def testCellOutput(self):
x = tf.ones([1, 2], dtype=tf.float32)
c0 = tf.constant(0.1 * np.asarray([[0, 1]]), dtype=tf.float32)
h0 = tf.constant(0.1 * np.asarray([[2, 3]]), dtype=tf.float32)
state0 = [h0, c0]
c1 = tf.constant(0.1 * np.asarray([[4, 5]]), dtype=tf.float32)
h1 = tf.constant(0.1 * np.asarray([[6, 7]]), dtype=tf.float32)
state1 = [h1, c1]
state = (state0, state1)
const_initializer = tf.constant_initializer(0.5)
single_cell = lambda: rnn_cell.LayerNormLSTMCell(
units=2,
kernel_initializer=const_initializer,
recurrent_initializer=const_initializer,
bias_initializer=const_initializer,
norm_epsilon=1e-12)
cell = keras.layers.StackedRNNCells([single_cell() for _ in range(2)])
output, output_states = cell(x, state)
self.evaluate([tf.compat.v1.global_variables_initializer()])
output_v, output_states_v = self.evaluate([output, output_states])
expected_output = np.array([[-0.47406167, 0.47406143]])
expected_state0_c = np.array([[-1., 1.]])
expected_state0_h = np.array([[-0.47406167, 0.47406143]])
expected_state1_c = np.array([[-1., 1.]])
expected_state1_h = np.array([[-0.47406167, 0.47406143]])
actual_state0_h = output_states_v[0][0]
actual_state0_c = output_states_v[0][1]
actual_state1_h = output_states_v[1][0]
actual_state1_c = output_states_v[1][1]
self.assertAllClose(output_v, expected_output, 1e-5)
self.assertAllClose(expected_state0_c, actual_state0_c, 1e-5)
self.assertAllClose(expected_state0_h, actual_state0_h, 1e-5)
self.assertAllClose(expected_state1_c, actual_state1_c, 1e-5)
self.assertAllClose(expected_state1_h, actual_state1_h, 1e-5)
# Test BasicLSTMCell with input_size != num_units.
x = tf.ones([1, 3], dtype=tf.float32)
c = tf.constant(0.1 * np.asarray([[0, 1]]), dtype=tf.float32)
h = tf.constant(0.1 * np.asarray([[2, 3]]), dtype=tf.float32)
state = [h, c]
cell = rnn_cell.LayerNormLSTMCell(
units=2,
kernel_initializer=const_initializer,
recurrent_initializer=const_initializer,
bias_initializer=const_initializer,
norm_epsilon=1e-12)
output, output_states = cell(x, state)
self.evaluate([tf.compat.v1.global_variables_initializer()])
output_v, output_states_v = self.evaluate([output, output_states])
expected_h = np.array([[-0.47406167, 0.47406143]])
expected_c = np.array([[-1.0, 1.0]])
self.assertAllClose(output_v, expected_h, 1e-5)
self.assertAllClose(output_states_v[0], expected_h, 1e-5)
self.assertAllClose(output_states_v[1], expected_c, 1e-5)
def single_cell():
return rnn_cell.LayerNormLSTMCell(
units=2,
kernel_initializer=const_initializer,
recurrent_initializer=const_initializer,
bias_initializer=const_initializer,
norm_epsilon=1e-12)
def test_cell_output():
x = tf.ones([1, 2], dtype=tf.float32)
c0 = tf.constant(0.1 * np.asarray([[0, 1]]), dtype=tf.float32)
h0 = tf.constant(0.1 * np.asarray([[2, 3]]), dtype=tf.float32)
state0 = [h0, c0]
c1 = tf.constant(0.1 * np.asarray([[4, 5]]), dtype=tf.float32)
h1 = tf.constant(0.1 * np.asarray([[6, 7]]), dtype=tf.float32)
state1 = [h1, c1]
state = (state0, state1)
const_initializer = tf.constant_initializer(0.5)
def single_cell():
return rnn_cell.LayerNormLSTMCell(
units=2,
kernel_initializer=const_initializer,
recurrent_initializer=const_initializer,
bias_initializer=const_initializer,
norm_epsilon=1e-12,
)
cell = keras.layers.StackedRNNCells([single_cell() for _ in range(2)])
output_v, output_states_v = cell(x, state)
expected_output = np.array([[-0.47406167, 0.47406143]])
expected_state0_c = np.array([[-1.0, 1.0]])
expected_state0_h = np.array([[-0.47406167, 0.47406143]])
expected_state1_c = np.array([[-1.0, 1.0]])
expected_state1_h = np.array([[-0.47406167, 0.47406143]])
actual_state0_h = output_states_v[0][0]
actual_state0_c = output_states_v[0][1]
actual_state1_h = output_states_v[1][0]
actual_state1_c = output_states_v[1][1]
np.testing.assert_allclose(output_v, expected_output, 1e-5)
np.testing.assert_allclose(expected_state0_c, actual_state0_c, 1e-5)
np.testing.assert_allclose(expected_state0_h, actual_state0_h, 1e-5)
np.testing.assert_allclose(expected_state1_c, actual_state1_c, 1e-5)
np.testing.assert_allclose(expected_state1_h, actual_state1_h, 1e-5)
# Test BasicLSTMCell with input_size != num_units.
x = tf.ones([1, 3], dtype=tf.float32)
c = tf.constant(0.1 * np.asarray([[0, 1]]), dtype=tf.float32)
h = tf.constant(0.1 * np.asarray([[2, 3]]), dtype=tf.float32)
state = [h, c]
cell = rnn_cell.LayerNormLSTMCell(
units=2,
kernel_initializer=const_initializer,
recurrent_initializer=const_initializer,
bias_initializer=const_initializer,
norm_epsilon=1e-12,
)
output_v, output_states_v = cell(x, state)
expected_h = np.array([[-0.47406167, 0.47406143]])
expected_c = np.array([[-1.0, 1.0]])
np.testing.assert_allclose(output_v, expected_h, 1e-5)
np.testing.assert_allclose(output_states_v[0], expected_h, 1e-5)
np.testing.assert_allclose(output_states_v[1], expected_c, 1e-5)
def test_config(self):
cell = rnn_cell.LayerNormLSTMCell(10)
expected_config = {
"dtype": "float32",
"name": "layer_norm_lstm_cell",
"trainable": True,
"units": 10,
"activation": "tanh",
"recurrent_activation": "sigmoid",
"use_bias": True,
"kernel_initializer": {
"class_name": "GlorotUniform",
"config": {
"seed": None
}
},
"recurrent_initializer": {
"class_name": "Orthogonal",
"config": {
"seed": None,
"gain": 1.0
}
},
"bias_initializer": {
"class_name": "Zeros",
"config": {}
},
"unit_forget_bias": True,
"kernel_regularizer": None,
"recurrent_regularizer": None,
"bias_regularizer": None,
"kernel_constraint": None,
"recurrent_constraint": None,
"bias_constraint": None,
"dropout": 0.,
"recurrent_dropout": 0.,
"implementation": 2,
"norm_gamma_initializer": {
"class_name": "Ones",
"config": {}
},
"norm_beta_initializer": {
"class_name": "Zeros",
"config": {}
},
"norm_epsilon": 1e-3,
}
config = cell.get_config()
self.assertEqual(config, expected_config)
restored_cell = rnn_cell.LayerNormLSTMCell.from_config(config)
restored_config = restored_cell.get_config()
self.assertEqual(config, restored_config)
