def next_frame_basic_stochastic_discrete():
"""Basic 2-frame conv model with stochastic discrete latent."""
hparams = basic_deterministic_params.next_frame_sampling()
hparams.batch_size = 4
hparams.video_num_target_frames = 6
hparams.scheduled_sampling_mode = "prob_inverse_lin"
hparams.scheduled_sampling_decay_steps = 40000
hparams.scheduled_sampling_max_prob = 1.0
hparams.dropout = 0.15
hparams.filter_double_steps = 3
hparams.hidden_size = 96
hparams.learning_rate_constant = 0.002
hparams.learning_rate_warmup_steps = 2000
hparams.learning_rate_schedule = "linear_warmup * constant"
hparams.concat_internal_states = True
hparams.video_modality_loss_cutoff = 0.03
hparams.add_hparam("bottleneck_bits", 128)
hparams.add_hparam("bottleneck_noise", 0.1)
hparams.add_hparam("discretize_warmup_steps", 40000)
hparams.add_hparam("latent_rnn_warmup_steps", 40000)
hparams.add_hparam("latent_rnn_max_sampling", 0.5)
hparams.add_hparam("latent_use_max_probability", 0.8)
hparams.add_hparam("full_latent_tower", False)
hparams.add_hparam("latent_predictor_state_size", 128)
hparams.add_hparam("latent_predictor_temperature", 1.0)
hparams.add_hparam("complex_addn", True)
hparams.add_hparam("recurrent_state_size", 64)
return hparams
def next_frame_basic_stochastic_discrete():
"""Basic 2-frame conv model with stochastic discrete latent."""
hparams = basic_deterministic_params.next_frame_sampling()
hparams.add_hparam("bottleneck_bits", 16)
hparams.add_hparam("bottleneck_noise", 0.02)
hparams.add_hparam("full_latent_tower", False)
return hparams
def next_frame_basic_stochastic_discrete():
"""Basic 2-frame conv model with stochastic discrete latent."""
hparams = basic_deterministic_params.next_frame_sampling()
hparams.batch_size = 4
hparams.video_num_target_frames = 6
hparams.scheduled_sampling_mode = "prob_inverse_lin"
hparams.scheduled_sampling_decay_steps = 40000
hparams.scheduled_sampling_max_prob = 1.0
hparams.dropout = 0.15
hparams.filter_double_steps = 3
hparams.hidden_size = 96
hparams.learning_rate_constant = 0.002
hparams.learning_rate_warmup_steps = 2000
hparams.learning_rate_schedule = "linear_warmup * constant"
hparams.concat_internal_states = True
hparams.add_hparam("bottleneck_bits", 256)
hparams.add_hparam("bottleneck_noise", 0.1)
hparams.add_hparam("discretize_warmup_steps", 40000)
hparams.add_hparam("latent_rnn_warmup_steps", 40000)
hparams.add_hparam("latent_rnn_max_sampling", 0.6)
hparams.add_hparam("latent_use_max_probability", 0.8)
hparams.add_hparam("full_latent_tower", False)
hparams.add_hparam("latent_predictor_state_size", 128)
hparams.add_hparam("latent_predictor_temperature", 0.9)
hparams.add_hparam("complex_addn", True)
return hparams
def next_frame_basic_stochastic_discrete():
"""Basic 2-frame conv model with stochastic discrete latent."""
hparams = basic_deterministic_params.next_frame_sampling()
hparams.add_hparam("bottleneck_bits", 32)
hparams.add_hparam("bottleneck_noise", 0.05)
hparams.add_hparam("discrete_warmup_steps", 4000)
hparams.add_hparam("full_latent_tower", False)
hparams.add_hparam("complex_addn", True)
return hparams
def next_frame_basic_stochastic_discrete():
"""Basic 2-frame conv model with stochastic discrete latent."""
hparams = basic_deterministic_params.next_frame_sampling()
hparams.batch_size = 2
hparams.video_num_target_frames = 16
hparams.scheduled_sampling_warmup_steps = 40000
hparams.scheduled_sampling_prob = 1.0
hparams.add_hparam("bottleneck_bits", 64)
hparams.add_hparam("bottleneck_noise", 0.02)
hparams.add_hparam("discrete_warmup_steps", 40000)
hparams.add_hparam("full_latent_tower", False)
hparams.add_hparam("latent_predictor_state_size", 128)
hparams.add_hparam("latent_predictor_temperature", 0.5)
hparams.add_hparam("complex_addn", True)
return hparams
def next_frame_sampling_stochastic():
"""Basic 2-frame conv model with stochastic tower."""
hparams = basic_deterministic_params.next_frame_sampling()
hparams.stochastic_model = True
hparams.add_hparam("latent_channels", 1)
hparams.add_hparam("latent_std_min", -5.0)
hparams.add_hparam("num_iterations_1st_stage", 15000)
hparams.add_hparam("num_iterations_2nd_stage", 15000)
hparams.add_hparam("latent_loss_multiplier", 1e-3)
hparams.add_hparam("latent_loss_multiplier_dynamic", False)
hparams.add_hparam("latent_loss_multiplier_alpha", 1e-5)
hparams.add_hparam("latent_loss_multiplier_epsilon", 1.0)
hparams.add_hparam("latent_loss_multiplier_schedule", "constant")
hparams.add_hparam("latent_num_frames", 0) # 0 means use all frames.
hparams.add_hparam("anneal_end", 40000)
hparams.add_hparam("information_capacity", 0.0)
return hparams
def next_frame_sampling_stochastic():
"""Basic 2-frame conv model with stochastic tower."""
hparams = basic_deterministic_params.next_frame_sampling()
hparams.stochastic_model = True
hparams.add_hparam("latent_channels", 1)
hparams.add_hparam("latent_std_min", -5.0)
hparams.add_hparam("num_iterations_1st_stage", 15000)
hparams.add_hparam("num_iterations_2nd_stage", 15000)
hparams.add_hparam("latent_loss_multiplier", 1e-3)
hparams.add_hparam("latent_loss_multiplier_dynamic", False)
hparams.add_hparam("latent_loss_multiplier_alpha", 1e-5)
hparams.add_hparam("latent_loss_multiplier_epsilon", 1.0)
hparams.add_hparam("latent_loss_multiplier_schedule", "constant")
hparams.add_hparam("latent_num_frames", 0) # 0 means use all frames.
hparams.add_hparam("anneal_end", 40000)
hparams.add_hparam("information_capacity", 0.0)
return hparams
def next_frame_basic_stochastic_discrete():
"""Basic 2-frame conv model with stochastic discrete latent."""
hparams = basic_deterministic_params.next_frame_sampling()
hparams.batch_size = 2
hparams.video_num_target_frames = 16
hparams.scheduled_sampling_mode = "prob_inverse_lin"
hparams.scheduled_sampling_decay_steps = 40000
hparams.scheduled_sampling_max_prob = 1.0
hparams.dropout = 0.3
hparams.learning_rate_constant = 0.002
hparams.learning_rate_warmup_steps = 2000
hparams.learning_rate_schedule = "linear_warmup * constant"
hparams.add_hparam("bottleneck_bits", 64)
hparams.add_hparam("bottleneck_noise", 0.02)
hparams.add_hparam("discretize_warmup_steps", 40000)
hparams.add_hparam("full_latent_tower", False)
hparams.add_hparam("latent_predictor_state_size", 128)
hparams.add_hparam("latent_predictor_temperature", 0.5)
hparams.add_hparam("complex_addn", True)
return hparams
