33 lines
829 B
Python
33 lines
829 B
Python
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from layer import *
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import numpy as np
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import pickle
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DIMENTION = 3
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VAR_RANGE = 1
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N = 10
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ALPHA = 0.1
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SIGMA = VAR_RANGE * ALPHA
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gen: np.random.Generator = np.random.default_rng()
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or_weight = (gen.uniform(high = VAR_RANGE,low = -VAR_RANGE,size=(1,DIMENTION)))
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or_bias = (gen.uniform(high = VAR_RANGE, low = -VAR_RANGE,size=()))
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print(or_bias)
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input_x = gen.uniform(low = -VAR_RANGE,high=VAR_RANGE,size=(DIMENTION,N))
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y = or_weight @ input_x + or_bias
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error = gen.normal(0,SIGMA,size = (1,N))
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y += error
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print(y)
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input_var = Variable(np.array([[1],[2],[3]]))
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weight = Variable(np.array([[2,-1,1]]))
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bias = Variable(np.array([[1]]))
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v = ((weight @ input_var) + bias)
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print(make_mermaid_graph(v))
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print(f"result : {v.numpy()}")
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v.backprop(np.ones(()))
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print(f"grad input : {input_var.grad}, w : {weight.grad}, b : {bias.grad}")
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