tf.contrib.model_pruning.get_pruning_hparams

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Get a tf.HParams object with the default values for the hyperparameters.

tf.contrib.model_pruning.get_pruning_hparams()

name: string name of the pruning specification. Used for adding summaries and ops under a common tensorflow name_scope begin_pruning_step: integer the global step at which to begin pruning end_pruning_step: integer the global step at which to terminate pruning. Defaults to -1 implying that pruning continues till the training stops weight_sparsity_map: list of strings comma separed list of {weight_variable_name:target sparsity} or {regex:target sparsity} pairs. For layers/weights not in this list, sparsity as specified by the target_sparsity hyperparameter is used. Eg. [conv1:0.9,conv2/kernel:0.8] block_dims_map: list of strings comma separated list of {weight variable name:block_height x block_width} or {regex:block_height x block_width} pairs. For layers/weights not in this list, block dims are specified by the block_height, block_width hyperparameters are used Eg. [dense1:4x4,dense2:1x16,dense3:1x1] threshold_decay: float the decay factor to use for exponential decay of the thresholds pruning_frequency: integer How often should the masks be updated? (in # of global_steps) nbins: integer number of bins to use for histogram computation block_height: integer number of rows in a block (defaults to 1), can be -1 in which case it is set to the size of the corresponding weight tensor. block_width: integer number of cols in a block (defaults to 1), can be -1 in which case it is set to the size of the corresponding weight tensor. block_pooling_function: string Whether to perform average (AVG) or max (MAX) pooling in the block (default: AVG) initial_sparsity: float initial sparsity value target_sparsity: float target sparsity value sparsity_function_begin_step: integer the global step at this which the gradual sparsity function begins to take effect sparsity_function_end_step: integer the global step used as the end point for the gradual sparsity function sparsity_function_exponent: float exponent = 1 is linearly varying sparsity between initial and final. exponent > 1 varies more slowly towards the end than the beginning use_tpu: False Indicates whether to use TPU

We use the following sparsity function:

num_steps = (sparsity_function_end_step - sparsity_function_begin_step)/pruning_frequency sparsity(step) = (initial_sparsity - target_sparsity)* [1-step/(num_steps -1)]**exponent + target_sparsity