autoencoder.rar_tensorflow_自编码

""" Auto Encoder Example. Build a 2 layers auto-encoder with TensorFlow to compress images to a lower latent space and then reconstruct them. References: Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998. Links: [MNIST Dataset] http://yann.lecun.com/exdb/mnist/ Project: https://github.com/aymericdamien/TensorFlow-Examples/ """ from __future__ import division, print_function, absolute_import 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=True) # Training Parameters learning_rate = 0.01 num_steps = 30000 batch_size = 256 display_step = 1000 examples_to_show = 10 # Network Parameters num_hidden_1 = 256 # 1st layer num features num_hidden_2 = 128 # 2nd layer num features (the latent dim) num_input = 784 # MNIST data input (img shape: 28*28) # tf Graph input (only pictures) X = tf.placeholder("float", [None, num_input]) 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 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 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 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 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 session with tf.Session() as sess: # Run the initializer sess.run(init) # Training 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 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()