这段代码尝试利用dropout的自定义实现：
%reset -f
import torch
import torch.nn as nn
# import torchvision
# import torchvision.transforms as transforms
import torch
import torch.nn as nn
import torch.utils.data as data_utils
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
num_epochs = 1000
number_samples = 10
from sklearn.datasets import make_moons
from matplotlib import pyplot
from pandas import DataFrame
# generate 2d classification dataset
X, y = make_moons(n_samples=number_samples, noise=0.1)
# scatter plot, dots colored by class value
x_data = [a for a in enumerate(X)]
x_data_train = x_data[:int(len(x_data) * .5)]
x_data_train = [i[1] for i in x_data_train]
x_data_train
y_data = [y[i[0]] for i in x_data]
y_data_train = y_data[:int(len(y_data) * .5)]
y_data_train
x_test = [a[1] for a in x_data[::-1][:int(len(x_data) * .5)]]
y_test = [a for a in y_data[::-1][:int(len(y_data) * .5)]]
x = torch.tensor(x_data_train).float() # <2>
print(x)
y = torch.tensor(y_data_train).long()
print(y)
x_test = torch.tensor(x_test).float()
print(x_test)
y_test = torch.tensor(y_test).long()
print(y_test)
class Dropout(nn.Module):
def __init__(self, p=0.5, inplace=False):
# print(p)
super(Dropout, self).__init__()
if p < 0 or p > 1:
raise ValueError("dropout probability has to be between 0 and 1, "
"but got {}".format(p))
self.p = p
self.inplace = inplace
def forward(self, input):
print(list(input.shape))
return np.random.binomial([np.ones((len(input),np.array(list(input.shape))))],1-dropout_percent)[0] * (1.0/(1-self.p))
def __repr__(self):
inplace_str = ', inplace' if self.inplace else ''
return self.__class__.__name__ + '(' \
+ 'p=' + str(self.p) \
+ inplace_str + ')'
class MyLinear(nn.Linear):
def __init__(self, in_feats, out_feats, drop_p, bias=True):
super(MyLinear, self).__init__(in_feats, out_feats, bias=bias)
self.custom_dropout = Dropout(p=drop_p)
def forward(self, input):
dropout_value = self.custom_dropout(self.weight)
return F.linear(input, dropout_value, self.bias)
my_train = data_utils.TensorDataset(x, y)
train_loader = data_utils.DataLoader(my_train, batch_size=2, shuffle=True)
my_test = data_utils.TensorDataset(x_test, y_test)
test_loader = data_utils.DataLoader(my_train, batch_size=2, shuffle=True)
# Device configuration
device = 'cpu'
print(device)
# Hyper-parameters
input_size = 2
hidden_size = 100
num_classes = 2
learning_rate = 0.0001
pred = []
# Fully connected neural network with one hidden layer
class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes, p):
super(NeuralNet, self).__init__()
# self.drop_layer = nn.Dropout(p=p)
# self.drop_layer = MyLinear()
# self.fc1 = MyLinear(input_size, hidden_size, p)
self.fc1 = MyLinear(input_size, hidden_size , p)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size, num_classes)
def forward(self, x):
# out = self.drop_layer(x)
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out
model = NeuralNet(input_size, hidden_size, num_classes, p=0.9).to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# Train the model
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
# Move tensors to the configured device
images = images.reshape(-1, 2).to(device)
labels = labels.to(device)
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (epoch) % 100 == 0:
print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
自定义辍学的实现方式为：
class Dropout(nn.Module):
def __init__(self, p=0.5, inplace=False):
# print(p)
super(Dropout, self).__init__()
if p < 0 or p > 1:
raise ValueError("dropout probability has to be between 0 and 1, "
"but got {}".format(p))
self.p = p
self.inplace = inplace
def forward(self, input):
print(list(input.shape))
return np.random.binomial([np.ones((len(input),np.array(list(input.shape))))],1-dropout_percent)[0] * (1.0/(1-self.p))
def __repr__(self):
inplace_str = ', inplace' if self.inplace else ''
return self.__class__.__name__ + '(' \
+ 'p=' + str(self.p) \
+ inplace_str + ')'
class MyLinear(nn.Linear):
def __init__(self, in_feats, out_feats, drop_p, bias=True):
super(MyLinear, self).__init__(in_feats, out_feats, bias=bias)
self.custom_dropout = Dropout(p=drop_p)
def forward(self, input):
dropout_value = self.custom_dropout(self.weight)
return F.linear(input, dropout_value, self.bias)
看来我没有正确实现dropout函数？ ：
np.random.binomial([np.ones((len(input),np.array(list(input.shape))))],1-dropout_percent)[0] * (1.0/(1-self.p))
如何修改以正确利用辍学？
这些帖子有助于达到这一点：
3行Python中Hinton的辍学：
https://iamtrask.github.io/2015/07/28/dropout/
进行自定义辍学功能：https://discuss.pytorch.org/t/making-a-custom-dropout-function/14053/2
最佳答案
看来我没有正确实现辍学功能？
np.random.binomial([np.ones((len(input),np.array(list(input.shape))))],1 dropout_percent)[0] * (1.0/(1-self.p))
实际上，以上实现称为反向丢弃。反向辍学是在各种深度学习框架中实际实现辍学的方式。
什么是反向辍学？
在跳入倒置的辍学之前，了解辍学如何作用于单个神经元可能会有所帮助：
由于在训练阶段，神经元以概率
q（= 1-p）保持打开状态，因此在测试阶段，我们必须模拟训练阶段使用的网络集合的行为。为此，作者建议在测试阶段将激活函数按q比例缩放，以便将训练阶段产生的预期输出用作测试阶段所需的单个输出（Section 10, Multiplicative Gaussian Noise）。从而：反向辍学有些不同。这种方法包括在训练阶段对激活进行缩放，而保持测试阶段不变。比例因子是保持概率
1/1-p = 1/q的倒数，因此：反向辍学有助于一次定义模型，只需更改参数（保持/掉落概率）即可对同一模型进行训练和测试。相反，直接丢弃会强制您在测试阶段修改网络，因为如果不乘以
q输出，则神经元将产生相对于连续神经元期望的值更高的值（因此以下神经元）会饱和或爆炸）：这就是为什么Inverted Dropout是更常见的实现。参考文献：
Dropout Regularization, coursera by Andrew NG
What is inverted dropout?
Dropout: scaling the activation versus inverting the dropout
Analysis of Dropout
如何实现反向辍学Pytorch？
class MyDropout(nn.Module):
def __init__(self, p: float = 0.5):
super(MyDropout, self).__init__()
if p < 0 or p > 1:
raise ValueError("dropout probability has to be between 0 and 1, " "but got {}".format(p))
self.p = p
def forward(self, X):
if self.training:
binomial = torch.distributions.binomial.Binomial(probs=1-self.p)
return X * binomial.sample(X.size()) * (1.0/(1-self.p))
return weights
如何在Numpy中实施？
import numpy as np
pKeep = 0.8
weights = np.ones([1, 5])
binary_value = np.random.rand(weights.shape[0], weights.shape[1]) < pKeep
res = np.multiply(weights, binary_value)
res /= pKeep # this line is called inverted dropout technique
print(res)
如何在Tensorflow中实现？
import tensorflow as tf
tf.enable_eager_execution()
weights = tf.ones(shape=[1, 5])
keep_prob = 0.8
random_tensor = keep_prob
random_tensor += tf.random_uniform(weights.shape)
# 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
binary_tensor = tf.floor(random_tensor)
ret = tf.div(weights, keep_prob) * binary_tensor
print(ret)
关于machine-learning - 从头开始实现辍学，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/54109617/