Fix Tests

      char_dict,

      seq_length,

      n_embedding=75,

      filter_sizes=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20],

      kernel_sizes=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20],

      num_filters=[100, 200, 200, 200, 200, 100, 100, 100, 100, 100, 160, 160],

      dropout=0.25,

      mode="classification",

    self.char_dict = char_dict

    self.seq_length = seq_length

    self.n_embedding = n_embedding

    self.filter_sizes = filter_sizes

    self.kernel_sizes = kernel_sizes

    self.num_filters = num_filters

    self.dropout = dropout

    self.mode = mode

        in_layers=[self.smiles_seqs])

    self.pooled_outputs = []

    self.conv_layers = []

    for filter_size, num_filter in zip(self.filter_sizes, self.num_filters):

    for filter_size, num_filter in zip(self.kernel_sizes, self.num_filters):

      # Multiple convolutional layers with different filter widths

      self.conv_layers.append(

          Conv1D(

              filter_size,

              num_filter,

              padding='VALID',

              kernel_size=filter_size,

              filters=num_filter,

              padding='valid',

              in_layers=[self.Embedding]))

      # Max-over-time pooling

      self.pooled_outputs.append(