## morvanzhou / Tensorflow-Tutorial .gitee-modal { width: 500px !important; }

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404_AutoEncoder.py 2.89 KB
Morvan Zhou authored 2017-06-18 00:11 . update
"""
Know more, visit my Python tutorial page: https://morvanzhou.github.io/tutorials/

Dependencies:
tensorflow: 1.1.0
matplotlib
numpy
"""
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import numpy as np

tf.set_random_seed(1)

# Hyper Parameters
BATCH_SIZE = 64
LR = 0.002         # learning rate
N_TEST_IMG = 5

# Mnist digits
mnist = input_data.read_data_sets('./mnist', one_hot=False)     # use not one-hotted target data
test_x = mnist.test.images[:200]
test_y = mnist.test.labels[:200]

# plot one example
print(mnist.train.images.shape)     # (55000, 28 * 28)
print(mnist.train.labels.shape)     # (55000, 10)
plt.imshow(mnist.train.images[0].reshape((28, 28)), cmap='gray')
plt.title('%i' % np.argmax(mnist.train.labels[0]))
plt.show()

# tf placeholder
tf_x = tf.placeholder(tf.float32, [None, 28*28])    # value in the range of (0, 1)

# encoder
en0 = tf.layers.dense(tf_x, 128, tf.nn.tanh)
en1 = tf.layers.dense(en0, 64, tf.nn.tanh)
en2 = tf.layers.dense(en1, 12, tf.nn.tanh)
encoded = tf.layers.dense(en2, 3)

# decoder
de0 = tf.layers.dense(encoded, 12, tf.nn.tanh)
de1 = tf.layers.dense(de0, 64, tf.nn.tanh)
de2 = tf.layers.dense(de1, 128, tf.nn.tanh)
decoded = tf.layers.dense(de2, 28*28, tf.nn.sigmoid)

loss = tf.losses.mean_squared_error(labels=tf_x, predictions=decoded)

sess = tf.Session()
sess.run(tf.global_variables_initializer())

# initialize figure
f, a = plt.subplots(2, N_TEST_IMG, figsize=(5, 2))
plt.ion()   # continuously plot

# original data (first row) for viewing
view_data = mnist.test.images[:N_TEST_IMG]
for i in range(N_TEST_IMG):
a[0][i].imshow(np.reshape(view_data[i], (28, 28)), cmap='gray')
a[0][i].set_xticks(()); a[0][i].set_yticks(())

for step in range(8000):
b_x, b_y = mnist.train.next_batch(BATCH_SIZE)
_, encoded_, decoded_, loss_ = sess.run([train, encoded, decoded, loss], {tf_x: b_x})

if step % 100 == 0:     # plotting
print('train loss: %.4f' % loss_)
# plotting decoded image (second row)
decoded_data = sess.run(decoded, {tf_x: view_data})
for i in range(N_TEST_IMG):
a[1][i].clear()
a[1][i].imshow(np.reshape(decoded_data[i], (28, 28)), cmap='gray')
a[1][i].set_xticks(()); a[1][i].set_yticks(())
plt.draw(); plt.pause(0.01)
plt.ioff()

# visualize in 3D plot
view_data = test_x[:200]
encoded_data = sess.run(encoded, {tf_x: view_data})
fig = plt.figure(2); ax = Axes3D(fig)
X, Y, Z = encoded_data[:, 0], encoded_data[:, 1], encoded_data[:, 2]
for x, y, z, s in zip(X, Y, Z, test_y):
c = cm.rainbow(int(255*s/9)); ax.text(x, y, z, s, backgroundcolor=c)
ax.set_xlim(X.min(), X.max()); ax.set_ylim(Y.min(), Y.max()); ax.set_zlim(Z.min(), Z.max())
plt.show()