NIN-numpy

numpy实现NIN模型,利用cifar-10cifar-100mnist数据集进行MLPConvGAP的测试

完整实现:zjZSTU/PyNet

MLPConv实现

MLPConv对局部连接执行微神经网络操作,在NIN模型中,每个MLPConv包含一个3MLP

首先需要对输入数据体提取局部连接,可使用一个常规卷积操作实现

比如输入数据体大小为$128\times 3\times 32\times 32$

第一层执行$5\times 5$卷积核大小,步长为$1$,零填充为$2$,滤波器个数为192的卷积操作

输出数据体大小为$128\times 192\times 32\times 32$,特征图上的一点即为输入数据体的单个局部连接结果

第二和第三层执行$1\times 1$卷积核大小,步长为$1$,零填充为$0$的卷积操作

输出数据体空间尺寸为$32\times 32$,不改变输入数据体大小,仅执行深度方向的降维重组

GAP实现

输入张量大小为$N\times C\times H\times W$,对每个特征图进行均值运算,输出大小为$N\times C$,前向运算如下

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>>> a = np.arange(36).reshape(2,2,3,3)
>>> a
array([[[[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8]],

[[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17]]],


[[[18, 19, 20],
[21, 22, 23],
[24, 25, 26]],

[[27, 28, 29],
[30, 31, 32],
[33, 34, 35]]]])
>>> b = a.reshape(2,2,-1) # 2-D特征图转换成1-D向量
>>> b
array([[[ 0, 1, 2, 3, 4, 5, 6, 7, 8],
[ 9, 10, 11, 12, 13, 14, 15, 16, 17]],

[[18, 19, 20, 21, 22, 23, 24, 25, 26],
[27, 28, 29, 30, 31, 32, 33, 34, 35]]])
>>> c = np.mean(b, axis=2) # 计算每个特征图均值
>>> c
array([[ 4., 13.],
[22., 31.]])

反向操作中,输入梯度大小为$N\times C$,单个梯度图所有像素点有相同梯度,输出数据体大小为$N\times C\times H\times W$,反向计算如下:

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>>> a = np.arange(4).reshape(2,2)
>>> a
array([[0, 1],
[2, 3]])
>>> b = a.reshape(4, -1)
>>> b
array([[0],
[1],
[2],
[3]])
>>> c = np.repeat(b, 9, axis=1)
>>> c
array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1],
[2, 2, 2, 2, 2, 2, 2, 2, 2],
[3, 3, 3, 3, 3, 3, 3, 3, 3]])
>>> d = c.reshape(2,2,3,3)
>>> d
array([[[[0, 0, 0],
[0, 0, 0],
[0, 0, 0]],

[[1, 1, 1],
[1, 1, 1],
[1, 1, 1]]],


[[[2, 2, 2],
[2, 2, 2],
[2, 2, 2]],

[[3, 3, 3],
[3, 3, 3],
[3, 3, 3]]]])

所以GAP类完整实现及测试如下:

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# -*- coding: utf-8 -*-

# @Time : 19-6-21 上午11:18
# @Author : zj


import numpy as np
from nn.Layer import *

__all__ = ['GAP']


class GAP(Layer):
"""
global average pooling layer
全局平均池化层
"""

def __init__(self):
super(GAP, self).__init__()
self.input_shape = None

def __call__(self, inputs):
return self.forward(inputs)

def forward(self, inputs):
# input.shape == [N, C, H, W]
assert len(inputs.shape) == 4
N, C, H, W = inputs.shape[:4]

z = np.mean(inputs.reshape(N, C, -1), axis=2)
self.input_shape = inputs.shape

return z

def backward(self, grad_out):
N, C, H, W = self.input_shape[:4]
dz = grad_out.reshape(N * C, -1)
da = np.repeat(dz, H * W, axis=1)

return da.reshape(N, C, H, W)


if __name__ == '__main__':
gap = GAP()

inputs = np.arange(36).reshape(2, 2, 3, 3)
res = gap(inputs)
print(res)

grad_out = np.arange(4).reshape(2, 2)
da = gap.backward(grad_out)
print(da)

输出如下:

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[[ 4. 13.]
[22. 31.]]
[[[[0 0 0]
[0 0 0]
[0 0 0]]
[[1 1 1]
[1 1 1]
[1 1 1]]]
[[[2 2 2]
[2 2 2]
[2 2 2]]
[[3 3 3]
[3 3 3]
[3 3 3]]]]

NIN定义

假定输入数据体空间尺寸为$32\times 32$,输出类别为$10$

共有3MLPConv1GAP,其中每个MLPConv3MLP,完整实现如下:

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class NIN(Net):
"""
NIN网络
"""

def __init__(self, in_channels=1, out_channels=10, momentum=0, nesterov=False, p_h=1.0):
super(NIN, self).__init__()
self.conv1 = nn.Conv2d(in_channels, 5, 5, 192, stride=1, padding=2, momentum=momentum, nesterov=nesterov)
self.conv2 = nn.Conv2d(96, 5, 5, 192, stride=1, padding=2, momentum=momentum, nesterov=nesterov)
self.conv3 = nn.Conv2d(192, 3, 3, 192, stride=1, padding=1, momentum=momentum, nesterov=nesterov)

self.mlp1 = nn.Conv2d(192, 1, 1, 160, stride=1, padding=0, momentum=momentum, nesterov=nesterov)
self.mlp2 = nn.Conv2d(160, 1, 1, 96, stride=1, padding=0, momentum=momentum, nesterov=nesterov)

self.mlp2_1 = nn.Conv2d(192, 1, 1, 192, stride=1, padding=0, momentum=momentum, nesterov=nesterov)
self.mlp2_2 = nn.Conv2d(192, 1, 1, 192, stride=1, padding=0, momentum=momentum, nesterov=nesterov)

self.mlp3_1 = nn.Conv2d(192, 1, 1, 192, stride=1, padding=0, momentum=momentum, nesterov=nesterov)
self.mlp3_2 = nn.Conv2d(192, 1, 1, out_channels, stride=1, padding=0, momentum=momentum, nesterov=nesterov)

self.maxPool1 = nn.MaxPool(2, 2, 96, stride=2)
self.maxPool2 = nn.MaxPool(2, 2, 192, stride=2)

self.gap = nn.GAP()

self.relu1 = nn.ReLU()
self.relu2 = nn.ReLU()
self.relu3 = nn.ReLU()
self.relu4 = nn.ReLU()
self.relu5 = nn.ReLU()
self.relu6 = nn.ReLU()
self.relu7 = nn.ReLU()
self.relu8 = nn.ReLU()
self.relu9 = nn.ReLU()

self.dropout = nn.Dropout2d()

self.p_h = p_h
self.U1 = None
self.U2 = None

def __call__(self, inputs):
return self.forward(inputs)

def forward(self, inputs):
# inputs.shape = [N, C, H, W]
assert len(inputs.shape) == 4
x = self.relu1(self.conv1(inputs))
x = self.relu2(self.mlp1(x))
x = self.relu3(self.mlp2(x))
x = self.maxPool1(x)
self.U1 = self.dropout(x.shape, self.p_h)
x *= self.U1

x = self.relu4(self.conv2(x))
x = self.relu5(self.mlp2_1(x))
x = self.relu6(self.mlp2_2(x))
x = self.maxPool2(x)
self.U2 = self.dropout(x.shape, self.p_h)
x *= self.U2

x = self.relu7(self.conv3(x))
x = self.relu8(self.mlp3_1(x))
x = self.relu9(self.mlp3_2(x))

x = self.gap(x)
return x

def backward(self, grad_out):
# grad_out.shape = [N, C]
assert len(grad_out.shape) == 2
da11 = self.gap.backward(grad_out)

dz11 = self.relu9.backward(da11)
da10 = self.mlp3_2.backward(dz11)
dz10 = self.relu8.backward(da10)
da9 = self.mlp3_1.backward(dz10)
dz9 = self.relu7.backward(da9)
da8 = self.conv3.backward(dz9)

da8 *= self.U2
da7 = self.maxPool2.backward(da8)
dz7 = self.relu6.backward(da7)
da6 = self.mlp2_2.backward(dz7)
dz6 = self.relu5.backward(da6)
da5 = self.mlp2_1.backward(dz6)
dz5 = self.relu4.backward(da5)
da4 = self.conv2.backward(dz5)

da4 *= self.U1
da3 = self.maxPool1.backward(da4)
dz3 = self.relu3.backward(da3)
da2 = self.mlp2.backward(dz3)
dz2 = self.relu2.backward(da2)
da1 = self.mlp1.backward(dz2)
dz1 = self.relu1.backward(da1)
da0 = self.conv1.backward(dz1)

def update(self, lr=1e-3, reg=1e-3):
self.mlp3_2.update(learning_rate=lr, regularization_rate=reg)
self.mlp3_1.update(learning_rate=lr, regularization_rate=reg)
self.conv3.update(learning_rate=lr, regularization_rate=reg)

self.mlp2_2.update(learning_rate=lr, regularization_rate=reg)
self.mlp2_1.update(learning_rate=lr, regularization_rate=reg)
self.conv2.update(learning_rate=lr, regularization_rate=reg)

self.mlp2.update(learning_rate=lr, regularization_rate=reg)
self.mlp1.update(learning_rate=lr, regularization_rate=reg)
self.conv1.update(learning_rate=lr, regularization_rate=reg)

def predict(self, inputs):
# inputs.shape = [N, C, H, W]
assert len(inputs.shape) == 4
x = self.relu1(self.conv1(inputs))
x = self.relu2(self.mlp1(x))
x = self.relu3(self.mlp2(x))
x = self.maxPool1(x)

x = self.relu4(self.conv2(x))
x = self.relu5(self.mlp2_1(x))
x = self.relu6(self.mlp2_2(x))
x = self.maxPool2(x)

x = self.relu7(self.conv3(x))
x = self.relu8(self.mlp3_1(x))
x = self.relu9(self.mlp3_2(x))

x = self.gap(x)
return x

def get_params(self):
out = dict()
out['conv1'] = self.conv1.get_params()
out['conv2'] = self.conv2.get_params()
out['conv3'] = self.conv3.get_params()

out['mlp1'] = self.mlp1.get_params()
out['mlp2'] = self.mlp2.get_params()
out['mlp2_1'] = self.mlp2_1.get_params()
out['mlp2_2'] = self.mlp2_2.get_params()
out['mlp3_1'] = self.mlp3_1.get_params()
out['mlp3_2'] = self.mlp3_2.get_params()

out['p_h'] = self.p_h

return out

def set_params(self, params):
self.conv1.set_params(params['conv1'])
self.conv2.set_params(params['conv2'])
self.conv3.set_params(params['conv3'])

self.mlp1.set_params(params['mlp1'])
self.mlp2.set_params(params['mlp2'])
self.mlp2_1.set_params(params['mlp2_1'])
self.mlp2_2.set_params(params['mlp2_1'])
self.mlp3_1.set_params(params['mlp3_1'])
self.mlp3_2.set_params(params['mlp3_1'])

self.p_h = params.get('p_h', 1.0)

测试

测试代码如下:

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def nin_train():
x_train, x_test, y_train, y_test = vision.data.load_cifar10(data_path, shuffle=True)

# 标准化
x_train = x_train / 255.0 - 0.5
x_test = x_test / 255.0 - 0.5

net = models.nin(in_channels=3, p_h=p_h)
criterion = nn.CrossEntropyLoss()

accuracy = vision.Accuracy()

loss_list = []
train_list = []
test_list = []
best_train_accuracy = 0.995
best_test_accuracy = 0.995

range_list = np.arange(0, x_train.shape[0] - batch_size, step=batch_size)
for i in range(epochs):
total_loss = 0
num = 0
start = time.time()
for j in range_list:
data = x_train[j:j + batch_size]
labels = y_train[j:j + batch_size]

scores = net(data)
loss = criterion(scores, labels)
total_loss += loss
num += 1

grad_out = criterion.backward()
net.backward(grad_out)
net.update(lr=learning_rate, reg=reg)
end = time.time()
print('one epoch need time: %.3f' % (end - start))
print('epoch: %d loss: %f' % (i + 1, total_loss / num))
loss_list.append(total_loss / num)

if (i % 20) == 19:
# # 每隔20次降低学习率
# learning_rate *= 0.5

train_accuracy = accuracy.compute_v2(x_train, y_train, net, batch_size=batch_size)
test_accuracy = accuracy.compute_v2(x_test, y_test, net, batch_size=batch_size)
train_list.append(train_accuracy)
test_list.append(test_accuracy)

print(loss_list)
print(train_list)
print(test_list)
if train_accuracy > best_train_accuracy and test_accuracy > best_test_accuracy:
path = 'nin-epochs-%d.pkl' % (i + 1)
utils.save_params(net.get_params(), path=path)
break

draw = vision.Draw()
draw(loss_list, xlabel='迭代/20次')
draw.multi_plot((train_list, test_list), ('训练集', '测试集'), title='精度图', xlabel='迭代/20次', ylabel='精度值')

numpy实现的速度太慢,就看看如何实现的吧,不建议运行

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