激發函數

import torch

import torch.nn.functional as F # 激勵函數都在這

from torch.autograd import Variable

# 做一些假資料來觀看圖像

x = torch.linspace(-5, 5, 200) # x data (tensor), shape=(100, 1)

x = Variable(x)

x_np = x.data.numpy() # 換成 numpy array, 出圖時用

# 幾種常用的 激勵函數

y_relu = F.relu(x).data.numpy()

y_sigmoid = F.sigmoid(x).data.numpy()

y_tanh = F.tanh(x).data.numpy()

y_softplus = F.softplus(x).data.numpy()

# y_softmax = F.softmax(x) softmax 比較特殊, 不能直接顯示, 不過他是關於概率的, 用於分類

import matplotlib.pyplot as plt

plt.figure(1, figsize=(8, 6))

plt.subplot(221)

plt.plot(x_np, y_relu, c='red', label='relu')

plt.ylim((-1, 5))

plt.legend(loc='best')

plt.subplot(222)

plt.plot(x_np, y_sigmoid, c='red', label='sigmoid')

plt.ylim((-0.2, 1.2))

plt.legend(loc='best')

plt.subplot(223)

plt.plot(x_np, y_tanh, c='red', label='tanh')

plt.ylim((-1.2, 1.2))

plt.legend(loc='best')

plt.subplot(224)

plt.plot(x_np, y_softplus, c='red', label='softplus')

plt.ylim((-0.2, 6))

plt.legend(loc='best')

plt.show()

import torch

from torch.autograd import Variable

tensor = torch.FloatTensor([[1,2],[3,4]])

variable = Variable(tensor, requires_grad=True)

print(tensor)

print(variable)

t_out = torch.mean(tensor*tensor)

v_out = torch.mean(variable*variable)

#v_out = 1/4 x sum(variable x variable)

print(t_out)

print(v_out)

v_out.backward()

#d(v_out)/d(variable)=1/4 x 2 x variable = 1/2 x variable

print(variable.grad)