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_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