Trainer

[TOC]

Generic trainer for TensorFlow models.

Other Functions and Classes

class skflow.TensorFlowTrainer

General trainer class.

Attributes: model: Model object. gradients: Gradients tensor. - - -

skflow.TensorFlowTrainer.__init__(loss, global_step, optimizer, learning_rate, clip_gradients=5.0) {#TensorFlowTrainer.init}

Build a trainer part of graph.

Args:

• loss: Tensor that evaluates to model’s loss.
• global_step: Tensor with global step of the model.
• optimizer: Name of the optimizer class (SGD, Adam, Adagrad) or class.
• learning_rate: If this is constant float value, no decay function is used. Instead, a customized decay function can be passed that accepts global_step as parameter and returns a Tensor. e.g. exponential decay function: def exp_decay(global_step): return tf.train.exponential_decay( learning_rate=0.1, global_step=global_step, decay_steps=2, decay_rate=0.001)

Raises:

• ValueError: if learning_rate is not a float or a callable.

skflow.TensorFlowTrainer.initialize(sess) {#TensorFlowTrainer.initialize}

Initalizes all variables.

Args:

• sess: Session object.

Returns:

Values of initializers.

skflow.TensorFlowTrainer.train(sess, feed_dict_fn, steps, monitor, summary_writer=None, summaries=None, feed_params_fn=None) {#TensorFlowTrainer.train}

Trains a model for given number of steps, given feed_dict function.

Args:

• sess: Session object.
• feed_dict_fn: Function that will return a feed dictionary.
• summary_writer: SummaryWriter object to use for writing summaries.
• steps: Number of steps to run.
• monitor: Monitor object to track training progress and induce early stopping
• summaries: Joined object of all summaries that should be ran.

Returns:

List of losses for each step.