text: # coding=utf-8
  # Copyright 2018 The Tensor2Tensor Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  """Param sets for EPVA model."""
  
  from __future__ import division
  from __future__ import print_function
  
  from tensor2tensor.models.video import basic_deterministic_params
  from tensor2tensor.utils import registry
  
  
  @registry.register_hparams
  def next_frame_epva():
    """EPVA hparams."""
    hparams = basic_deterministic_params.next_frame_basic_deterministic()
    hparams.video_num_input_frames = 4
    hparams.video_num_target_frames = 4
    hparams.target_modality = "video:l2raw"
    hparams.input_modalities = "inputs:video:l2raw"
    hparams.learning_rate_schedule = "constant"
    hparams.learning_rate_constant = 1e-05
    hparams.batch_size = 2
    hparams.clip_grad_norm = 0.01
    # TODO(msaffar): disentangle EPVA from SV2P
    hparams.add_hparam("reward_prediction", False)
    hparams.add_hparam("context_frames", 5)
    hparams.add_hparam("enc_learning_rate", 1e-5)
    hparams.add_hparam("enc_pred_loss_scale", 0.1)
    hparams.add_hparam("enc_pred_loss_scale_delay", 6e5)
    hparams.add_hparam("enc_size", 64)
    hparams.add_hparam("enc_keep_prob", .65)
    hparams.add_hparam("enc_pred_use_l1_loss", False)
    hparams.add_hparam("enc_pred_use_l2norm", False)
    hparams.add_hparam("van_learning_rate", 3e-5)
    hparams.add_hparam("van_keep_prob", .9)
    hparams.add_hparam("sequence_length ", 64)
    hparams.add_hparam("skip_num", 2)
    hparams.add_hparam("pred_noise_std", 0)
    hparams.add_hparam("lstm_state_noise_stddev", 0)
    return hparams
  