2.3AutoEncoder

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(‘MNIST_data‘, one_hot=False)# Visualize decoder setting# Parameterslearning_rate = 0.01training_epochs = 5batch_size = 256display_step = 1examples_to_show = 10# Network Parametersn_input = 784 ?# MNIST data input (img shape: 28*28)# tf Graph input (only pictures)X = tf.placeholder("float", [None, n_input])# hidden layer settingsn_hidden_1 = 256 # 1st layer num featuresn_hidden_2 = 128 # 2nd layer num featuresweights = { ???‘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 encoderdef 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‘])) ???# 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 decoderdef 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# Parameterslearning_rate = 0.01 ???# 0.01 this learning rate will be better! Testedtraining_epochs = 10batch_size = 256display_step = 1# Network Parametersn_input = 784 ?# MNIST data input (img shape: 28*28)# tf Graph input (only pictures)X = tf.placeholder("float", [None, n_input])# hidden layer settingsn_hidden_1 = 128n_hidden_2 = 64n_hidden_3 = 10n_hidden_4 = 2 ?#2D showweights = { ???‘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])),}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_4def 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 modelencoder_op = encoder(X)decoder_op = decoder(encoder_op)# Predictiony_pred = decoder_op# Targets (Labels) are the input data.y_true = X# Define loss and optimizer, minimize the squared errorcost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)# Launch the graphwith tf.Session() as sess: ???# tf.initialize_all_variables() no long valid from ???# 2017-03-02 if using tensorflow >= 0.12 ???if int((tf.__version__).split(‘.‘)[1]) < 12 and int((tf.__version__).split(‘.‘)[0]) < 1: ???????init = tf.initialize_all_variables() ???else: ???????init = tf.global_variables_initializer() ???sess.run(init) ???total_batch = int(mnist.train.num_examples/batch_size) ???# Training cycle ???for epoch in range(training_epochs): ???????# 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 ???????????# Run optimization op (backprop) and cost op (to get loss value) ???????????_, c = sess.run([optimizer, cost], feed_dict={X: batch_xs}) ???????# Display logs per epoch step ???????if epoch % display_step == 0: ???????????print("Epoch:", ‘%04d‘ % (epoch+1), ?????????????????"cost=", "{:.9f}".format(c)) ???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}) ???# plt.scatter(encoder_result[:, 0], encoder_result[:, 1], c=mnist.test.labels) ???# plt.colorbar() ???# plt.show()

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(‘MNIST_data‘, one_hot=False)"""# Visualize decoder setting# Parameterslearning_rate = 0.01training_epochs = 5batch_size = 256display_step = 1examples_to_show = 10# Network Parametersn_input = 784 ?# MNIST data input (img shape: 28*28)# tf Graph input (only pictures)X = tf.placeholder("float", [None, n_input])# hidden layer settingsn_hidden_1 = 256 # 1st layer num featuresn_hidden_2 = 128 # 2nd layer num featuresweights = { ???‘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 encoderdef 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‘])) ???# 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 decoderdef 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# Parameterslearning_rate = 0.01 ???# 0.01 this learning rate will be better! Testedtraining_epochs = 10batch_size = 256display_step = 1# Network Parametersn_input = 784 ?# MNIST data input (img shape: 28*28)# tf Graph input (only pictures)X = tf.placeholder("float", [None, n_input])# hidden layer settingsn_hidden_1 = 128n_hidden_2 = 64n_hidden_3 = 10n_hidden_4 = 2 ?#2D showweights = { ???‘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])),}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_4def 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 modelencoder_op = encoder(X)decoder_op = decoder(encoder_op)# Predictiony_pred = decoder_op# Targets (Labels) are the input data.y_true = X# Define loss and optimizer, minimize the squared errorcost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)# Launch the graphwith tf.Session() as sess: ???# tf.initialize_all_variables() no long valid from ???# 2017-03-02 if using tensorflow >= 0.12 ???if int((tf.__version__).split(‘.‘)[1]) < 12 and int((tf.__version__).split(‘.‘)[0]) < 1: ???????init = tf.initialize_all_variables() ???else: ???????init = tf.global_variables_initializer() ???sess.run(init) ???total_batch = int(mnist.train.num_examples/batch_size) ???# Training cycle ???for epoch in range(training_epochs): ???????# 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 ???????????# Run optimization op (backprop) and cost op (to get loss value) ???????????_, c = sess.run([optimizer, cost], feed_dict={X: batch_xs}) ???????# Display logs per epoch step ???????if epoch % display_step == 0: ???????????print("Epoch:", ‘%04d‘ % (epoch+1), ?????????????????"cost=", "{:.9f}".format(c)) ???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}) ???plt.scatter(encoder_result[:, 0], encoder_result[:, 1], c=mnist.test.labels) ???plt.colorbar() ???plt.show()