def test_reshape_inputs(self):
"""Test that layers can automatically reshape inconsistent inputs."""
value1 = np.random.uniform(size=(2, 3)).astype(np.float32)
value2 = np.random.uniform(size=(1, 6, 1)).astype(np.float32)
with self.test_session() as sess:
out_tensor = Add()(tf.constant(value1), tf.constant(value2))
result = out_tensor.eval()
assert result.shape == (1, 6, 1)
assert np.array_equal(value1.reshape((1, 6, 1)) + value2, result)
def test_add(self):
"""Test that Add can be invoked."""
value1 = np.random.uniform(size=(2, 3)).astype(np.float32)
value2 = np.random.uniform(size=(2, 3)).astype(np.float32)
value3 = np.random.uniform(size=(2, 3)).astype(np.float32)
with self.test_session() as sess:
out_tensor = Add(weights=[1, 2, 1])(
tf.constant(value1), tf.constant(value2), tf.constant(value3))
assert np.array_equal(value1 + 2 * value2 + value3, out_tensor.eval())
def test_add(self):
"""Test that Add can be invoked."""
value1 = np.random.uniform(size=(2, 3)).astype(np.float32)
value2 = np.random.uniform(size=(2, 3)).astype(np.float32)
value3 = np.random.uniform(size=(2, 3)).astype(np.float32)
with self.session() as sess:
out_tensor = Add(weights=[1, 2, 1])(tf.constant(value1),
tf.constant(value2),
tf.constant(value3))
assert np.array_equal(value1 + 2 * value2 + value3, out_tensor.eval())