def test_weightnorm_tflayers(self):
images = random_ops.random_uniform((2, 4, 4, 3))
wn_wrapper = wrappers.WeightNormalization(keras.layers.Conv2D(
32, [2, 2]),
input_shape=(4, 4, 3))
wn_wrapper.apply(images)
self.assertTrue(hasattr(wn_wrapper.layer, 'g'))
def test_weightnorm_dense_train(self):
model = keras.models.Sequential()
model.add(
wrappers.WeightNormalization(keras.layers.Dense(2),
input_shape=(3, 4)))
model.compile(optimizer=RMSprop(learning_rate=0.001), loss='mse')
model.fit(np.random.random((10, 3, 4)),
np.random.random((10, 3, 2)),
epochs=3,
batch_size=10)
self.assertTrue(hasattr(model.layers[0].layer, 'g'))
def test_weightnorm_conv2d(self):
model = keras.models.Sequential()
model.add(wrappers.WeightNormalization(
keras.layers.Conv2D(5, (2, 2), padding='same'),
input_shape=(4, 4, 3)))
model.add(keras.layers.Activation('relu'))
model.compile(optimizer=RMSPropOptimizer(0.01), loss='mse')
model.train_on_batch(
np.random.random((2, 4, 4, 3)),
np.random.random((2, 4, 4, 5)))
self.assertTrue(hasattr(model.layers[0].layer, 'g'))
def test_weightnorm_conv2d(self):
model = keras.models.Sequential()
model.add(
wrappers.WeightNormalization(keras.layers.Conv2D(5, (2, 2),
padding='same'),
input_shape=(4, 4, 3)))
model.add(keras.layers.Activation('relu'))
model.compile(optimizer=RMSprop(learning_rate=0.001), loss='mse')
model.fit(np.random.random((2, 4, 4, 3)),
np.random.random((2, 4, 4, 5)),
epochs=3,
batch_size=10)
self.assertTrue(hasattr(model.layers[0].layer, 'g'))
def test_weightnorm_nokernel(self):
with self.assertRaises(ValueError):
wrappers.WeightNormalization(tf.keras.layers.MaxPooling2D(
2, 2)).build((2, 2))
def test_weightnorm_nonlayer(self):
images = tf.random.uniform((2, 4, 43))
with self.assertRaises(ValueError):
wrappers.WeightNormalization(images)
