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.session() as sess: out_tensor = ReduceSquareDifference()(tf.constant(value1), tf.constant(value2)) result = out_tensor.eval() diff = value1.reshape((1, 6, 1)) - value2 loss = np.mean(diff**2) assert (loss - result) / loss < 1e-6
def test_reduce_square_difference(self): """Test that ReduceSquareDifference can be invoked.""" batch_size = 10 n_features = 5 in_tensor_1 = np.random.rand(batch_size, n_features) in_tensor_2 = np.random.rand(batch_size, n_features) with self.session() as sess: in_tensor_1 = tf.convert_to_tensor(in_tensor_1, dtype=tf.float32) in_tensor_2 = tf.convert_to_tensor(in_tensor_2, dtype=tf.float32) out_tensor = ReduceSquareDifference()(in_tensor_1, in_tensor_2) out_tensor = out_tensor.eval() assert isinstance(out_tensor, np.float32)