def testKerasStyleAddWeight(self): keras_layer = keras_base_layer.Layer(name='keras_layer') with ops.name_scope('foo', skip_on_eager=False): keras_variable = keras_layer.add_variable( 'my_var', [2, 2], initializer=init_ops.zeros_initializer()) self.assertEqual(keras_variable.name, 'foo/my_var:0') with ops.name_scope('baz', skip_on_eager=False): old_style_layer = base_layers.Layer(name='my_layer') # Test basic variable creation. variable = old_style_layer.add_variable( 'my_var', [2, 2], initializer=init_ops.zeros_initializer()) self.assertEqual(variable.name, 'my_layer/my_var:0') with base_layers.keras_style_scope(): layer = base_layers.Layer(name='my_layer') # Test basic variable creation. with ops.name_scope('bar', skip_on_eager=False): variable = layer.add_variable( 'my_var', [2, 2], initializer=init_ops.zeros_initializer()) self.assertEqual(variable.name, 'bar/my_var:0')
def testKerasStyleAddWeight(self): keras_layer = keras_base_layer.Layer(name='keras_layer') with backend.name_scope('foo'): keras_variable = keras_layer.add_variable( 'my_var', [2, 2], initializer=init_ops.zeros_initializer()) self.assertEqual(keras_variable.name, 'foo/my_var:0') with backend.name_scope('baz'): old_style_layer = base_layers.Layer(name='my_layer') # Test basic variable creation. variable = old_style_layer.add_variable( 'my_var', [2, 2], initializer=init_ops.zeros_initializer()) self.assertEqual(variable.name, 'my_layer/my_var:0') with base_layers.keras_style_scope(): layer = base_layers.Layer(name='my_layer') # Assert that the layer was not instrumented as a Keras layer self.assertFalse(layer._instrumented_keras_api) # Test basic variable creation. with backend.name_scope('bar'): variable = layer.add_variable( 'my_var', [2, 2], initializer=init_ops.zeros_initializer()) self.assertEqual(variable.name, 'bar/my_var:0')
def test_delete_variable(self): layer = base_layer.Layer(dtype=policy.Policy('mixed_float16')) layer.x = layer.add_weight('x') self.assertEqual(layer.trainable_weights, [layer.x]) del layer.x self.assertEqual(layer.trainable_weights, [])