ef3c40a4542966e2dac08cc51710ddedc31fd2d9,deepchem/models/tensorgraph/progressive_multitask.py,ProgressiveMultitaskRegressor,add_adapter,#ProgressiveMultitaskRegressor#Any#Any#Any#,215

Before Change

    // prev_layers is a list with elements of size
    // (batch_size, layer_sizes[i-1])
    prev_layer = Concat(axis=1, in_layers=prev_layers)
    with self._get_tf("Graph").as_default():
      alpha = TensorWrapper(
          tf.Variable(
              tf.truncated_normal((1,), stddev=alpha_init_stddev),
              name="alpha_layer_%d_task%d" % (i, task)))
      trainable_layers.append(alpha)

    prev_layer = prev_layer * alpha
    dense1 = Dense(
        in_layers=[prev_layer],
        out_channels=layer_sizes[i - 1],

After Change

      prev_layers.append(all_layers[(i - 1, prev_task)])
    // prev_layers is a list with elements of size
    // (batch_size, layer_sizes[i-1])
    if len(prev_layers) == 1:
      prev_layer = prev_layers[0]
    else:
      prev_layer = Concatenate(axis=1)(prev_layers)

Instances