d3bdc5449964423387861c78edd9d890a81a5466,tests/layers/test_layers_recurrent.py,Layer_RNN_Test,setUpClass,#Any#,18

Before Change

        cls.keras_rnn_model = tf.keras.Model(inputs=cls.keras_net_in, outputs=cls.keras_net_rnn)
        cls.keras_rnn_model.compile(optimizer="adam",
              loss="categorical_crossentropy",
              metrics=["accuracy"])


        """

After Change

        cls.data_y = np.zeros([cls.batch_size, 1]).astype(np.float32)

        map1 = np.random.random([1, cls.num_steps])
        map2 = np.random.random([cls.embedding_size, 1])
        for i in range(cls.batch_size):
            cls.data_y[i] = np.matmul(map1, np.matmul(cls.data_x[i], map2))

    @classmethod

Instances