From 66a419e43b6d466bd01f516d5b8d3b3b9c5dd4ec Mon Sep 17 00:00:00 2001
From: Anant Gupta <anant718@gmail.com>
Date: Sun, 13 Nov 2016 09:47:56 +0530
Subject: [PATCH] script added

---
 autoencoder.py | 143 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 143 insertions(+)
 create mode 100644 autoencoder.py

diff --git a/autoencoder.py b/autoencoder.py
new file mode 100644
index 0000000..fa88969
--- /dev/null
+++ b/autoencoder.py
@@ -0,0 +1,143 @@
+
+# coding: utf-8
+
+# In[1]:
+
+import tensorflow as tf
+import numpy as np
+from tensorflow.examples.tutorials.mnist import input_data
+from utils import *
+
+
+# In[2]:
+
+import matplotlib.pyplot as plt
+#get_ipython().magic(u'matplotlib inline')
+
+mnist = input_data.read_data_sets("data/")
+
+# In[3]:
+
+img_height = 28
+img_width = 28
+channel = 1
+
+num_layers = 3
+filter_size = 3
+fmap_in = channel
+fmap_out = 32
+strides = [1, 1, 1, 1]
+
+batch_size = 50
+
+from models import PixelCNN
+class Conf(object):
+    pass
+
+conf = Conf()
+conf.ckpt_path='ckpts'
+conf.conditional=True
+conf.data='mnist'
+conf.data_path='data'
+conf.epochs=50
+conf.f_map=32
+conf.grad_clip=1
+conf.layers=5
+conf.samples_path='samples'
+conf.num_classes = 10
+conf.img_height = 28
+conf.img_width = 28
+conf.channel = 1
+conf.num_batches = mnist.train.num_examples // batch_size
+conf.type='train'
+
+
+
+X = tf.placeholder(shape=[None, img_height, img_width, channel], dtype=tf.float32)
+
+fan_in = X
+W = []
+for i in range(num_layers):
+    if i == num_layers -1 :
+        fmap_out = 10 
+    W.append(tf.Variable(tf.truncated_normal(shape=[filter_size, filter_size, fmap_in, fmap_out], stddev=0.1), name="W_%d"%i))
+    b = tf.Variable(tf.ones(shape=[fmap_out], dtype=tf.float32), name="encoder_b_%d"%i)
+    en_conv = tf.nn.conv2d(fan_in, W[i], strides, padding='SAME', name="encoder_conv_%d"%i)
+
+    fan_in = tf.tanh(tf.add(en_conv, b))
+    fmap_in = fmap_out
+
+fan_in = tf.reshape(fan_in, (-1, conf.img_width*conf.img_height*fmap_out))
+conf.num_classes = int(fan_in.get_shape()[1])
+
+# TODO
+# Make X enter from model input
+model = PixelCNN(conf)
+# output is model.pre
+# define loss function here after getting prediction
+y = model.pred
+
+'''
+W.reverse()
+for i in range(num_layers):
+    if i == num_layers-1:
+        fmap_out = channel
+    c = tf.Variable(tf.ones(shape=[fmap_out], dtype=tf.float32), name="decoder_b_%d"%i)
+    de_conv = tf.nn.conv2d_transpose(fan_in, W[i], [tf.shape(X)[0], img_height, img_width, fmap_out], strides, padding='SAME', name="decoder_conv_%d"%i)
+    fan_in = tf.tanh(tf.add(de_conv, c))
+y = fan_in    
+'''
+
+
+# In[10]:
+
+loss = tf.reduce_mean(tf.square(X - y))
+trainer = tf.train.GradientDescentOptimizer(1e-4).minimize(loss)
+
+
+# In[5]:
+'''
+import cPickle
+data =  cPickle.load(open('cifar-100-python/test', 'r'))['data']
+data = np.reshape(data, (data.shape[0], 3, 32, 32))
+data = np.transpose(data, (0, 2, 3, 1))
+#data = (data - np.mean(data))/np.std(data)
+'''
+
+
+# In[ ]:
+epochs = 5
+num_batches = 1#mnist.train.num_examples // batch_size
+
+with tf.Session() as sess:
+    sess.run(tf.initialize_all_variables())
+    
+    for i in range(epochs):
+        for j in range(num_batches):
+            batch_X = binarize(mnist.train.next_batch(batch_size)[0].reshape(batch_size, img_height, img_width, channel))
+            condition = sess.run([fan_in], feed_dict={X:batch_X})
+            # TODO shape of condition does  not match: (1, 10, 28, 28, 32) for (?, 10)
+            _, l = sess.run([trainer, loss], feed_dict={X:batch_X, model.X:batch_X, model.h: condition[0]})
+            #batch_X = data[:10]/255.0
+            #_, l = sess.run([trainer, loss], feed_dict={X:batch_X})
+        print l
+
+    n_examples = 10
+    #test_X = mnist.train.next_batch(n_examples)[0].reshape(n_examples, img_height, img_width, channel)
+ 
+    o_test_X = mnist.test.next_batch(10)[0].reshape(10, img_height, img_width, channel)
+    test_X = binarize(o_test_X)
+    condition = sess.run(fan_in, feed_dict={X:test_X})
+    samples = sess.run(y, feed_dict={X: test_X, model.X:test_X, model.h:condition})
+    print samples.shape
+    #test_X = data[:10]
+    #samples = sess.run(y, feed_dict={X:test_X/255.0})
+    fig, axs = plt.subplots(2, n_examples, figsize=(10,2))
+    for i in range(n_examples):
+        axs[0][i].imshow(np.reshape(o_test_X[i], (img_height, img_width)), cmap='binary')
+        axs[1][i].imshow(np.reshape(samples[i], (img_height, img_width)), cmap='binary')
+    fig.show()
+    plt.draw()
+    plt.waitforbuttonpress()
+
+