def create_dataset_and_model(config, split, shuffle, repeat):
"""Creates the dataset and model for a given config.
Args:
config: A configuration object with config values accessible as properties.
Most likely a FLAGS object. This function expects the properties
batch_size, dataset_path, dataset_type, and latent_size to be defined.
split: The dataset split to load.
shuffle: If true, shuffle the dataset randomly.
repeat: If true, repeat the dataset endlessly.
Returns:
inputs: A batch of input sequences represented as a dense Tensor of shape
[time, batch_size, data_dimension].
targets: A batch of target sequences represented as a dense Tensor of
shape [time, batch_size, data_dimension].
lens: An int Tensor of shape [batch_size] representing the lengths of each
sequence in the batch.
model: A vrnn.VRNNCell model object.
"""
if config.dataset_type == "pianoroll":
inputs, targets, lengths, mean = datasets.create_pianoroll_dataset(
config.dataset_path,
split,
config.batch_size,
shuffle=shuffle,
repeat=repeat)
# Convert the mean of the training set to logit space so it can be used to
# initialize the bias of the generative distribution.
generative_bias_init = -tf.log(
1. / tf.clip_by_value(mean, 0.0001, 0.9999) - 1)
generative_distribution_class = vrnn.ConditionalBernoulliDistribution
elif config.dataset_type == "speech":
inputs, targets, lengths = datasets.create_speech_dataset(
config.dataset_path,
config.batch_size,
samples_per_timestep=config.data_dimension,
prefetch_buffer_size=1,
shuffle=False,
repeat=False)
generative_bias_init = None
generative_distribution_class = vrnn.ConditionalNormalDistribution
model = vrnn.create_vrnn(inputs.get_shape().as_list()[2],
config.latent_size,
generative_distribution_class,
generative_bias_init=generative_bias_init,
raw_sigma_bias=0.5)
return inputs, targets, lengths, model
def create_dataset_and_model(config, split, shuffle, repeat):
"""Creates the dataset and model for a given config.
Args:
config: A configuration object with config values accessible as properties.
Most likely a FLAGS object. This function expects the properties
batch_size, dataset_path, dataset_type, and latent_size to be defined.
split: The dataset split to load.
shuffle: If true, shuffle the dataset randomly.
repeat: If true, repeat the dataset endlessly.
Returns:
inputs: A batch of input sequences represented as a dense Tensor of shape
[time, batch_size, data_dimension].
targets: A batch of target sequences represented as a dense Tensor of
shape [time, batch_size, data_dimension].
lens: An int Tensor of shape [batch_size] representing the lengths of each
sequence in the batch.
model: A vrnn.VRNNCell model object.
"""
if config.dataset_type == "pianoroll":
inputs, targets, lengths, mean = datasets.create_pianoroll_dataset(
config.dataset_path, split, config.batch_size, shuffle=shuffle,
repeat=repeat)
# Convert the mean of the training set to logit space so it can be used to
# initialize the bias of the generative distribution.
generative_bias_init = -tf.log(
1. / tf.clip_by_value(mean, 0.0001, 0.9999) - 1)
generative_distribution_class = vrnn.ConditionalBernoulliDistribution
elif config.dataset_type == "speech":
inputs, targets, lengths = datasets.create_speech_dataset(
config.dataset_path, config.batch_size,
samples_per_timestep=config.data_dimension, prefetch_buffer_size=1,
shuffle=False, repeat=False)
generative_bias_init = None
generative_distribution_class = vrnn.ConditionalNormalDistribution
model = vrnn.create_vrnn(inputs.get_shape().as_list()[2],
config.latent_size,
generative_distribution_class,
generative_bias_init=generative_bias_init,
raw_sigma_bias=0.5)
return inputs, targets, lengths, model
def create_dataset_and_model(config, split, shuffle, repeat):
inputs, targets, lengths, mmsis, mean = datasets.create_AIS_dataset(config.dataset_path,
config.split,
config.batch_size,
config.data_dim,
config.lat_bins,
config.lon_bins,
config.sog_bins,
config.cog_bins,
shuffle=shuffle,
repeat=repeat)
# Convert the mean of the training set to logit space so it can be used to
# initialize the bias of the generative distribution.
generative_bias_init = -tf.log(1. / tf.clip_by_value(mean, 0.0001, 0.9999) - 1)
generative_distribution_class = vrnn.ConditionalBernoulliDistribution
model = vrnn.create_vrnn(inputs.get_shape().as_list()[2],
config.latent_size,
generative_distribution_class,
generative_bias_init=generative_bias_init,
raw_sigma_bias=0.5)
return inputs, targets, mmsis, lengths, model