Replace get_shape with shape everywhere. Remove trailing white spaces.

README.md

@@ -754,7 +754,7 @@ import tensorflow as tf
 
 def get_shape(tensor):
   """Returns static shape if available and dynamic shape otherwise."""
-  static_shape = tensor.get_shape().as_list()
+  static_shape = tensor.shape.as_list()
   dynamic_shape = tf.unstack(tf.shape(tensor))
   dims = [s[1] if s[0] is None else s[0]
           for s in zip(static_shape, dynamic_shape)]
@@ -777,7 +777,7 @@ def batch_gather(tensor, indices):
   shape = get_shape(tensor)
   flat_first = tf.reshape(tensor, [shape[0] * shape[1]] + shape[2:])
   indices = tf.convert_to_tensor(indices)
-  offset_shape = [shape[0]] + [1] * (indices.get_shape().ndims - 1)
+  offset_shape = [shape[0]] + [1] * (indices.shape.ndims - 1)
   offset = tf.reshape(tf.range(shape[0]) * shape[1], offset_shape)
   output = tf.gather(flat_first, indices + offset)
   return output
@@ -799,7 +799,7 @@ def rnn_beam_search(update_fn, initial_state, sequence_length, beam_width,
     ids: Output indices.
     logprobs: Output log probabilities probabilities.
   """
-  batch_size = initial_state.get_shape().as_list()[0]
+  batch_size = initial_state.shape.as_list()[0]
 
   state = tf.tile(tf.expand_dims(initial_state, axis=1), [1, beam_width, 1])
 
@@ -820,7 +820,7 @@ def rnn_beam_search(update_fn, initial_state, sequence_length, beam_width,
           tf.expand_dims(sel_sum_logprobs, axis=2) +
           (logits * tf.expand_dims(mask, axis=2)))
 
-      num_classes = logits.get_shape().as_list()[-1]
+      num_classes = logits.shape.as_list()[-1]
 
       sel_sum_logprobs, indices = tf.nn.top_k(
           tf.reshape(sum_logprobs, [batch_size, num_classes * beam_width]),

code/framework/common/ops.py

@@ -8,7 +8,7 @@
 
 def get_shape(tensor):
   """Returns static shape if available and dynamic shape otherwise."""
-  static_shape = tensor.get_shape().as_list()
+  static_shape = tensor.shape.as_list()
   dynamic_shape = tf.unstack(tf.shape(tensor))
   dims = [s[1] if s[0] is None else s[0]
           for s in zip(static_shape, dynamic_shape)]
@@ -30,12 +30,12 @@ def reshape(tensor, dims_list):
   return tensor
 
 
-def dense_layers(tensor, 
+def dense_layers(tensor,
                  sizes,
                  activation=tf.nn.relu,
                  linear_top_layer=False,
                  drop_rate=0.0,
-                 name=None, 
+                 name=None,
                  **kwargs):
   """Builds a stack of fully connected layers with optional dropout."""
   with tf.variable_scope(name, default_name='dense_layers'):
@@ -52,19 +52,19 @@ def dense_layers(tensor,
   return tensor
 
 
-def conv_layers(tensor, 
-                filters, 
-                kernels, 
-                pools, 
-                padding="same", 
+def conv_layers(tensor,
+                filters,
+                kernels,
+                pools,
+                padding="same",
                 activation=tf.nn.relu,
                 drop_rate=0.0,
                 **kwargs):
   for fs, ks, ps in zip(filters, kernels, pools):
     tensor = tf.layers.dropout(tensor, drop_rate)
     tensor = tf.layers.conv2d(
-        tensor, 
-        filters=fs, 
+        tensor,
+        filters=fs,
         kernel_size=ks,
         padding=padding,
         activation=activation,

code/framework/dataset/mnist.py

@@ -55,12 +55,12 @@ def _download_data():
   if not os.path.exists(LOCAL_DIR):
     os.makedirs(LOCAL_DIR)
   for name in [
-    TRAIN_IMAGE_URL, 
-    TRAIN_LABEL_URL, 
-    TEST_IMAGE_URL, 
+    TRAIN_IMAGE_URL,
+    TRAIN_LABEL_URL,
+    TEST_IMAGE_URL,
     TEST_LABEL_URL]:
     if not os.path.exists(LOCAL_DIR + name):
-      urllib.request.urlretrieve(REMOTE_URL + name, LOCAL_DIR + name)  
+      urllib.request.urlretrieve(REMOTE_URL + name, LOCAL_DIR + name)
 
 
 def _image_iterator(split):
@@ -69,7 +69,7 @@ def _image_iterator(split):
     tf.estimator.ModeKeys.EVAL: TEST_IMAGE_URL
   }[split]
   label_urls = {
-    tf.estimator.ModeKeys.TRAIN: TRAIN_LABEL_URL, 
+    tf.estimator.ModeKeys.TRAIN: TRAIN_LABEL_URL,
     tf.estimator.ModeKeys.EVAL: TEST_LABEL_URL
   }[split]
 

code/framework/model/convnet_classifier.py

@@ -24,15 +24,15 @@ def model_fn(features, labels, mode, params):
   drop_rate = params.drop_rate if mode == tf.estimator.ModeKeys.TRAIN else 0.0
 
   features = ops.conv_layers(
-    images, 
-    filters=[32, 64, 128], 
+    images,
+    filters=[32, 64, 128],
     kernels=[3, 3, 3],
     pools=[2, 2, 2])
 
   features = tf.contrib.layers.flatten(features)
 
   logits = ops.dense_layers(
-    features, [512, params.num_classes], 
+    features, [512, params.num_classes],
     drop_rate=drop_rate,
     linear_top_layer=True)