Add auto encoder model

Author: Lukas Herman

5_Auto_Encoder.py

@@ -0,0 +1,90 @@
+import tensorflow as tf
+from tensorflow.examples.tutorials.mnist import input_data
+import sys
+from os.path import join, splitext
+from layers import layers
+import config
+
+train_dir = config.TRAIN_DIR
+test_dir = config.TEST_DIR
+n_trains = config.N_TRAINS
+batch_size = config.BATCH_SIZE
+
+width = config.WIDTH
+height = config.HEIGHT
+channels = config.CHANNELS
+flat = config.FLAT
+n_classes = config.N_CLASSES
+
+k = 1000
+l = 500
+m = 30
+n = l
+o = k
+
+mnist = input_data.read_data_sets('data', one_hot=True)
+
+def get_dict(train=True, batch=True):
+    if train:
+        if batch:
+            batch_x, _ = mnist.train.next_batch(batch_size)
+            return {x:batch_x}
+        else:
+            return {x:mnist.train.images}
+    else:
+        if batch:
+            batch_x, _ = mnist.test.next_batch(batch_size)
+            return {x:batch_x}
+        else:
+            return {x:mnist.test.images}
+
+with tf.name_scope('InputLayer'):
+    x = tf.placeholder(tf.float32, shape=[None, flat], name='x')
+
+with tf.name_scope('NetworkModel'):
+    with tf.name_scope('Encoder'):
+        y1 = layers.ae_layer(x, flat, k)
+        y2 = layers.ae_layer(y1, k, l)
+        y3 = layers.ae_layer(y2, l, m)
+    with tf.name_scope('Decoder'):
+        y4 = layers.ae_layer(y3, m, n)
+        y5 = layers.ae_layer(y4, n, o)
+        y = layers.ae_layer(y5, o, flat)
+
+with tf.name_scope('Train'):
+    loss = tf.reduce_mean(tf.pow(y-x, 2), name='loss')
+    train = tf.train.AdamOptimizer().minimize(loss)
+
+with tf.name_scope('Accuracy'):
+    accuracy = 1 - loss
+
+# Add image summaries
+x_img = tf.reshape(x, [-1, height, width, channels]) # input
+y_img = tf.reshape(y, [-1, height, width, channels]) # reconstructed
+tf.summary.image('InputImage', x_img)
+tf.summary.image('OutputImage', y_img)
+
+# Add scalar summaries
+tf.summary.scalar('Loss', loss)
+tf.summary.scalar('Accuracy', accuracy)
+
+init_op = tf.global_variables_initializer()
+summary_op = tf.summary.merge_all()
+
+with tf.Session() as sess:
+    # Open protocol for writing files
+    train_writer = tf.summary.FileWriter(train_dir)
+    train_writer.add_graph(sess.graph)
+    test_writer = tf.summary.FileWriter(test_dir)
+
+    sess.run(init_op)
+    for n_train in range(1, n_trains+1):
+        print("Training {}...".format(n_train))
+        _ = sess.run([train], feed_dict=get_dict(train=True, batch=True))
+        if n_train % 100 == 0:
+            # Train
+            s = sess.run(summary_op, feed_dict=get_dict(train=True, batch=False))
+            train_writer.add_summary(s, n_train)
+            # Test
+            s = sess.run(summary_op, feed_dict=get_dict(train=False, batch=False))
+            test_writer.add_summary(s, n_train)