Trivial update of layer imports in eager execution examples, to reflect recommended...

PiperOrigin-RevId: 190319480

diff --git a/tensorflow/contrib/eager/python/examples/gan/mnist.py b/tensorflow/contrib/eager/python/examples/gan/mnist.py
index 2b7e199..b80c909 100644 (file)
--- a/tensorflow/contrib/eager/python/examples/gan/mnist.py
+++ b/tensorflow/contrib/eager/python/examples/gan/mnist.py
@@ -32,6 +32,7 @@ import tensorflow as tf
 import tensorflow.contrib.eager as tfe
 from tensorflow.examples.tutorials.mnist import input_data
 
+layers = tf.keras.layers
 FLAGS = None
 
 
@@ -56,15 +57,15 @@ class Discriminator(tf.keras.Model):
     else:
       assert data_format == 'channels_last'
       self._input_shape = [-1, 28, 28, 1]
-    self.conv1 = tf.layers.Conv2D(
+    self.conv1 = layers.Conv2D(
         64, 5, padding='SAME', data_format=data_format, activation=tf.tanh)
-    self.pool1 = tf.layers.AveragePooling2D(2, 2, data_format=data_format)
-    self.conv2 = tf.layers.Conv2D(
+    self.pool1 = layers.AveragePooling2D(2, 2, data_format=data_format)
+    self.conv2 = layers.Conv2D(
         128, 5, data_format=data_format, activation=tf.tanh)
-    self.pool2 = tf.layers.AveragePooling2D(2, 2, data_format=data_format)
-    self.flatten = tf.layers.Flatten()
-    self.fc1 = tf.layers.Dense(1024, activation=tf.tanh)
-    self.fc2 = tf.layers.Dense(1, activation=None)
+    self.pool2 = layers.AveragePooling2D(2, 2, data_format=data_format)
+    self.flatten = layers.Flatten()
+    self.fc1 = layers.Dense(1024, activation=tf.tanh)
+    self.fc2 = layers.Dense(1, activation=None)
 
   def call(self, inputs):
     """Return two logits per image estimating input authenticity.
@@ -112,16 +113,16 @@ class Generator(tf.keras.Model):
     else:
       assert data_format == 'channels_last'
       self._pre_conv_shape = [-1, 6, 6, 128]
-    self.fc1 = tf.layers.Dense(6 * 6 * 128, activation=tf.tanh)
+    self.fc1 = layers.Dense(6 * 6 * 128, activation=tf.tanh)
 
     # In call(), we reshape the output of fc1 to _pre_conv_shape
 
     # Deconvolution layer. Resulting image shape: (batch, 14, 14, 64)
-    self.conv1 = tf.layers.Conv2DTranspose(
+    self.conv1 = layers.Conv2DTranspose(
         64, 4, strides=2, activation=None, data_format=data_format)
 
     # Deconvolution layer. Resulting image shape: (batch, 28, 28, 1)
-    self.conv2 = tf.layers.Conv2DTranspose(
+    self.conv2 = layers.Conv2DTranspose(
         1, 2, strides=2, activation=tf.nn.sigmoid, data_format=data_format)
 
   def call(self, inputs):

diff --git a/tensorflow/contrib/eager/python/examples/linear_regression/linear_regression.py b/tensorflow/contrib/eager/python/examples/linear_regression/linear_regression.py
index 6ab847c..4e1380a 100644 (file)
--- a/tensorflow/contrib/eager/python/examples/linear_regression/linear_regression.py
+++ b/tensorflow/contrib/eager/python/examples/linear_regression/linear_regression.py
@@ -32,6 +32,8 @@ import tensorflow as tf
 
 import tensorflow.contrib.eager as tfe
 
+layers = tf.keras.layers
+
 
 class LinearModel(tf.keras.Model):
   """A TensorFlow linear regression model."""
@@ -39,7 +41,7 @@ class LinearModel(tf.keras.Model):
   def __init__(self):
     """Constructs a LinearModel object."""
     super(LinearModel, self).__init__()
-    self._hidden_layer = tf.layers.Dense(1)
+    self._hidden_layer = layers.Dense(1)
 
   def call(self, xs):
     """Invoke the linear model.

diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py
index 6b59413..a28bc8a 100644 (file)
--- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py
+++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py
@@ -28,6 +28,8 @@ import functools
 
 import tensorflow as tf
 
+layers = tf.keras.layers
+
 
 class _IdentityBlock(tf.keras.Model):
   """_IdentityBlock is the block that has no conv layer at shortcut.
@@ -49,23 +51,23 @@ class _IdentityBlock(tf.keras.Model):
     bn_name_base = 'bn' + str(stage) + block + '_branch'
     bn_axis = 1 if data_format == 'channels_first' else 3
 
-    self.conv2a = tf.layers.Conv2D(
+    self.conv2a = layers.Conv2D(
         filters1, (1, 1), name=conv_name_base + '2a', data_format=data_format)
-    self.bn2a = tf.layers.BatchNormalization(
+    self.bn2a = layers.BatchNormalization(
         axis=bn_axis, name=bn_name_base + '2a')
 
-    self.conv2b = tf.layers.Conv2D(
+    self.conv2b = layers.Conv2D(
         filters2,
         kernel_size,
         padding='same',
         data_format=data_format,
         name=conv_name_base + '2b')
-    self.bn2b = tf.layers.BatchNormalization(
+    self.bn2b = layers.BatchNormalization(
         axis=bn_axis, name=bn_name_base + '2b')
 
-    self.conv2c = tf.layers.Conv2D(
+    self.conv2c = layers.Conv2D(
         filters3, (1, 1), name=conv_name_base + '2c', data_format=data_format)
-    self.bn2c = tf.layers.BatchNormalization(
+    self.bn2c = layers.BatchNormalization(
         axis=bn_axis, name=bn_name_base + '2c')
 
   def call(self, input_tensor, training=False):
@@ -113,34 +115,34 @@ class _ConvBlock(tf.keras.Model):
     bn_name_base = 'bn' + str(stage) + block + '_branch'
     bn_axis = 1 if data_format == 'channels_first' else 3
 
-    self.conv2a = tf.layers.Conv2D(
+    self.conv2a = layers.Conv2D(
         filters1, (1, 1),
         strides=strides,
         name=conv_name_base + '2a',
         data_format=data_format)
-    self.bn2a = tf.layers.BatchNormalization(
+    self.bn2a = layers.BatchNormalization(
         axis=bn_axis, name=bn_name_base + '2a')
 
-    self.conv2b = tf.layers.Conv2D(
+    self.conv2b = layers.Conv2D(
         filters2,
         kernel_size,
         padding='same',
         name=conv_name_base + '2b',
         data_format=data_format)
-    self.bn2b = tf.layers.BatchNormalization(
+    self.bn2b = layers.BatchNormalization(
         axis=bn_axis, name=bn_name_base + '2b')
 
-    self.conv2c = tf.layers.Conv2D(
+    self.conv2c = layers.Conv2D(
         filters3, (1, 1), name=conv_name_base + '2c', data_format=data_format)
-    self.bn2c = tf.layers.BatchNormalization(
+    self.bn2c = layers.BatchNormalization(
         axis=bn_axis, name=bn_name_base + '2c')
 
-    self.conv_shortcut = tf.layers.Conv2D(
+    self.conv_shortcut = layers.Conv2D(
         filters3, (1, 1),
         strides=strides,
         name=conv_name_base + '1',
         data_format=data_format)
-    self.bn_shortcut = tf.layers.BatchNormalization(
+    self.bn_shortcut = layers.BatchNormalization(
         axis=bn_axis, name=bn_name_base + '1')
 
   def call(self, input_tensor, training=False):
@@ -219,15 +221,15 @@ class ResNet50(tf.keras.Model):
       return _IdentityBlock(
           3, filters, stage=stage, block=block, data_format=data_format)
 
-    self.conv1 = tf.layers.Conv2D(
+    self.conv1 = layers.Conv2D(
         64, (7, 7),
         strides=(2, 2),
         data_format=data_format,
         padding='same',
         name='conv1')
     bn_axis = 1 if data_format == 'channels_first' else 3
-    self.bn_conv1 = tf.layers.BatchNormalization(axis=bn_axis, name='bn_conv1')
-    self.max_pool = tf.layers.MaxPooling2D(
+    self.bn_conv1 = layers.BatchNormalization(axis=bn_axis, name='bn_conv1')
+    self.max_pool = layers.MaxPooling2D(
         (3, 3), strides=(2, 2), data_format=data_format)
 
     self.l2a = conv_block([64, 64, 256], stage=2, block='a', strides=(1, 1))
@@ -250,11 +252,12 @@ class ResNet50(tf.keras.Model):
     self.l5b = id_block([512, 512, 2048], stage=5, block='b')
     self.l5c = id_block([512, 512, 2048], stage=5, block='c')
 
-    self.avg_pool = tf.layers.AveragePooling2D(
+    self.avg_pool = layers.AveragePooling2D(
         (7, 7), strides=(7, 7), data_format=data_format)
 
     if self.include_top:
-      self.fc1000 = tf.layers.Dense(classes, name='fc1000')
+      self.flatten = layers.Flatten()
+      self.fc1000 = layers.Dense(classes, name='fc1000')
     else:
       reduction_indices = [1, 2] if data_format == 'channels_last' else [2, 3]
       reduction_indices = tf.constant(reduction_indices)
@@ -298,7 +301,7 @@ class ResNet50(tf.keras.Model):
     x = self.avg_pool(x)
 
     if self.include_top:
-      return self.fc1000(tf.layers.flatten(x))
+      return self.fc1000(self.flatten(x))
     elif self.global_pooling:
       return self.global_pooling(x)
     else:

diff --git a/tensorflow/contrib/eager/python/examples/rnn_colorbot/rnn_colorbot.py b/tensorflow/contrib/eager/python/examples/rnn_colorbot/rnn_colorbot.py
index 88fffc9..492adbe 100644 (file)
--- a/tensorflow/contrib/eager/python/examples/rnn_colorbot/rnn_colorbot.py
+++ b/tensorflow/contrib/eager/python/examples/rnn_colorbot/rnn_colorbot.py
@@ -73,6 +73,8 @@ try:
 except ImportError:
   HAS_MATPLOTLIB = False
 
+layers = tf.keras.layers
+
 
 def parse(line):
   """Parse a line from the colors dataset."""
@@ -152,7 +154,7 @@ class RNNColorbot(tf.keras.Model):
 
     self.cells = self._add_cells(
         [tf.nn.rnn_cell.BasicLSTMCell(size) for size in rnn_cell_sizes])
-    self.relu = tf.layers.Dense(
+    self.relu = layers.Dense(
         label_dimension, activation=tf.nn.relu, name="relu")
 
   def call(self, inputs, training=False):
@@ -204,7 +206,7 @@ class RNNColorbot(tf.keras.Model):
 
   def _add_cells(self, cells):
     # "Magic" required for keras.Model classes to track all the variables in
-    # a list of tf.layers.Layer objects.
+    # a list of layers.Layer objects.
     # TODO(ashankar): Figure out API so user code doesn't have to do this.
     for i, c in enumerate(cells):
       setattr(self, "cell-%d" % i, c)

diff --git a/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py b/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py
index 69cd16d..a90048d 100644 (file)
--- a/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py
+++ b/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py
@@ -38,6 +38,8 @@ import tensorflow as tf
 from tensorflow.contrib.cudnn_rnn.python.layers import cudnn_rnn
 from tensorflow.contrib.eager.python import tfe
 
+layers = tf.keras.layers
+
 
 class RNN(tf.keras.Model):
   """A static RNN.
@@ -74,14 +76,14 @@ class RNN(tf.keras.Model):
 
   def _add_cells(self, cells):
     # "Magic" required for keras.Model classes to track all the variables in
-    # a list of tf.layers.Layer objects.
+    # a list of Layer objects.
     # TODO(ashankar): Figure out API so user code doesn't have to do this.
     for i, c in enumerate(cells):
       setattr(self, "cell-%d" % i, c)
     return cells
 
 
-class Embedding(tf.layers.Layer):
+class Embedding(layers.Layer):
   """An Embedding layer."""
 
   def __init__(self, vocab_size, embedding_dim, **kwargs):
@@ -132,7 +134,7 @@ class PTBModel(tf.keras.Model):
     else:
       self.rnn = RNN(hidden_dim, num_layers, self.keep_ratio)
 
-    self.linear = tf.layers.Dense(
+    self.linear = layers.Dense(
         vocab_size, kernel_initializer=tf.random_uniform_initializer(-0.1, 0.1))
     self._output_shape = [-1, embedding_dim]