9c51193d4203f796c2adeca69b29a4ea168d7ceb,deepchem/models/tensorgraph/models/gan.py,GAN,predict_gan_generator,#GAN#Any#Any#Any#Any#,440

Before Change

      batch_size = len(conditional_inputs[0])
    if noise_input is None:
      noise_input = self.get_noise_batch(batch_size)
    batch = {}
    batch[self.noise_input] = noise_input
    for layer, value in zip(self.conditional_inputs, conditional_inputs):
      batch[layer] = value
    return self.predict_on_generator(
        [batch], outputs=self.generators[generator_index])

  def _set_empty_inputs(self, feed_dict, layers):

After Change

      batch_size = len(conditional_inputs[0])
    if noise_input is None:
      noise_input = self.get_noise_batch(batch_size)
    inputs = [noise_input]
    inputs += conditional_inputs
    inputs = [i.astype(np.float32) for i in inputs]
    pred = self.generators[generator_index](inputs, training=False)
    if tf.executing_eagerly():
      pred = pred.numpy()
    else:
      pred = pred.eval(session=self.session)
    return pred


class WGAN(GAN):

Instances