old_contents: """
  Script that trains Tensorflow models on PDBbind dataset.
  """
  from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  
  __author__ = "Bharath Ramsundar"
  __copyright__ = "Copyright 2016, Stanford University"
  __license__ = "MIT"
  
  import os
  import numpy as np
  import tensorflow as tf
  # For stable runs
  np.random.seed(123)
  tf.set_random_seed(123)
  
  import deepchem as dc
  from deepchem.molnet import load_pdbbind_grid
  
  split = "random"
  subset = "full"
  pdbbind_tasks, pdbbind_datasets, transformers = load_pdbbind_grid(
      split=split, subset=subset)
  train_dataset, valid_dataset, test_dataset = pdbbind_datasets
  
  metric = dc.metrics.Metric(dc.metrics.pearson_r2_score)
  
  current_dir = os.path.dirname(os.path.realpath(__file__))
  model_dir = os.path.join(current_dir, "%s_%s_DNN" % (split, subset))
  
  n_features = train_dataset.X.shape[1]
  model = dc.models.MultitaskRegressor(
      len(pdbbind_tasks),
      n_features,
      logdir=model_dir,
      dropouts=[.25],
      learning_rate=0.0003,
      weight_init_stddevs=[.1],
      batch_size=64)
  
  # Fit trained model
  model.fit(train_dataset, nb_epoch=100)
  model.save()
  
  print("Evaluating model")
  train_scores = model.evaluate(train_dataset, [metric], transformers)
  valid_scores = model.evaluate(valid_dataset, [metric], transformers)
  
  print("Train scores")
  print(train_scores)
  
  print("Validation scores")
  print(valid_scores)
  
new_contents: """
  Script that trains Tensorflow models on PDBbind dataset.
  """
  from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  
  __author__ = "Bharath Ramsundar"
  __copyright__ = "Copyright 2016, Stanford University"
  __license__ = "MIT"
  
  import os
  import numpy as np
  import tensorflow as tf
  # For stable runs
  np.random.seed(123)
  tf.set_random_seed(123)
  
  import deepchem as dc
  from deepchem.molnet import load_pdbbind_grid
  
  split = "random"
  subset = "full"
  pdbbind_tasks, pdbbind_datasets, transformers = load_pdbbind_grid(
      split=split, subset=subset)
  train_dataset, valid_dataset, test_dataset = pdbbind_datasets
  
  metric = dc.metrics.Metric(dc.metrics.pearson_r2_score)
  
  current_dir = os.path.dirname(os.path.realpath(__file__))
  model_dir = os.path.join(current_dir, "%s_%s_DNN" % (split, subset))
  
  n_features = train_dataset.X.shape[1]
  model = dc.models.MultitaskRegressor(
      len(pdbbind_tasks),
      n_features,
      model_dir=model_dir,
      dropouts=[.25],
      learning_rate=0.0003,
      weight_init_stddevs=[.1],
      batch_size=64)
  
  # Fit trained model
  model.fit(train_dataset, nb_epoch=100)
  model.save()
  
  print("Evaluating model")
  train_scores = model.evaluate(train_dataset, [metric], transformers)
  valid_scores = model.evaluate(valid_dataset, [metric], transformers)
  
  print("Train scores")
  print(train_scores)
  
  print("Validation scores")
  print(valid_scores)
  
subject: change a parameter name