ad1908266bd4478886c1d3e667d1fd1cc48f2b69,deepchem/models/tensorgraph/models/sequence_dnn.py,SequenceDNN,__init__,#SequenceDNN#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,42

Before Change

    super(SequenceDNN, self).__init__(loss, **kwargs)
    self.num_tasks = num_tasks
    self.verbose = verbose
    self.add(layers.Conv2D(num_filters, kernel_size=kernel_size))
    self.add(layers.Dropout(dropout))
    self.add(layers.Flatten())
    self.add(layers.Dense(self.num_tasks, activation_fn=tf.nn.relu))

After Change

    if not isinstance(num_filters, collections.Sequence) or not isinstance(
        kernel_size, collections.Sequence):
      raise ValueError("num_filters and kernel_size must be lists")
    if not len(num_filters) == len(kernel_size):
      raise ValueError("num_filters and kernel_size must be of same length")
    n_layers = len(num_filters)
    self.num_tasks = num_tasks
    self.verbose = verbose
    for layer in range(n_layers):
      self.add(
          layers.Conv2D(num_filters[layer], kernel_size=kernel_size[layer]))
      self.add(layers.Dropout(dropout))
    self.add(layers.Flatten())
    self.add(layers.Dense(self.num_tasks, activation_fn=tf.nn.relu))

Instances