56 lines
2.0 KiB
Python
Executable File
56 lines
2.0 KiB
Python
Executable File
import numpy as np
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class network:
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def __init__(self, inputLayerSize, *layerSizes):
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if type(inputLayerSize) != int:
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raise TypeError("The input layer size must be an int!")
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self.__weights = []
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self.__inputLayerSize = inputLayerSize
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oldLayerSize = inputLayerSize
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for layerSize in layerSizes:
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self.__weights.append( np.random.default_rng(42).random((oldLayerSize, layerSize)) )
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oldLayerSize = layerSize
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self.__biases = [[0]*layerSize for layerSize in layerSizes]
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self.__weights = np.array(self.__weights, dtype=object)
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self.__biases = np.array(self.__biases, dtype=object)
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def __reLu(value, derivative=False):
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if (derivative):
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return 0 if (value == 0) else 1
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return max(0, value)
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def __sigmoid(value, derivative=False):
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if (derivative):
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return __sigmoid(value) * (1 - __sigmoid(value))
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return 1/(1+np.exp(-value))
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def process(self, input, storeValues=False):
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if type(input) != np.ndarray:
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raise TypeError("The input must be a vector!")
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if input.size != self.__inputLayerSize:
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raise ValueError("The input vector has the wrong size!")
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if input.dtype != np.float64:
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raise TypeError("The input vector must contain floats!")
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for layerWeights, bias in zip(self.__weights, self.__biases):
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input = np.matmul(input, layerWeights)
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input = np.add(input, bias)
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#reLu application
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with np.nditer(input, op_flags=['readwrite']) as layer:
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for neuron in layer:
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neuron = network.__reLu(neuron)
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return input
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def train(self, inputs, results):
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self.__outputs = 1
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#for j in range(1,):
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#partialDerivatives
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def __Error(layer, output, desiredOutput):
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return __ErrorFinalLayerFromValue() if (layer == 1)
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def __ErrorFinalLayer(self, neuron):
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return __reLu(value, true) * (output - desiredOutput) |