cb4ea667b426ebc6595cc0e8be81c94955266c9c,deepchem/utils/test/test_generator_evaluator.py,TestGeneratorEvaluator,test_compute_model_performance_multitask_regressor,#TestGeneratorEvaluator#,91

Before Change

    y = np.stack([y1, y2], axis=1)
    dataset = NumpyDataset(X, y)

    features = Feature(shape=(None, n_features))
    label = Label(shape=(None, n_tasks))
    dense = Dense(out_channels=n_tasks, in_layers=[features])
    loss = ReduceSquareDifference(in_layers=[dense, label])

    tg = dc.models.TensorGraph(random_seed=random_seed, learning_rate=0.1)
    tg.add_output(dense)
    tg.set_loss(loss)

    tg.fit(dataset, nb_epoch=1000)
    metric = [
        dc.metrics.Metric(

After Change

    y = np.stack([y1, y2], axis=1)
    dataset = NumpyDataset(X, y)

    features = layers.Input(shape=(n_features,))
    dense = layers.Dense(n_tasks)(features)
    keras_model = tf.keras.Model(inputs=features, outputs=[dense])
    model = dc.models.KerasModel(
        keras_model, dc.models.losses.L2Loss(), learning_rate=0.1)

    model.fit(dataset, nb_epoch=1000)

Instances