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models.py
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# -*- coding:utf-8 -*-
"""
@Time: 2022/03/01 22:23
@Author: KI
@File: models.py
@Motto: Hungry And Humble
"""
import numpy as np
from torch import nn
class BP:
def __init__(self, args, file_name):
self.file_name = file_name
self.len = 0
self.args = args
self.input = np.zeros((args.B, args.input_dim)) # self.B samples per round
self.w1 = 2 * np.random.random((args.input_dim, 20)) - 1 # limit to (-1, 1)
self.z1 = 2 * np.random.random((args.B, 20)) - 1
self.hidden_layer_1 = np.zeros((args.B, 20))
self.w2 = 2 * np.random.random((20, 20)) - 1
self.z2 = 2 * np.random.random((args.B, 20)) - 1
self.hidden_layer_2 = np.zeros((args.B, 20))
self.w3 = 2 * np.random.random((20, 20)) - 1
self.z3 = 2 * np.random.random((args.B, 20)) - 1
self.hidden_layer_3 = np.zeros((args.B, 20))
self.w4 = 2 * np.random.random((20, 1)) - 1
self.z4 = 2 * np.random.random((args.B, 1)) - 1
self.output_layer = np.zeros((args.B, 1))
self.loss = np.zeros((args.B, 1))
def sigmoid(self, x):
return 1 / (1 + np.exp(-x))
def sigmoid_deri(self, x):
return x * (1 - x)
def forward_prop(self, data, label):
self.input = data
self.z1 = np.dot(self.input, self.w1)
self.hidden_layer_1 = self.sigmoid(self.z1)
self.z2 = np.dot(self.hidden_layer_1, self.w2)
self.hidden_layer_2 = self.sigmoid(self.z2)
self.z3 = np.dot(self.hidden_layer_2, self.w3)
self.hidden_layer_3 = self.sigmoid(self.z3)
self.z4 = np.dot(self.hidden_layer_3, self.w4)
self.output_layer = self.sigmoid(self.z4)
# error
self.loss = 1 / 2 * (label - self.output_layer) ** 2
return self.output_layer
def backward_prop(self, label):
# w4
l_deri_out = self.output_layer - label
l_deri_z4 = l_deri_out * self.sigmoid_deri(self.output_layer)
l_deri_w4 = np.dot(self.hidden_layer_3.T, l_deri_z4)
# w3
l_deri_h3 = np.dot(l_deri_z4, self.w4.T)
l_deri_z3 = l_deri_h3 * self.sigmoid_deri(self.hidden_layer_3)
l_deri_w3 = np.dot(self.hidden_layer_2.T, l_deri_z3)
# w2
l_deri_h2 = np.dot(l_deri_z3, self.w3.T)
l_deri_z2 = l_deri_h2 * self.sigmoid_deri(self.hidden_layer_2)
l_deri_w2 = np.dot(self.hidden_layer_1.T, l_deri_z2)
# w1
l_deri_h1 = np.dot(l_deri_z2, self.w2.T)
l_deri_z1 = l_deri_h1 * self.sigmoid_deri(self.hidden_layer_1)
l_deri_w1 = np.dot(self.input.T, l_deri_z1)
# update
self.w4 -= self.args.lr * l_deri_w4
self.w3 -= self.args.lr * l_deri_w3
self.w2 -= self.args.lr * l_deri_w2
self.w1 -= self.args.lr * l_deri_w1
class ANN(nn.Module):
def __init__(self, args, name):
super(ANN, self).__init__()
self.name = name
self.len = 0
self.loss = 0
self.sigmoid = nn.Sigmoid()
self.fc1 = nn.Linear(args.input_dim, 16)
self.fc2 = nn.Linear(16, 32)
self.fc3 = nn.Linear(32, 16)
self.fc4 = nn.Linear(16, 1)
def forward(self, data):
x = self.fc1(data)
x = self.sigmoid(x)
x = self.fc2(x)
x = self.sigmoid(x)
x = self.fc3(x)
x = self.sigmoid(x)
x = self.fc4(x)
x = self.sigmoid(x)
return x