snntoolbox.utils

snntoolbox.utils.utils

General utility functions on project-level.

@author: rbodo

snntoolbox.utils.utils.get_range(start=0.0, stop=1.0, num=5, method='linear')

Return a range of parameter values.

Convenience function. For more flexibility, use numpy.linspace, numpy.logspace, numpy.random.random_sample directly.

Parameters:

• start (float) – The starting value of the sequence
• stop (float) – End value of the sequence.
• num (int) – Number of samples to generate. Must be non-negative.
• method (str) – The sequence will be computed on either a linear, logarithmic or random grid.

Returns:

samples – There are num samples in the closed interval [start, stop].

Return type:

np.array

snntoolbox.utils.utils.confirm_overwrite(filepath)

If config.get(‘output’, ‘overwrite’)==False and the file exists, ask user if it should be overwritten.

snntoolbox.utils.utils.to_json(data, path)

Write data dictionary to path.

A TypeError is raised if objects in data are not JSON serializable.

snntoolbox.utils.utils.import_helpers(filepath, config)

Import a module with helper functions from filepath.

Parameters:

• filepath (str) – Filename or relative or absolute path of module to import. If only the filename is given, module is assumed to be in current working directory (config.get('paths', 'path_wd')). Non-absolute paths are taken relative to working dir.
• config (configparser.ConfigParser) – Settings.

Returns:

Module with helper functions.

snntoolbox.utils.utils.get_abs_path(filepath, config)

Get an absolute path, possibly using current toolbox working dir.

Parameters:

• filepath (str) – Filename or relative or absolute path. If only the filename is given, file is assumed to be in current working directory (config.get('paths', 'path_wd')). Non-absolute paths are interpreted relative to working dir.
• config (configparser.ConfigParser) – Settings.

Returns:

path – Absolute path to file.

Return type:

str

snntoolbox.utils.utils.import_script(path, filename)

Import python script independently from python version.

Parameters:

• path (string) – Path to directory where to load script from.
• filename (string) – Name of script file.

snntoolbox.utils.utils.binary_tanh(x)

Round a float to -1 or 1.

Parameters:x (float) – Returns:Integer in {-1, 1} Return type:int

snntoolbox.utils.utils.binary_sigmoid(x)

Round a float to 0 or 1.

Parameters:x (float) – Returns:Integer in {0, 1} Return type:int

snntoolbox.utils.utils.hard_sigmoid(x)

Parameters:x –

snntoolbox.utils.utils.binarize_var(w, h=1.0, deterministic=True)

Binarize shared variable.

Parameters:

• w (keras.backend.Variable) – Weights.
• h (float) – Values are round to +/-h.
• deterministic (bool) – Whether to apply deterministic rounding.

Returns:

w – The binarized weights.

Return type:

keras.backend.variable

snntoolbox.utils.utils.binarize(w, h=1.0, deterministic=True)

Binarize weights.

Parameters:

• w (ndarray) – Weights.
• h (float) – Values are round to +/-h.
• deterministic (bool) – Whether to apply deterministic rounding.

Returns:

The binarized weights.

Return type:

ndarray

snntoolbox.utils.utils.reduce_precision(x, m, f)

Reduces precision of x to format Qm.f.

Parameters:

• x (ndarray) – Input data.
• m (int) – Number of integer bits.
• f (int) – Number of fractional bits.

Returns:

x_lp – The input data with reduced precision.

Return type:

ndarray

snntoolbox.utils.utils.reduce_precision_var(x, m, f)

Reduces precision of x to format Qm.f.

Parameters:

• x (keras.backend.variable) – Input data.
• m (int) – Number of integer bits.
• f (int) – Number of fractional bits.

Returns:

x_lp – The input data with reduced precision.

Return type:

keras.backend.variable

snntoolbox.utils.utils.quantized_relu(x, m, f)

Rectified linear unit activation function with precision of x reduced to fixed point format Qm.f.

Parameters:

• x (keras.backend.variable) – Input data.
• m (int) – Number of integer bits.
• f (int) – Number of fractional bits.

Returns:

x_lp – The input data with reduced precision.

Return type:

keras.backend.variable

class snntoolbox.utils.utils.LimitedReLU(cfg)

Bases: keras.layers.activation.relu.ReLU

get_cfg()

set_cfg(cfg)

class snntoolbox.utils.utils.ClampedReLU(threshold=0.1, max_value=None)

Bases: object

Rectified linear unit activation function where values in x below threshold are clamped to 0, and values above max_value are clipped to max_value.

threshold

Type:Optional[float]

max_value

Type:Optional[float]

class snntoolbox.utils.utils.NoisySoftplus(k=0.17, sigma=1)

Bases: object

snntoolbox.utils.utils.wilson_score(p, n)

Confidence interval of a binomial distribution.

See https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval.

Parameters:

• p (float) – The proportion of successes in n experiments.
• n (int) – The number of Bernoulli-trials (sample size).

Returns:

Return type:

The confidence interval.

snntoolbox.utils.utils.extract_label(label)

Get the layer number, name and shape from a string.

Parameters:label (str) – Specifies both the layer type, index and shape, e.g. '03Conv2D_3x32x32'. Returns:

• layer_num: The index of the layer in the network.
• name: The type of the layer.
• shape: The shape of the layer

Return type:tuple[int, str, tuple]

snntoolbox.utils.utils.in_top_k(predictions, targets, k)

Returns whether the targets are in the top k predictions.

# Arguments

predictions: A tensor of shape batch_size x classes and type float32. targets: A tensor of shape batch_size and type int32 or int64. k: An int, number of top elements to consider.

# Returns

A tensor of shape batch_size and type int. output_i is 1 if targets_i is within top-k values of predictions_i

snntoolbox.utils.utils.top_k_categorical_accuracy(y_true, y_pred, k=5)

Parameters:

• y_true –
• y_pred –
• k –

snntoolbox.utils.utils.echo(text)

python 2 version of print(end=’’, flush=True).

snntoolbox.utils.utils.to_list(x)

Normalize a list/tensor to a list.

If a tensor is passed, returns a list of size 1 containing the tensor.

snntoolbox.utils.utils.apply_modifications(model, custom_objects=None)

Applies modifications to the model layers to create a new Graph. For example, simply changing model.layers[idx].activation = new activation does not change the graph. The entire graph needs to be updated with modified inbound and outbound tensors because of change in layer building function.

Parameters:

• model (keras.models.Model) –
• custom_objects (dict) –

Returns:

• The modified model with changes applied. Does not mutate the original
• model.

snntoolbox.utils.utils.import_configparser()

snntoolbox.utils.utils.is_module_installed(mod)

snntoolbox.utils.utils.get_pearson_coefficients(spikerates_batch, activations_batch, max_rate)

Compute Pearson coefficients.

Parameters:

• spikerates_batch –
• activations_batch –
• max_rate (float) – Highest spike rate.

Returns:

co

Return type:

list