Python nets.inception.inception_resnet_v2_arg_scope() Examples

The following are 8 code examples of nets.inception.inception_resnet_v2_arg_scope(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module nets.inception , or try the search function .
Example #1
Source File: inception_resnet_v2_test.py    From MAX-Image-Segmenter with Apache License 2.0 5 votes vote down vote up 
def testNoBatchNormScaleByDefault(self):
    height, width = 299, 299
    num_classes = 1000
    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
    with tf.contrib.slim.arg_scope(inception.inception_resnet_v2_arg_scope()):
      inception.inception_resnet_v2(inputs, num_classes, is_training=False)

    self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), [])
Example #2
Source File: inception_resnet_v2_test.py    From MAX-Image-Segmenter with Apache License 2.0 5 votes vote down vote up 
def testBatchNormScale(self):
    height, width = 299, 299
    num_classes = 1000
    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
    with tf.contrib.slim.arg_scope(
        inception.inception_resnet_v2_arg_scope(batch_norm_scale=True)):
      inception.inception_resnet_v2(inputs, num_classes, is_training=False)

    gamma_names = set(
        v.op.name for v in tf.global_variables('.*/BatchNorm/gamma:0$'))
    self.assertGreater(len(gamma_names), 0)
    for v in tf.global_variables('.*/BatchNorm/moving_mean:0$'):
      self.assertIn(v.op.name[:-len('moving_mean')] + 'gamma', gamma_names)
Example #3
Source File: inception_resnet_v2_test.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
def testNoBatchNormScaleByDefault(self):
    height, width = 299, 299
    num_classes = 1000
    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
    with tf.contrib.slim.arg_scope(inception.inception_resnet_v2_arg_scope()):
      inception.inception_resnet_v2(inputs, num_classes, is_training=False)

    self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), [])
Example #4
Source File: inception_resnet_v2_test.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
def testBatchNormScale(self):
    height, width = 299, 299
    num_classes = 1000
    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
    with tf.contrib.slim.arg_scope(
        inception.inception_resnet_v2_arg_scope(batch_norm_scale=True)):
      inception.inception_resnet_v2(inputs, num_classes, is_training=False)

    gamma_names = set(
        v.op.name for v in tf.global_variables('.*/BatchNorm/gamma:0$'))
    self.assertGreater(len(gamma_names), 0)
    for v in tf.global_variables('.*/BatchNorm/moving_mean:0$'):
      self.assertIn(v.op.name[:-len('moving_mean')] + 'gamma', gamma_names)
Example #5
Source File: inception_resnet_v2_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def testNoBatchNormScaleByDefault(self):
    height, width = 299, 299
    num_classes = 1000
    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
    with tf.contrib.slim.arg_scope(inception.inception_resnet_v2_arg_scope()):
      inception.inception_resnet_v2(inputs, num_classes, is_training=False)

    self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), [])
Example #6
Source File: inception_resnet_v2_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def testBatchNormScale(self):
    height, width = 299, 299
    num_classes = 1000
    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
    with tf.contrib.slim.arg_scope(
        inception.inception_resnet_v2_arg_scope(batch_norm_scale=True)):
      inception.inception_resnet_v2(inputs, num_classes, is_training=False)

    gamma_names = set(
        v.op.name for v in tf.global_variables('.*/BatchNorm/gamma:0$'))
    self.assertGreater(len(gamma_names), 0)
    for v in tf.global_variables('.*/BatchNorm/moving_mean:0$'):
      self.assertIn(v.op.name[:-len('moving_mean')] + 'gamma', gamma_names)
Example #7
Source File: inception_resnet_v2_test.py    From models with Apache License 2.0 5 votes vote down vote up 
def testNoBatchNormScaleByDefault(self):
    height, width = 299, 299
    num_classes = 1000
    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
    with slim.arg_scope(inception.inception_resnet_v2_arg_scope()):
      inception.inception_resnet_v2(inputs, num_classes, is_training=False)

    self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), [])
Example #8
Source File: inception_resnet_v2_test.py    From models with Apache License 2.0 5 votes vote down vote up 
def testBatchNormScale(self):
    height, width = 299, 299
    num_classes = 1000
    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
    with slim.arg_scope(
        inception.inception_resnet_v2_arg_scope(batch_norm_scale=True)):
      inception.inception_resnet_v2(inputs, num_classes, is_training=False)

    gamma_names = set(
        v.op.name
        for v in tf.global_variables('.*/BatchNorm/gamma:0$'))
    self.assertGreater(len(gamma_names), 0)
    for v in tf.global_variables('.*/BatchNorm/moving_mean:0$'):
      self.assertIn(v.op.name[:-len('moving_mean')] + 'gamma', gamma_names)