()
return hparams
Return default HParams for sketch-rnn.
Return default HParams for sketch-rnn.
Return default HParams for sketch - rnn .
def get_default_hparams():
  """Return default HParams for sketch-rnn."""
  hparams = contrib_training.HParams(
      data_set=['aaron_sheep.npz'],  # Our dataset.
      num_steps=10000000,  # Total number of steps of training. Keep large.
      save_every=500,  # Number of batches per checkpoint creation.
      max_seq_len=250,  # Not used. Will be changed by model. [Eliminate?]
      dec_rnn_size=512,  # Size of decoder.
      dec_model='lstm',  # Decoder: lstm, layer_norm or hyper.
      enc_rnn_size=256,  # Size of encoder.
      enc_model='lstm',  # Encoder: lstm, layer_norm or hyper.
      z_size=128,  # Size of latent vector z. Recommend 32, 64 or 128.
      kl_weight=0.5,  # KL weight of loss equation. Recommend 0.5 or 1.0.
      kl_weight_start=0.01,  # KL start weight when annealing.
      kl_tolerance=0.2,  # Level of KL loss at which to stop optimizing for KL.
      batch_size=100,  # Minibatch size. Recommend leaving at 100.
      grad_clip=1.0,  # Gradient clipping. Recommend leaving at 1.0.
      num_mixture=20,  # Number of mixtures in Gaussian mixture model.
      learning_rate=0.001,  # Learning rate.
      decay_rate=0.9999,  # Learning rate decay per minibatch.
      kl_decay_rate=0.99995,  # KL annealing decay rate per minibatch.
      min_learning_rate=0.00001,  # Minimum learning rate.
      use_recurrent_dropout=True,  # Dropout with memory loss. Recommended
      recurrent_dropout_prob=0.90,  # Probability of recurrent dropout keep.
      use_input_dropout=False,  # Input dropout. Recommend leaving False.
      input_dropout_prob=0.90,  # Probability of input dropout keep.
      use_output_dropout=False,  # Output dropout. Recommend leaving False.
      output_dropout_prob=0.90,  # Probability of output dropout keep.
      random_scale_factor=0.15,  # Random scaling data augmentation proportion.
      augment_stroke_prob=0.10,  # Point dropping augmentation proportion.
      conditional=True,  # When False, use unconditional decoder-only model.
      is_training=True  # Is model training? Recommend keeping true.
  )
  return hparams
def get_default_hparams ( ) : hparams = contrib_training . HParams ( data_set = [ 'aaron_sheep.npz' ] , # Our dataset. num_steps = 10000000 , # Total number of steps of training. Keep large. save_every = 500 , # Number of batches per checkpoint creation. max_seq_len = 250 , # Not used. Will be changed by model. [Eliminate?] dec_rnn_size = 512 , # Size of decoder. dec_model = 'lstm' , # Decoder: lstm, layer_norm or hyper. enc_rnn_size = 256 , # Size of encoder. enc_model = 'lstm' , # Encoder: lstm, layer_norm or hyper. z_size = 128 , # Size of latent vector z. Recommend 32, 64 or 128. kl_weight = 0.5 , # KL weight of loss equation. Recommend 0.5 or 1.0. kl_weight_start = 0.01 , # KL start weight when annealing. kl_tolerance = 0.2 , # Level of KL loss at which to stop optimizing for KL. batch_size = 100 , # Minibatch size. Recommend leaving at 100. grad_clip = 1.0 , # Gradient clipping. Recommend leaving at 1.0. num_mixture = 20 , # Number of mixtures in Gaussian mixture model. learning_rate = 0.001 , # Learning rate. decay_rate = 0.9999 , # Learning rate decay per minibatch. kl_decay_rate = 0.99995 , # KL annealing decay rate per minibatch. min_learning_rate = 0.00001 , # Minimum learning rate. use_recurrent_dropout = True , # Dropout with memory loss. Recommended recurrent_dropout_prob = 0.90 , # Probability of recurrent dropout keep. use_input_dropout = False , # Input dropout. Recommend leaving False. input_dropout_prob = 0.90 , # Probability of input dropout keep. use_output_dropout = False , # Output dropout. Recommend leaving False. output_dropout_prob = 0.90 , # Probability of output dropout keep. random_scale_factor = 0.15 , # Random scaling data augmentation proportion. augment_stroke_prob = 0.10 , # Point dropping augmentation proportion. conditional = True , # When False, use unconditional decoder-only model. is_training = True # Is model training? Recommend keeping true. ) return hparams