test dense layer
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from NeuralNetwork import *
import util
np.random.seed(1)
pts = 100
k = 2
b =1
X = np.random.randn(pts,1)*10 # 随机采样一些x坐标
Y = k*X+b
Y = Y+ np.random.randn(pts,1)*4 #给 Y随机噪声
plt.plot(X,Y,'o')
plt.xlabel('x')
plt.ylabel('y')
plt.show()
import util
dense = Dense(1,1,('no',0.01))
losses = []
epochs = 100
reg = 0.
learning_rate = 1e-2
nn = dense
for epoch in range(epochs):
for i in range(len(nn.params)):
nn.grads[i] = 0.
#print(nn.params[0],nn.params[1])
F = nn.forward(X)
loss,grad = util.loss_grad_least(F,Y)
dx = dense.backward(grad)
for i in range(len(nn.params)):
nn.params[i] -= learning_rate*nn.grads[i]
print(loss)
losses.append(loss)
plt.plot(losses)
test NeuralNetwork
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from NeuralNetwork import *
import util
pts = 1000
k = 2
b =1
X = np.random.randn(pts,1)*10 # 随机采样一些x坐标
Y = k*X+b
Y = Y+ np.random.randn(pts,1)*4 #给 Y随机噪声
plt.plot(X,Y,'o')
plt.xlabel('x')
plt.ylabel('y')
plt.show()
m,n = X.shape[0],X.shape[1]
print("n",n)
nn = NeuralNetwork()
dense = Dense(1,1,('no',0.01))
nn.add_layer(Dense(1,1,('xavier',0.01))) # dense)
learning_rate = 1e-3
momentum = 0 #0.9
optimizer = SGD(nn.parameters(),learning_rate,momentum)
losses = []
epochs = 100
reg = 0.
for epoch in range(epochs):
F = nn.forward(X)
loss,loss_grad = util.loss_grad_least(F,Y)
optimizer.zero_grad()
nn.backward(loss_grad,reg)
loss += nn.reg_loss(reg)
optimizer.step()
losses.append(loss)
if epoch % 10 == 4:
print('epoch {}, loss {}'.format(epoch,loss.item())) #.data[0]))
plt.plot(losses)
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