vae.UnsupervisedModel¶

class vae.UnsupervisedModel(n_inputs, n_labels=None, optimizer='Adam', learning_rate=0.001, learning_rate_decay_fn=None, clip_gradients=None, model_dir=None, debug=False)¶

Base abstract class for unsupervised models

Parameters:

n_inputs : int: Length of the input vector.
n_labels : int or None, optional (default=None): Length of the label vector. If not provided, assumes data is un-labeled.
optimizer : str, optional (default=’Adam’): Optimizer to use for training the model. See tf.contrib.layers.OPTIMIZER_CLS_NAMES for available options.
learning_rate : float, optional (default=0.001): Learning rate for training the model.
learning_rate_decay_fn : optional (default=None): Function for decaying the learning rate. Takes learning_rate and global_step, and returns the decayed learning rate.
clip_gradients : float or None, optional (default=None): If provided, global clipping is applied to prevent the gradient norms from exceeding the provided value.
model_dir : str or None, optional (default=None): Path to the model directory. Defaults to the current working directory.
debug : bool, optional (default=False):: Whether to open the TensorFlow session in debug mode.

Attributes:

model_dir: str: Directory for saving and restoring the model

Methods

`collect_stats`(data, tensors[, enable_summaries])	Collect statistics on the provided data
`evaluate`(data[, tensors, combine_func, …])	Evaluate the model on the provided data.
`fit`(train_data[, validation_data, epochs, …])	Train the model using the provided training data

collect_stats(data, tensors, enable_summaries=False, **feed_dict)¶

Collect statistics on the provided data

Parameters:	data Input data. Can be a NumPy array, a SciPy sparse matrix, or a `Dataset` object. tensors: list of str List containing names of statistics to collect. enable_summaries : bool, optional (default=False) Whether to write summaries to disk. **feed_dict Additional keyword arguments to append to the feed dictionary used for evaluation.
Returns:	results : list The collected statistics.

evaluate(data, tensors=None, combine_func=<function _concat>, enable_summaries=False, batch_size=100, **feed_dict)¶

Evaluate the model on the provided data.

Parameters:

data: Input data. Can be a NumPy array, a SciPy sparse matrix, or a Dataset object.
tensors : list of str or None, optional (default=None): List of tensor names to collect. Defaults to all available tensors.
combine_func : optional: Function to use to combine collected data from all batches. If set to None, each batch is returned separately. Defaults to concatenation along the first axis.
enable_summaries : bool, optional (default=False): Whether to write summaries to disk.
batch_size : int, optional (default=100): Size of mini-batches.
**feed_dict: Additional keyword arguments to append to the feed dictionary used for evaluation.

Yields:

results : list: If combine_func is None, a list containing outputs from a single batch, otherwise yields the combined results from all batches at once.

fit(train_data, validation_data=None, epochs=1, shuffle=True, restore=False, summary_steps=None, init_feed_dict={}, validation_feed_dict={}, batch_size=100, **train_feed_dict)¶

Train the model using the provided training data

Parameters:

train_data: Training data. Can be a NumPy array, a SciPy sparse matrix, or a Dataset object.
validation_data : optional (default=None): Validation data. Can be a NumPy array, a SciPy sparse matrix, or a Dataset object.
epochs : int (default=1): Number of training epochs.
shuffle : bool, optional (default=True): Whether to shuffle training data before each epoch. Ignored if training data is a Dataset object.
restore : bool, optional (default=False): Whether to restore the model from a previous checkpoint.
summary_steps : int or None, optional (default=None): Number of steps between writing summaries, or None for disabling summaries.
init_feed_dict : dict, optional (default={}): Feed dictionary to append for initialization.
validation_feed_dict : dict, optional (default={}): Feed dictionary to append for validation.
batch_size : int, optional (default=100): Size of mini-batches.
**feed_dict: Additional keyword arguments to append to the feed dictionary used for training

Returns:

self

model_dir¶: str: Directory for saving and restoring the model