Converted the models repo to TF 1.0 using the upgrade script

slim/nets/inception_v3.py

@@ -158,7 +158,7 @@ def inception_v3_base(inputs,
           branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
           branch_3 = slim.conv2d(branch_3, depth(32), [1, 1],
                                  scope='Conv2d_0b_1x1')
-        net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
       end_points[end_point] = net
       if end_point == final_endpoint: return net, end_points
 
@@ -182,7 +182,7 @@ def inception_v3_base(inputs,
           branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
           branch_3 = slim.conv2d(branch_3, depth(64), [1, 1],
                                  scope='Conv2d_0b_1x1')
-        net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
       end_points[end_point] = net
       if end_point == final_endpoint: return net, end_points
 
@@ -205,7 +205,7 @@ def inception_v3_base(inputs,
           branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
           branch_3 = slim.conv2d(branch_3, depth(64), [1, 1],
                                  scope='Conv2d_0b_1x1')
-        net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
       end_points[end_point] = net
       if end_point == final_endpoint: return net, end_points
 
@@ -224,7 +224,7 @@ def inception_v3_base(inputs,
         with tf.variable_scope('Branch_2'):
           branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
                                      scope='MaxPool_1a_3x3')
-        net = tf.concat(3, [branch_0, branch_1, branch_2])
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
       end_points[end_point] = net
       if end_point == final_endpoint: return net, end_points
 
@@ -253,7 +253,7 @@ def inception_v3_base(inputs,
           branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
           branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
                                  scope='Conv2d_0b_1x1')
-        net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
       end_points[end_point] = net
       if end_point == final_endpoint: return net, end_points
 
@@ -282,7 +282,7 @@ def inception_v3_base(inputs,
           branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
           branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
                                  scope='Conv2d_0b_1x1')
-        net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
       end_points[end_point] = net
       if end_point == final_endpoint: return net, end_points
       # mixed_6: 17 x 17 x 768.
@@ -310,7 +310,7 @@ def inception_v3_base(inputs,
           branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
           branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
                                  scope='Conv2d_0b_1x1')
-        net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
       end_points[end_point] = net
       if end_point == final_endpoint: return net, end_points
 
@@ -339,7 +339,7 @@ def inception_v3_base(inputs,
           branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
           branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
                                  scope='Conv2d_0b_1x1')
-        net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
       end_points[end_point] = net
       if end_point == final_endpoint: return net, end_points
 
@@ -361,7 +361,7 @@ def inception_v3_base(inputs,
         with tf.variable_scope('Branch_2'):
           branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
                                      scope='MaxPool_1a_3x3')
-        net = tf.concat(3, [branch_0, branch_1, branch_2])
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
       end_points[end_point] = net
       if end_point == final_endpoint: return net, end_points
       # mixed_9: 8 x 8 x 2048.
@@ -371,21 +371,21 @@ def inception_v3_base(inputs,
           branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
         with tf.variable_scope('Branch_1'):
           branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
-          branch_1 = tf.concat(3, [
+          branch_1 = tf.concat(axis=3, values=[
               slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
               slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0b_3x1')])
         with tf.variable_scope('Branch_2'):
           branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
           branch_2 = slim.conv2d(
               branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
-          branch_2 = tf.concat(3, [
+          branch_2 = tf.concat(axis=3, values=[
               slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
               slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')])
         with tf.variable_scope('Branch_3'):
           branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
           branch_3 = slim.conv2d(
               branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
-        net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
       end_points[end_point] = net
       if end_point == final_endpoint: return net, end_points
 
@@ -396,21 +396,21 @@ def inception_v3_base(inputs,
           branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
         with tf.variable_scope('Branch_1'):
           branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
-          branch_1 = tf.concat(3, [
+          branch_1 = tf.concat(axis=3, values=[
               slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
               slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0c_3x1')])
         with tf.variable_scope('Branch_2'):
           branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
           branch_2 = slim.conv2d(
               branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
-          branch_2 = tf.concat(3, [
+          branch_2 = tf.concat(axis=3, values=[
               slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
               slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')])
         with tf.variable_scope('Branch_3'):
           branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
           branch_3 = slim.conv2d(
               branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
-        net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
       end_points[end_point] = net
       if end_point == final_endpoint: return net, end_points
     raise ValueError('Unknown final endpoint %s' % final_endpoint)

slim/nets/inception_v4.py

@@ -49,7 +49,7 @@ def block_inception_a(inputs, scope=None, reuse=None):
       with tf.variable_scope('Branch_3'):
         branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
         branch_3 = slim.conv2d(branch_3, 96, [1, 1], scope='Conv2d_0b_1x1')
-      return tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+      return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
 
 
 def block_reduction_a(inputs, scope=None, reuse=None):
@@ -69,7 +69,7 @@ def block_reduction_a(inputs, scope=None, reuse=None):
       with tf.variable_scope('Branch_2'):
         branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID',
                                    scope='MaxPool_1a_3x3')
-      return tf.concat(3, [branch_0, branch_1, branch_2])
+      return tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
 
 
 def block_inception_b(inputs, scope=None, reuse=None):
@@ -93,7 +93,7 @@ def block_inception_b(inputs, scope=None, reuse=None):
       with tf.variable_scope('Branch_3'):
         branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
         branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
-      return tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+      return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
 
 
 def block_reduction_b(inputs, scope=None, reuse=None):
@@ -115,7 +115,7 @@ def block_reduction_b(inputs, scope=None, reuse=None):
       with tf.variable_scope('Branch_2'):
         branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID',
                                    scope='MaxPool_1a_3x3')
-      return tf.concat(3, [branch_0, branch_1, branch_2])
+      return tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
 
 
 def block_inception_c(inputs, scope=None, reuse=None):
@@ -128,20 +128,20 @@ def block_inception_c(inputs, scope=None, reuse=None):
         branch_0 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1')
       with tf.variable_scope('Branch_1'):
         branch_1 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
-        branch_1 = tf.concat(3, [
+        branch_1 = tf.concat(axis=3, values=[
             slim.conv2d(branch_1, 256, [1, 3], scope='Conv2d_0b_1x3'),
             slim.conv2d(branch_1, 256, [3, 1], scope='Conv2d_0c_3x1')])
       with tf.variable_scope('Branch_2'):
         branch_2 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
         branch_2 = slim.conv2d(branch_2, 448, [3, 1], scope='Conv2d_0b_3x1')
         branch_2 = slim.conv2d(branch_2, 512, [1, 3], scope='Conv2d_0c_1x3')
-        branch_2 = tf.concat(3, [
+        branch_2 = tf.concat(axis=3, values=[
             slim.conv2d(branch_2, 256, [1, 3], scope='Conv2d_0d_1x3'),
             slim.conv2d(branch_2, 256, [3, 1], scope='Conv2d_0e_3x1')])
       with tf.variable_scope('Branch_3'):
         branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
         branch_3 = slim.conv2d(branch_3, 256, [1, 1], scope='Conv2d_0b_1x1')
-      return tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+      return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
 
 
 def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None):
@@ -192,7 +192,7 @@ def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None):
         with tf.variable_scope('Branch_1'):
           branch_1 = slim.conv2d(net, 96, [3, 3], stride=2, padding='VALID',
                                  scope='Conv2d_0a_3x3')
-        net = tf.concat(3, [branch_0, branch_1])
+        net = tf.concat(axis=3, values=[branch_0, branch_1])
         if add_and_check_final('Mixed_3a', net): return net, end_points
 
       # 73 x 73 x 160
@@ -207,7 +207,7 @@ def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None):
           branch_1 = slim.conv2d(branch_1, 64, [7, 1], scope='Conv2d_0c_7x1')
           branch_1 = slim.conv2d(branch_1, 96, [3, 3], padding='VALID',
                                  scope='Conv2d_1a_3x3')
-        net = tf.concat(3, [branch_0, branch_1])
+        net = tf.concat(axis=3, values=[branch_0, branch_1])
         if add_and_check_final('Mixed_4a', net): return net, end_points
 
       # 71 x 71 x 192
@@ -218,7 +218,7 @@ def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None):
         with tf.variable_scope('Branch_1'):
           branch_1 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
                                      scope='MaxPool_1a_3x3')
-        net = tf.concat(3, [branch_0, branch_1])
+        net = tf.concat(axis=3, values=[branch_0, branch_1])
         if add_and_check_final('Mixed_5a', net): return net, end_points
 
       # 35 x 35 x 384

slim/nets/overfeat.py

@@ -41,7 +41,7 @@ def overfeat_arg_scope(weight_decay=0.0005):
   with slim.arg_scope([slim.conv2d, slim.fully_connected],
                       activation_fn=tf.nn.relu,
                       weights_regularizer=slim.l2_regularizer(weight_decay),
-                      biases_initializer=tf.zeros_initializer):
+                      biases_initializer=tf.zeros_initializer()):
     with slim.arg_scope([slim.conv2d], padding='SAME'):
       with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc:
         return arg_sc
@@ -107,7 +107,7 @@ def overfeat(inputs,
         net = slim.conv2d(net, num_classes, [1, 1],
                           activation_fn=None,
                           normalizer_fn=None,
-                          biases_initializer=tf.zeros_initializer,
+                          biases_initializer=tf.zeros_initializer(),
                           scope='fc8')
       # Convert end_points_collection into a end_point dict.
       end_points = slim.utils.convert_collection_to_dict(end_points_collection)

slim/nets/vgg.py

@@ -58,7 +58,7 @@ def vgg_arg_scope(weight_decay=0.0005):
   with slim.arg_scope([slim.conv2d, slim.fully_connected],
                       activation_fn=tf.nn.relu,
                       weights_regularizer=slim.l2_regularizer(weight_decay),
-                      biases_initializer=tf.zeros_initializer):
+                      biases_initializer=tf.zeros_initializer()):
     with slim.arg_scope([slim.conv2d], padding='SAME') as arg_sc:
       return arg_sc
 

slim/preprocessing/cifarnet_preprocessing.py

@@ -45,7 +45,7 @@ def preprocess_for_train(image,
   Returns:
     A preprocessed image.
   """
-  tf.image_summary('image', tf.expand_dims(image, 0))
+  tf.summary.image('image', tf.expand_dims(image, 0))
 
   # Transform the image to floats.
   image = tf.to_float(image)
@@ -58,7 +58,7 @@ def preprocess_for_train(image,
   # Randomly flip the image horizontally.
   distorted_image = tf.image.random_flip_left_right(distorted_image)
 
-  tf.image_summary('distorted_image', tf.expand_dims(distorted_image, 0))
+  tf.summary.image('distorted_image', tf.expand_dims(distorted_image, 0))
 
   # Because these operations are not commutative, consider randomizing
   # the order their operation.
@@ -67,7 +67,7 @@ def preprocess_for_train(image,
   distorted_image = tf.image.random_contrast(distorted_image,
                                              lower=0.2, upper=1.8)
   # Subtract off the mean and divide by the variance of the pixels.
-  return tf.image.per_image_whitening(distorted_image)
+  return tf.image.per_image_standardization(distorted_image)
 
 
 def preprocess_for_eval(image, output_height, output_width):
@@ -81,7 +81,7 @@ def preprocess_for_eval(image, output_height, output_width):
   Returns:
     A preprocessed image.
   """
-  tf.image_summary('image', tf.expand_dims(image, 0))
+  tf.summary.image('image', tf.expand_dims(image, 0))
   # Transform the image to floats.
   image = tf.to_float(image)
 
@@ -89,10 +89,10 @@ def preprocess_for_eval(image, output_height, output_width):
   resized_image = tf.image.resize_image_with_crop_or_pad(image,
                                                          output_width,
                                                          output_height)
-  tf.image_summary('resized_image', tf.expand_dims(resized_image, 0))
+  tf.summary.image('resized_image', tf.expand_dims(resized_image, 0))
 
   # Subtract off the mean and divide by the variance of the pixels.
-  return tf.image.per_image_whitening(resized_image)
+  return tf.image.per_image_standardization(resized_image)
 
 
 def preprocess_image(image, output_height, output_width, is_training=False):

slim/preprocessing/inception_preprocessing.py

@@ -192,7 +192,7 @@ def preprocess_for_train(image, height, width, bbox,
     # the coordinates are ordered [ymin, xmin, ymax, xmax].
     image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
                                                   bbox)
-    tf.image_summary('image_with_bounding_boxes', image_with_box)
+    tf.summary.image('image_with_bounding_boxes', image_with_box)
 
     distorted_image, distorted_bbox = distorted_bounding_box_crop(image, bbox)
     # Restore the shape since the dynamic slice based upon the bbox_size loses
@@ -200,7 +200,7 @@ def preprocess_for_train(image, height, width, bbox,
     distorted_image.set_shape([None, None, 3])
     image_with_distorted_box = tf.image.draw_bounding_boxes(
         tf.expand_dims(image, 0), distorted_bbox)
-    tf.image_summary('images_with_distorted_bounding_box',
+    tf.summary.image('images_with_distorted_bounding_box',
                      image_with_distorted_box)
 
     # This resizing operation may distort the images because the aspect
@@ -215,7 +215,7 @@ def preprocess_for_train(image, height, width, bbox,
         lambda x, method: tf.image.resize_images(x, [height, width], method=method),
         num_cases=num_resize_cases)
 
-    tf.image_summary('cropped_resized_image',
+    tf.summary.image('cropped_resized_image',
                      tf.expand_dims(distorted_image, 0))
 
     # Randomly flip the image horizontally.
@@ -227,10 +227,10 @@ def preprocess_for_train(image, height, width, bbox,
         lambda x, ordering: distort_color(x, ordering, fast_mode),
         num_cases=4)
 
-    tf.image_summary('final_distorted_image',
+    tf.summary.image('final_distorted_image',
                      tf.expand_dims(distorted_image, 0))
-    distorted_image = tf.sub(distorted_image, 0.5)
-    distorted_image = tf.mul(distorted_image, 2.0)
+    distorted_image = tf.subtract(distorted_image, 0.5)
+    distorted_image = tf.multiply(distorted_image, 2.0)
     return distorted_image
 
 
@@ -270,8 +270,8 @@ def preprocess_for_eval(image, height, width,
       image = tf.image.resize_bilinear(image, [height, width],
                                        align_corners=False)
       image = tf.squeeze(image, [0])
-    image = tf.sub(image, 0.5)
-    image = tf.mul(image, 2.0)
+    image = tf.subtract(image, 0.5)
+    image = tf.multiply(image, 2.0)
     return image
 
 

slim/preprocessing/lenet_preprocessing.py

@@ -39,6 +39,6 @@ def preprocess_image(image, output_height, output_width, is_training):
   image = tf.to_float(image)
   image = tf.image.resize_image_with_crop_or_pad(
       image, output_width, output_height)
-  image = tf.sub(image, 128.0)
+  image = tf.subtract(image, 128.0)
   image = tf.div(image, 128.0)
   return image

slim/preprocessing/vgg_preprocessing.py

@@ -73,7 +73,7 @@ def _crop(image, offset_height, offset_width, crop_height, crop_width):
       ['Rank of image must be equal to 3.'])
   cropped_shape = control_flow_ops.with_dependencies(
       [rank_assertion],
-      tf.pack([crop_height, crop_width, original_shape[2]]))
+      tf.stack([crop_height, crop_width, original_shape[2]]))
 
   size_assertion = tf.Assert(
       tf.logical_and(
@@ -81,7 +81,7 @@ def _crop(image, offset_height, offset_width, crop_height, crop_width):
           tf.greater_equal(original_shape[1], crop_width)),
       ['Crop size greater than the image size.'])
 
-  offsets = tf.to_int32(tf.pack([offset_height, offset_width, 0]))
+  offsets = tf.to_int32(tf.stack([offset_height, offset_width, 0]))
 
   # Use tf.slice instead of crop_to_bounding box as it accepts tensors to
   # define the crop size.
@@ -227,10 +227,10 @@ def _mean_image_subtraction(image, means):
   if len(means) != num_channels:
     raise ValueError('len(means) must match the number of channels')
 
-  channels = tf.split(2, num_channels, image)
+  channels = tf.split(axis=2, num_or_size_splits=num_channels, value=image)
   for i in range(num_channels):
     channels[i] -= means[i]
-  return tf.concat(2, channels)
+  return tf.concat(axis=2, values=channels)
 
 
 def _smallest_size_at_least(height, width, smallest_side):

slim/train_image_classifier.py

@@ -316,8 +316,8 @@ def _configure_optimizer(learning_rate):
 def _add_variables_summaries(learning_rate):
   summaries = []
   for variable in slim.get_model_variables():
-    summaries.append(tf.histogram_summary(variable.op.name, variable))
-  summaries.append(tf.scalar_summary('training/Learning Rate', learning_rate))
+    summaries.append(tf.summary.histogram(variable.op.name, variable))
+  summaries.append(tf.summary.scalar('training/Learning Rate', learning_rate))
   return summaries
 
 
@@ -489,17 +489,17 @@ def main(_):
     end_points = clones[0].outputs
     for end_point in end_points:
       x = end_points[end_point]
-      summaries.add(tf.histogram_summary('activations/' + end_point, x))
-      summaries.add(tf.scalar_summary('sparsity/' + end_point,
+      summaries.add(tf.summary.histogram('activations/' + end_point, x))
+      summaries.add(tf.summary.scalar('sparsity/' + end_point,
                                       tf.nn.zero_fraction(x)))
 
     # Add summaries for losses.
     for loss in tf.get_collection(tf.GraphKeys.LOSSES, first_clone_scope):
-      summaries.add(tf.scalar_summary('losses/%s' % loss.op.name, loss))
+      summaries.add(tf.summary.scalar('losses/%s' % loss.op.name, loss))
 
     # Add summaries for variables.
     for variable in slim.get_model_variables():
-      summaries.add(tf.histogram_summary(variable.op.name, variable))
+      summaries.add(tf.summary.histogram(variable.op.name, variable))
 
     #################################
     # Configure the moving averages #
@@ -517,7 +517,7 @@ def main(_):
     with tf.device(deploy_config.optimizer_device()):
       learning_rate = _configure_learning_rate(dataset.num_samples, global_step)
       optimizer = _configure_optimizer(learning_rate)
-      summaries.add(tf.scalar_summary('learning_rate', learning_rate,
+      summaries.add(tf.summary.scalar('learning_rate', learning_rate,
                                       name='learning_rate'))
 
     if FLAGS.sync_replicas:
@@ -543,7 +543,7 @@ def main(_):
         optimizer,
         var_list=variables_to_train)
     # Add total_loss to summary.
-    summaries.add(tf.scalar_summary('total_loss', total_loss,
+    summaries.add(tf.summary.scalar('total_loss', total_loss,
                                     name='total_loss'))
 
     # Create gradient updates.
@@ -561,7 +561,7 @@ def main(_):
                                        first_clone_scope))
 
     # Merge all summaries together.
-    summary_op = tf.merge_summary(list(summaries), name='summary_op')
+    summary_op = tf.summary.merge(list(summaries), name='summary_op')
 
 
     ###########################

street/python/nn_ops.py

@@ -92,7 +92,7 @@ def rnn_helper(inp,
     elif direction == "backward":
       out = backward
     else:
-      out = tf.concat(2, [forward, backward])
+      out = tf.concat(axis=2, values=[forward, backward])
   return out
 
 
@@ -183,7 +183,7 @@ def lstm_layer(inp,
   with tf.variable_scope(name):
     if backward:
       if length is None:
-        inp = tf.reverse(inp, [False, True, False])
+        inp = tf.reverse(inp, [1])
       else:
         inp = tf.reverse_sequence(inp, length, 1, 0)
 
@@ -217,14 +217,14 @@ def lstm_layer(inp,
 
     batch_size = shapes.tensor_dim(inp, dim=0)
     num_frames = shapes.tensor_dim(inp, dim=1)
-    prev = tf.reshape(inp, tf.pack([batch_size * num_frames, num_prev]))
+    prev = tf.reshape(inp, tf.stack([batch_size * num_frames, num_prev]))
 
     if use_native_weights:
       with tf.variable_scope("LSTMCell"):
         b = tf.get_variable(
             "B",
             shape=[4 * num_nodes],
-            initializer=tf.zeros_initializer,
+            initializer=tf.zeros_initializer(),
             dtype=tf.float32)
       biases = tf.identity(b, name="biases")
     else:
@@ -236,17 +236,17 @@ def lstm_layer(inp,
               biases, name="biases_reg"))
     prev = tf.nn.xw_plus_b(prev, w_i_m, biases)
 
-    prev = tf.reshape(prev, tf.pack([batch_size, num_frames, 4, num_nodes]))
+    prev = tf.reshape(prev, tf.stack([batch_size, num_frames, 4, num_nodes]))
     if state is None:
-      state = tf.fill(tf.pack([batch_size, num_nodes]), 0.0)
+      state = tf.fill(tf.stack([batch_size, num_nodes]), 0.0)
     if memory is None:
-      memory = tf.fill(tf.pack([batch_size, num_nodes]), 0.0)
+      memory = tf.fill(tf.stack([batch_size, num_nodes]), 0.0)
 
     out, _, mem = rnn.variable_lstm(prev, state, memory, w_m_m, clip=clip)
 
     if backward:
       if length is None:
-        out = tf.reverse(out, [False, True, False])
+        out = tf.reverse(out, [1])
       else:
         out = tf.reverse_sequence(out, length, 1, 0)
 

street/python/vgsl_input.py

@@ -79,7 +79,7 @@ def ImageInput(input_pattern, num_threads, shape, using_ctc, reader=None):
   # Give the images a nice name as well.
   images = tf.identity(images, name='Images')
 
-  tf.image_summary('Images', images)
+  tf.summary.image('Images', images)
   return images, heights, widths, labels, sparse_labels, truths
 
 
@@ -145,6 +145,6 @@ def _ImageProcessing(image_buffer, shape):
   image = tf.image.decode_png(image_buffer, channels=shape.depth)
   image.set_shape([shape.height, shape.width, shape.depth])
   image = tf.cast(image, tf.float32)
-  image = tf.sub(image, 128.0)
-  image = tf.mul(image, 1 / 100.0)
+  image = tf.subtract(image, 128.0)
+  image = tf.multiply(image, 1 / 100.0)
   return image

street/python/vgsl_model.py

@@ -147,7 +147,7 @@ def Eval(train_dir,
       sequence_error=None)
   with tf.Graph().as_default():
     model = InitNetwork(eval_data, model_str, 'eval', reader=reader)
-    sw = tf.train.SummaryWriter(eval_dir)
+    sw = tf.summary.FileWriter(eval_dir)
 
     while True:
       sess = tf.Session('')
@@ -369,7 +369,7 @@ class VGSLImageModel(object):
     if self.mode == 'train':
       # Setup loss for training.
       self.loss = self._AddLossFunction(logits, height_in, out_dims, out_func)
-      tf.scalar_summary('loss', self.loss, name='loss')
+      tf.summary.scalar('loss', self.loss, name='loss')
     elif out_dims == 0:
       # Be sure the labels match the output, even in eval mode.
       self.labels = tf.slice(self.labels, [0, 0], [-1, 1])
@@ -484,7 +484,7 @@ class VGSLImageModel(object):
       opt = tf.train.AdamOptimizer(learning_rate=learn_rate_dec)
     else:
       raise ValueError('Invalid optimizer type: ' + optimizer_type)
-    tf.scalar_summary('learn_rate', learn_rate_dec, name='lr_summ')
+    tf.summary.scalar('learn_rate', learn_rate_dec, name='lr_summ')
 
     self.train_op = opt.minimize(
         self.loss, global_step=self.global_step, name='train')

street/python/vgslspecs.py

@@ -149,7 +149,7 @@ class VGSLSpecs(object):
     else:
       lengths = tf.ones_like(lengths)
     if factor != 1:
-      lengths = tf.mul(lengths, tf.cast(factor, tf.float32))
+      lengths = tf.multiply(lengths, tf.cast(factor, tf.float32))
     return tf.cast(lengths, tf.int32)
 
   def BuildFromString(self, prev_layer, index):
@@ -235,7 +235,7 @@ class VGSLSpecs(object):
         final_factors = self.reduction_factors
     if index == len(self.model_str):
       raise ValueError('Missing ) at end of parallel!' + self.model_str)
-    return tf.concat(num_dims - 1, layers), index + 1
+    return tf.concat(axis=num_dims - 1, values=layers), index + 1
 
   def AddConvLayer(self, prev_layer, index):
     """Add a single standard convolutional layer.
@@ -342,7 +342,7 @@ class VGSLSpecs(object):
         factor1 = tf.cast(self.reduction_factors[i], tf.float32)
         factor2 = tf.cast(prev_shape[i], tf.float32)
         divisor = tf.cast(result_shape[i], tf.float32)
-        self.reduction_factors[i] = tf.div(tf.mul(factor1, factor2), divisor)
+        self.reduction_factors[i] = tf.div(tf.multiply(factor1, factor2), divisor)
     return layer, m.end()
 
   def AddFCLayer(self, prev_layer, index):
@@ -401,7 +401,7 @@ class VGSLSpecs(object):
                             name + '_forward')
       back = self._LSTMLayer(prev_layer, 'backward', dim, True, depth,
                              name + '_reverse')
-      return tf.concat(3, [fwd, back], name=name + '_concat'), m.end()
+      return tf.concat(axis=3, values=[fwd, back], name=name + '_concat'), m.end()
     if direction == 'f':
       direction = 'forward'
     elif direction == 'r':

swivel/swivel.py

@@ -135,8 +135,8 @@ def count_matrix_input(filenames, submatrix_rows, submatrix_cols):
   sparse_local_col = features['sparse_local_col'].values
   sparse_count = features['sparse_value'].values
 
-  sparse_indices = tf.concat([tf.expand_dims(sparse_local_row, 1),
-                              tf.expand_dims(sparse_local_col, 1)], 1)
+  sparse_indices = tf.concat(axis=[tf.expand_dims(sparse_local_row, 1),
+                              tf.expand_dims(sparse_local_col, 1)], values=1)
   count = tf.sparse_to_dense(sparse_indices, [submatrix_rows, submatrix_cols],
                              sparse_count)
 

syntaxnet/syntaxnet/graph_builder.py

@@ -69,7 +69,7 @@ def EmbeddingLookupFeatures(params, sparse_features, allow_weights):
 
   if allow_weights:
     # Multiply by weights, reshaping to allow broadcast.
-    broadcast_weights_shape = tf.concat([tf.shape(weights), [1]], 0)
+    broadcast_weights_shape = tf.concat(axis=[tf.shape(weights), [1]], values=0)
     embeddings *= tf.reshape(weights, broadcast_weights_shape)
 
   # Sum embeddings by index.
@@ -330,7 +330,7 @@ class GreedyParser(object):
                                            i,
                                            return_average=return_average))
 
-    last_layer = tf.concat(embeddings, 1)
+    last_layer = tf.concat(axis=embeddings, values=1)
     last_layer_size = self.embedding_size
 
     # Create ReLU layers.