785ac0b6d0383e6ab46b9ad0f8ebb5cb1eddd3e2,deepchem/models/tests/test_overfit.py,TestOverfitAPI,test_keras_regression_overfit,#TestOverfitAPI#,153

Before Change

    n_tasks = len(tasks)
    
    // Generate dummy dataset
    np.random.seed(123)
    ids = np.arange(n_samples)
    X = np.random.rand(n_samples, n_features)
    y = np.random.rand(n_samples, n_tasks)
    w = np.ones((n_samples, n_tasks))

After Change

  def test_keras_regression_overfit(self):
    Test that keras models can overfit simple regression datasets.
    ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// DEBUG
    g = tf.Graph()
    sess = tf.Session(graph=g)
    K.set_session(sess)
    with g.as_default():
    ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// DEBUG
      tasks = ["task0"]
      task_types = {task: "regression" for task in tasks}
      n_samples = 10
      n_features = 3
      n_tasks = len(tasks)
      
      // Generate dummy dataset
      np.random.seed(123)
      ids = np.arange(n_samples)
      X = np.random.rand(n_samples, n_features)
      y = np.random.rand(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 = {
          "nb_hidden": 1000,
          "activation": "relu",
          "dropout": .0,
          "learning_rate": .15,
          "momentum": .9,
          "nesterov": False,
          "decay": 1e-4,
          "batch_size": n_samples,
          "nb_epoch": 200,
          "init": "glorot_uniform",
          "nb_layers": 1,
          "batchnorm": False,
          "data_shape": dataset.get_data_shape()
      }

      verbosity = "high"
      regression_metric = Metric(metrics.r2_score, verbosity=verbosity)
      model = MultiTaskDNN(tasks, task_types, model_params, self.model_dir,
                           verbosity=verbosity)

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

      // Eval model on train
      transformers = []
      evaluator = Evaluator(model, dataset, transformers, verbosity=verbosity)
      scores = evaluator.compute_model_performance([regression_metric])

      assert scores[regression_metric.name] > .7

  def test_tf_regression_overfit(self):
    Test that TensorFlow models can overfit simple regression datasets.
    tasks = ["task0"]
    task_types = {task: "regression" for task in tasks}

Instances