Merge pull request #52 from Lupino/master

update to support tensorflow 1.0.0

src/optimize.py

@@ -72,7 +72,7 @@ def optimize(content_targets, style_target, content_weight, style_weight,
             size = height * width * filters
             feats = tf.reshape(layer, (bs, height * width, filters))
             feats_T = tf.transpose(feats, perm=[0,2,1])
-            grams = tf.batch_matmul(feats_T, feats) / size
+            grams = tf.matmul(feats_T, feats) / size
             style_gram = style_features[style_layer]
             style_losses.append(2 * tf.nn.l2_loss(grams - style_gram)/style_gram.size)
 
@@ -89,7 +89,7 @@ def optimize(content_targets, style_target, content_weight, style_weight,
 
         # overall loss
         train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)
-        sess.run(tf.initialize_all_variables())
+        sess.run(tf.global_variables_initializer())
         import random
         uid = random.randint(1, 100)
         print("UID: %s" % uid)

src/transform.py

@@ -35,7 +35,7 @@ def _conv_tranpose_layer(net, num_filters, filter_size, strides):
     # new_shape = #tf.pack([tf.shape(net)[0], new_rows, new_cols, num_filters])
 
     new_shape = [batch_size, new_rows, new_cols, num_filters]
-    tf_shape = tf.pack(new_shape)
+    tf_shape = tf.stack(new_shape)
     strides_shape = [1,strides,strides,1]
 
     net = tf.nn.conv2d_transpose(net, weights_init, tf_shape, strides_shape, padding='SAME')