ce4a5ca7cab1ea4ae421fbcd3b28205b1e18158d,deepchem/models/tests/test_overfit.py,TestOverfitAPI,test_keras_classification_overfit,#TestOverfitAPI#,259

Before Change

    sess = tf.Session(graph=g)
    K.set_session(sess)
    with g.as_default():
      tasks = ["task0"]
      task_types = {task: "classification" 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.randint(2, size=(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"
      classification_metric = Metric(metrics.roc_auc_score, verbosity=verbosity)
      model = MultiTaskDNN(tasks, task_types, model_params, self.model_dir,
                           verbosity=verbosity)

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

After Change

      verbosity = "high"
      classification_metric = Metric(metrics.roc_auc_score, verbosity=verbosity)
      keras_model  = MultiTaskDNN(n_tasks, n_features, "classification",
                                  learning_rate=.15, decay=1e-4, dropout=0.)
      model = KerasModel(keras_model, self.model_dir)
      

      // Fit trained model
      model.fit(dataset, nb_epoch=200)

Instances