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())