def __init__(self,
hidden_sizes=DEFAULT_HIDDEN_SIZES,
weights=DEFAULT_WEIGHTS,
inputs=DEFAULT_INPUTS,
outputs=DEFAULT_OUTPUTS,
weight_spread=None,
weight_middle=None):
"""
@hidden_sizes: An iterable of integers that describe the sizes of the
hidden layers of the Net.
@weights: May be a function that returns arrays to use as weights.
If so, must take an iterable of sizes to create weights for
and must return the same data as described below.
Else it must be numpy.ndarrays of dtype=float and proper sizes
in the proper order provided in a sliceable.
@inputs: The integer number of inputs.
@outputs: The integer number of outputs.
"""
if not isinstance(inputs, int) or not isinstance(outputs, int):
raise ValueError('Number of inputs and outputs must be integers')
if (not hasattr(hidden_sizes, '__iter__')
or not all(isinstance(i, int) for i in hidden_sizes)):
raise ValueError('Sizes of hidden layers must be integers'
' provided in an iterable')
self.sizes = tuple(chain((inputs,),
hidden_sizes,
(outputs,)))
if weights and callable(weights):
weights = weights(self.sizes)
if (weights and (not hasattr(weights, '__getslice__')
or not all(isinstance(arr, numpy.ndarray)
for arr in weights)
or not all(arr.dtype == float for arr in weights))):
raise ValueError('Weights of hidden layers must be numpy.ndarrays'
' with dtype=float provided in a sliceable')
self.inputs = inputs
self.outputs = outputs
self.weights = weights or Net.random_weights(self.sizes,
weight_spread,
weight_middle)
for idx, w in enumerate(self.weights):
assert(w.shape == (self.sizes[idx], self.sizes[idx+1]))
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