgs = x +noise_factor * np.random.randn(*x.shape) #noisy_imgs = x + noise_factor * tf.random_normal(x) noisy_imgs = np.clip(noisy_imgs,0.,1.) return noisy_imgs # Launch the graph with tf.Session() as sess: init = tf.global_variables_initializer() sess.run(init) total_batch = int(mnist.train.num_examples / batch_size) training_epochs = 20 # Training cycle for epoch in range(training_epochs): # 到好的的效果，我们应进行10 ~ 20个 Epoch 的训练 # Loop over all batches for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) # max(x) = 1, min(x) = 0 Epoch: 0020 cost= 0.060871094,0.046518125 batch_xs = corruptoin(batch_xs) #Epoch: 0020 cost= 0.140342906,0.051774822 Epoch: 0020 cost= 0.055670232,0.046838347,Epoch: 0020 cost= 0.048563793,0.043603953,0.02=Epoch: 0020 cost= 0.046707503,0.0418 # Run optimization op (backprop) and cost op (to get loss value) _, c = sess.run([optimizer, loss], feed_dict={X: batch_xs}) # Display logs per epoch step if epoch % display_step == 0: print("Epoch:", ‘%04d‘ % (epoch + 1), "cost=", "{:.9f}".format(c)) a,t = sess.run([optimizer, loss], feed_dict={X: mnist.test.images[:examples_to_show]}) print(t) print("Optimization Finished!") # Applying encode and decode over test set encode_decode = sess.run( y_pred, feed_dict={X: mnist.test.images[:examples_to_show]}) # Compare original images with their reconstructions f, a = plt.subplots(2, 10, figsize=(10, 2)) for i in range(examples_to_show): a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28))) a[1][i].imshow(np.reshape(encode_decode[i], (28, 28))) plt.show() # encoder_result = sess.run(encoder_op, feed_dict={X: mnist.test.images}) # sc = plt.scatter(encoder_result[:, 0], encoder_result[:, 1], c=mnist.test.labels) #散点图 # plt.colorbar(sc) #scatter设置颜色渐变条colorbar
标签：python 最小 truncate ack splay learning play 效果 最小二乘法
原文地址：https://www.cnblogs.com/rongye/p/13195158.html
