odynn.coptim module

class odynn.coptim.CircuitOpt(circuit)

Bases: odynn.optim.Optimizer

Class for optimization of a neuronal circuit

Methods

optimize(subdir[, train, test, w, w_n, …])	Optimize the neuron parameters
settings(w, train)	Give the settings of the optimization

plot_out

__init__(circuit)

Parameters:circuit (CircuitTf) – Circuit to be optimized

optimize(subdir, train=None, test=None, w=(1, 0), w_n=None, epochs=700, l_rate=(0.9, 9, 0.95), suffix='', n_out=[1], evol_var=True, plot=True)

Optimize the neuron parameters

Parameters:

• dir (str) – path to the directory for the saved files
• train (list of ndarray) –

  list containing [time, input, voltage, ion_concentration] that will be used fitted dimensions : - time : [time]

  • input, voltage and concentration : [time, batch, neuron]

• test (list of ndarray) – same as train for the dimensions These arrays will be used fo testing the model (Default value = None)
• w (list) – list of weights for the loss, the first value is for the voltage and the following ones for the ion concentrations defined in the model. (Default value = [1, 0]:
• epochs (int) – Number of training steps (Default value = 700)
• l_rate (tuple) – Parameters for an exponential decreasing learning rate : (start, number of constant steps, exponent) (Default value = [0.1, 9, 0.92]:
• suffix (str) – suffix for the saved files (Default value = ‘’)
• n_out (list of int) – list of neurons corresponding to the data in train and test

Returns:

neuron attribute after optimization

Return type:

NeuronTf

plot_out(X, results, res_targ, suffix, step, name, i)

settings(w, train)

Give the settings of the optimization

Parameters:w (tuple) – weights for the loss of voltage and ions concentrations Returns:settings Return type:str

odynn.coptim.plot_heatmap(m, name, suffix, labels, n_out=None)