cb4ea667b426ebc6595cc0e8be81c94955266c9c,deepchem/utils/test/test_generator_evaluator.py,TestGeneratorEvaluator,test_compute_model_performance_singletask_classifier,#TestGeneratorEvaluator#,57

Before Change

    y = np.concatenate((class_0, class_1))
    dataset = NumpyDataset(X, y)

    features = Feature(shape=(None, n_features))
    label = Label(shape=(None, 2))
    dense = Dense(out_channels=2, in_layers=[features])
    output = SoftMax(in_layers=[dense])
    smce = SoftMaxCrossEntropy(in_layers=[label, dense])
    total_loss = ReduceMean(in_layers=smce)

    tg = dc.models.TensorGraph(learning_rate=0.1)
    tg.add_output(output)
    tg.set_loss(total_loss)

    tg.fit(dataset, nb_epoch=1000)
    metric = dc.metrics.Metric(
        dc.metrics.roc_auc_score, np.mean, mode="classification")

After Change

    y = np.concatenate((class_0, class_1))
    dataset = NumpyDataset(X, y)

    features = layers.Input(shape=(n_features,))
    dense = layers.Dense(2)(features)
    output = layers.Softmax()(dense)
    keras_model = tf.keras.Model(inputs=features, outputs=[output])
    model = dc.models.KerasModel(
        keras_model, dc.models.losses.SoftmaxCrossEntropy(), learning_rate=0.1)

Instances