sugartensor.sg_train module

sugartensor.sg_train.sg_init(sess)

Initializes session variables.

Args:

sess: Session to initialize.

sugartensor.sg_train.sg_optim(loss, **kwargs)

Applies gradients to variables.

Args:

loss: A 0-D Tensor containing the value to minimize. kwargs:

optim: A name for optimizer. ‘MaxProp’ (default), ‘AdaMax’, ‘Adam’, or ‘sgd’. lr: A Python Scalar (optional). Learning rate. Default is .001. beta1: A Python Scalar (optional). Default is .9. beta2: A Python Scalar (optional). Default is .99. category: A string or string list. Specifies the variables that should be trained (optional).

Only if the name of a trainable variable starts with category, it’s value is updated. Default is ‘’, which means all trainable variables are updated.

sugartensor.sg_train.sg_print(tensor_list)

Simple tensor printing function for debugging. Prints the value, shape, and data type of each tensor in the list.

Args:

tensor_list: A list/tuple of tensors or a single tensor.

Returns:

The value of the tensors.

For example,

`python import sugartensor as tf a = tf.constant([1.]) b = tf.constant([2.]) out = tf.sg_print([a, b]) # Should print [ 1.] (1,) float32 #              [ 2.] (1,) float32 print(out) # Should print [array([ 1.], dtype=float32), array([ 2.], dtype=float32)] `

sugartensor.sg_train.sg_restore(sess, save_path, category='')

Restores previously saved variables.

Args:

sess: A Session to use to restore the parameters. save_path: Path where parameters were previously saved. category: A String to filter variables starts with given category.

Returns:

sugartensor.sg_train.sg_train(**kwargs)

Trains the model.

Args:

**kwargs:

optim: A name for optimizer. ‘MaxProp’ (default), ‘AdaMax’, ‘Adam’, or ‘sgd’. loss: A 0-D Tensor containing the value to minimize. lr: A Python Scalar (optional). Learning rate. Default is .001. beta1: A Python Scalar (optional). Default is .9. beta2: A Python Scalar (optional). Default is .99.

eval_metric: A list of tensors containing the value to evaluate. Default is []. early_stop: Boolean. If True (default), the training should stop when the following two conditions are met.

1. Current loss is less than .95 * previous loss.
2. Current learning rate is less than 5e-6.

lr_reset: Boolean. If True, learning rate is set to opt.lr. when training restarts.

Otherwise (Default), the value of the stored _learning_rate is taken.

save_dir: A string. The root path to which checkpoint and log files are saved.

Default is asset/train.

max_ep: A positive integer. Maximum number of epochs. Default is 1000. ep_size: A positive integer. Number of Total batches in an epoch.

For proper display of log. Default is 1e5.

save_interval: A Python scalar. The interval of saving checkpoint files.

By default, for every 600 seconds, a checkpoint file is written.

log_interval: A Python scalar. The interval of recoding logs.

By default, for every 60 seconds, logging is executed.

max_keep: A positive integer. Maximum number of recent checkpoints to keep. Default is 5. keep_interval: A Python scalar. How often to keep checkpoints. Default is 1 hour.

category: Scope name or list to train

tqdm: Boolean. If True (Default), progress bars are shown. console_log: Boolean. If True, a series of loss will be shown

on the console instead of tensorboard. Default is False.

sugartensor.sg_train.sg_train_func(func)

Decorates a function func as sg_train_func.

Args:

func: A function to decorate