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Revert "visualizing output in autoencoder" #101

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Revert "visualizing output in autoencoder" #101

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30 changes: 2 additions & 28 deletions 06_autoencoder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,25 +2,6 @@
import numpy as np
import input_data

import matplotlib # to plot images
# Force matplotlib to not use any X-server backend.
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

## Visualizing reconstructions
def vis(images, save_name):
dim = images.shape[0]
n_image_rows = int(np.ceil(np.sqrt(dim)))
n_image_cols = int(np.ceil(dim * 1.0/n_image_rows))
gs = gridspec.GridSpec(n_image_rows,n_image_cols,top=1., bottom=0., right=1., left=0., hspace=0., wspace=0.)
for g,count in zip(gs,range(int(dim))):
ax = plt.subplot(g)
ax.imshow(images[count,:].reshape((28,28)))
ax.set_xticks([])
ax.set_yticks([])
plt.savefig(save_name + '_vis.png')

mnist_width = 28
n_visible = mnist_width * mnist_width
n_hidden = 500
Expand Down Expand Up @@ -58,7 +39,7 @@ def model(X, mask, W, b, W_prime, b_prime):
# create cost function
cost = tf.reduce_sum(tf.pow(X - Z, 2)) # minimize squared error
train_op = tf.train.GradientDescentOptimizer(0.02).minimize(cost) # construct an optimizer
predict_op = Z

# load MNIST data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
Expand All @@ -76,11 +57,4 @@ def model(X, mask, W, b, W_prime, b_prime):

mask_np = np.random.binomial(1, 1 - corruption_level, teX.shape)
print(i, sess.run(cost, feed_dict={X: teX, mask: mask_np}))
# save the predictions for 100 images
mask_np = np.random.binomial(1, 1 - corruption_level, teX[:100].shape)
predicted_imgs = sess.run(predict_op, feed_dict={X: teX[:100], mask: mask_np})
input_imgs = teX[:100]
# plot the reconstructed images
vis(predicted_imgs,'pred')
vis(input_imgs,'in')
print('Done')