pending changes

autoencoder.py

@@ -60,7 +60,6 @@ def trainPixelCNNAE(conf, data):
 
     encoder = AE(encoder_X)
     conf.num_classes = int(encoder.fan_in.get_shape()[1])
-    # TODO keep X out for main.py also
     decoder = PixelCNN(decoder_X, conf, encoder.fan_in)
     y = decoder.pred
 
@@ -74,16 +73,16 @@ def trainPixelCNNAE(conf, data):
             saver.restore(sess, conf.ckpt_file)
             print "Model Restored"
 
+        pointer = 0
         for i in range(conf.epochs):
             for j in range(conf.num_batches):
                 if conf.data == 'mnist':
                     batch_X = binarize(data.train.next_batch(conf.batch_size)[0].reshape(conf.batch_size, conf.img_height, conf.img_width, conf.channel))
                 else:
-                    # TODO move batch
-                    batch_X = data[0][0][:conf.batch_size]
-                #condition = sess.run(encoder.fan_in, feed_dict={encoder_X:batch_X})
-                _, l = sess.run([trainer, loss], feed_dict={encoder_X: batch_X, decoder_X:batch_X})
-            print l
+                    batch_X, pointer = get_batch(data, pointer, conf.batch_size)
+
+                _, l = sess.run([trainer, loss], feed_dict={encoder_X: batch_X, decoder_X: batch_X})
+            print "Epoch: %d, Cost: %f"%(i, l)
 
         saver.save(sess, conf.ckpt_file)
         generate_ae(sess, encoder_X, decoder_X, y, data, conf)
@@ -116,9 +115,12 @@ class Conf(object):
         from keras.datasets import cifar10
         data = cifar10.load_data()
         # TODO normalize pixel values
-        data[0][0] = np.transpose(data[0][0], (0, 2, 3, 1))
-        data[1][0] = np.transpose(data[1][0], (0, 2, 3, 1))
-        train_size = data[0][0].shape[0] 
+        data = data[0][0]
+        data = np.transpose(data, (0, 2, 3, 1))
+        conf.img_height = 32
+        conf.img_width = 32
+        conf.channel = 3
+        train_size = data.shape[0] 
 
     conf.num_batches = train_size // conf.batch_size
     conf = makepaths(conf)

main.py

@@ -5,7 +5,8 @@
 from utils import *
 
 def train(conf, data):
-    model = PixelCNN(conf)
+    X = tf.placeholder(tf.float32, shape=[None, conf.img_height, conf.img_width, conf.channel])
+    model = PixelCNN(conf, X)
 
     trainer = tf.train.RMSPropOptimizer(1e-3)
     gradients = trainer.compute_gradients(model.loss)
@@ -32,12 +33,12 @@ def train(conf, data):
                 else:
                     batch_X, pointer = get_batch(data, pointer, conf.batch_size)
 
-                _, cost = sess.run([optimizer, model.loss], feed_dict={model.X:batch_X, model.h:batch_y})
+                _, cost = sess.run([optimizer, model.loss], feed_dict={X:batch_X, model.h:batch_y})
 
             print "Epoch: %d, Cost: %f"%(i, cost)
 
         saver.save(sess, conf.ckpt_file)
-        generate_samples(sess, model.X, model.h, model.pred, conf)
+        generate_samples(sess, X, model.h, model.pred, conf)
 
 
 if __name__ == "__main__":
@@ -66,20 +67,17 @@ def train(conf, data):
         conf.channel = 1
         conf.num_batches = data.train.num_examples // conf.batch_size
     else:
-        import cPickle
-        data = cPickle.load(open('cifar-100-python/train', 'r'))['data']
+        from keras.datasets import cifar10
+        data = cifar10.load_data()
+        data = data[0][0]
+        data = np.transpose(data, (0, 2, 3, 1))
         conf.img_height = 32
         conf.img_width = 32
         conf.channel = 3
-        data = np.reshape(data, (data.shape[0], conf.channel, \
-                conf.img_height, conf.img_width))
-        data = np.transpose(data, (0, 2, 3, 1))
         raise ValueError("Specify num_classes")
         conf.num_classes = 10
-        conf.num_batches = 10#data.shape[0] // conf.batch_size
+        conf.num_batches = data.shape[0] // conf.batch_size
 
-        # Implementing tf.image.per_image_whitening for normalization
-        # data = (data-np.mean(data)) / max(np.std(data), 1.0/np.sqrt(sum(data.shape))) * 255.0
 
     conf = makepaths(conf) 
     train(conf, data)

utils.py

@@ -24,10 +24,12 @@ def generate_samples(sess, X, h, pred, conf):
 
 
 def generate_ae(sess, encoder_X, decoder_X, y, data, conf):
-    n_row, n_col = 3, 3
+    n_row, n_col = 5, 5
     samples = np.zeros((n_row*n_col, conf.img_height, conf.img_width, conf.channel), dtype=np.float32)
     if conf.data == 'mnist':
         labels = binarize(data.train.next_batch(n_row*n_col)[0].reshape(n_row*n_col, conf.img_height, conf.img_width, conf.channel))
+    else:
+        labels = get_batch(data, 0, n_row*n_col) 
 
     for i in xrange(conf.img_height):
         for j in xrange(conf.img_width):