49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
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import numpy as np
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class network:
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def __init__(self, inputLayerSize, *layerSizes):
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self.weights = []
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self.inputLayerSize = inputLayerSize
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self.oldLayerSize = inputLayerSize
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for layerSize in layerSizes:
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self.weights.append( np.random.default_rng(42).random((self.oldLayerSize, layerSize)) )
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self.oldLayerSize = layerSize
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self.biases = [[0]*layerSize for layerSize in layerSizes]
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self.weights = np.array(self.weights)
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self.biases = np.array(self.biases)
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def reLu(value):
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return max(0, value)
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def process(self, input):
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if type(input) != np.ndarray:
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print("non")
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if input.size != self.inputLayerSize:
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print("vite")
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if input.dtype != np.float64:
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print("aaa")
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for layer, bias in zip(self.weights, self.biases):
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print("---------------------")
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print(input)
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print(layer)
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print(bias)
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input = np.matmul(input, layer)
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input = np.add(input, bias)
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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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test = network(16, 16, 8, 4)
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for y in test.weights:
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print(y, end="\n\n")
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for y in test.biases:
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print(y, end="\n\n")
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print(network.reLu(8))
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print(test.process(np.random.default_rng(42).random((16))))
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