ce4a5ca7cab1ea4ae421fbcd3b28205b1e18158d,deepchem/models/tests/test_overfit.py,TestOverfitAPI,test_tf_multitask_classification_overfit,#TestOverfitAPI#,637

Before Change

  def test_tf_multitask_classification_overfit(self):
    Test tf multitask overfits tiny data.
    n_tasks = 10
    tasks = ["task%d" % task for task in range(n_tasks)]
    task_types = {task: "classification" for task in tasks}
    n_samples = 10
    n_features = 3
    n_classes = 2
    
    // Generate dummy dataset
    np.random.seed(123)
    ids = np.arange(n_samples)
    X = np.random.rand(n_samples, n_features)
    //y = np.random.randint(n_classes, size=(n_samples, n_tasks))
    y = np.zeros((n_samples, n_tasks))
    w = np.ones((n_samples, n_tasks))
  
    dataset = Dataset.from_numpy(self.train_dir, X, y, w, ids, tasks)

    model_params = {
      "layer_sizes": [1000],
      "dropouts": [.0],
      "learning_rate": 0.0003,
      "momentum": .9,
      "batch_size": n_samples,
      "num_classification_tasks": n_tasks,
      "num_classes": n_classes,
      "num_features": n_features,
      "weight_init_stddevs": [.1],
      "bias_init_consts": [1.],
      "nb_epoch": 100,
      "penalty": 0.0,
      "optimizer": "adam",
      "data_shape": dataset.get_data_shape()
    }

    verbosity = "high"
    classification_metric = Metric(metrics.accuracy_score, verbosity=verbosity)
    model = TensorflowModel(
        tasks, task_types, model_params, self.model_dir,
        tf_class=TensorflowMultiTaskClassifier,
        verbosity=verbosity)

    // Fit trained model
    model.fit(dataset)
    model.save()

After Change

    verbosity = "high"
    classification_metric = Metric(metrics.accuracy_score, verbosity=verbosity)
    tensorflow_model = TensorflowMultiTaskClassifier(
        n_tasks, n_features, self.model_dir, dropouts=[0.],
        learning_rate=0.0003, weight_init_stddevs=[.1],
        batch_size=n_samples, verbosity=verbosity)
    model = TensorflowModel(tensorflow_model, self.model_dir)

    // Fit trained model
    model.fit(dataset)
    model.save()

Instances