Formatting

  def __call__(self, *parents):

    self.out_tensor = tf.placeholder(tf.float32, shape=self.t_shape)

class LossLayer(Layer):

  def __init__(self, **kwargs):

class Conv2d(Layer):

  def __init__(self,

               num_outputs,

               kernel_size=5,

               **kwargs):

  def __init__(self, num_outputs, kernel_size=5, **kwargs):

    self.num_outputs = num_outputs

    self.kernel_size = kernel_size

    super().__init__(**kwargs)

class MaxPool(Layer):

  def __init__(self,

               ksize=[1, 2, 2, 1],

               strides=[1, 2, 2, 1],

class TensorGraph(Model):

  def __init__(self,

               tensorboard=False,

               tensorboard_log_frequency=100,

        y_pred = np.reshape(y_pred, (n_samples, n_tasks))

        return y_pred

  def predict_proba(self,

                    dataset,

                    transformers=[],

                    batch_size=None):

  def predict_proba(self, dataset, transformers=[], batch_size=None):

    """

    TODO: Do transformers even make sense here?

    g.add_layer(loss, parents=[dense, label_out])

    g.set_loss(loss)

    g.fit(dataset, nb_epoch=100)

    g.save()

    g1 = TensorGraph.load_from_dir('/tmp/tmpss5_ki5_')