【一起学NLP】Chapter3-使用神经网络解决问题

使用神经网络解决问题

python 复制代码

import sys
sys.path.append('..') # 为了引入父目录的文件而进行的设定
from dataset import spiral
import matplotlib.pyplot as plt

x,t = spiral.load_data()
print('x',x.shape)# (300,2)
print('y',t.shape)# (300,3)

x (300, 2)
y (300, 3)

在上面的例子中，要从ch01目录的dataset目录引入spiral.py。因此，上面的代码通过sys.path.append（'...'）将父目录添加到了import的检索路径中。

python 复制代码

print(x)
print(t)

[[-0.00000000e+00  0.00000000e+00]
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 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
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 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]]

此时，x是输入数据， t是监督标签。观察x和t的形状，可知它们各自有300笔样本数据，其中x是二维数据，t是三维数据。另外，t是one-hot向量，对应的正确解标签的类标记为1，其余的标记为0。下面，我们把这些数据绘制在图上，结果如图1-31所示。

python 复制代码

# 绘制数据点
N = 100
CLS_NUM = 3
markers = ['o', 'x', '^']
for i in range(CLS_NUM):
    plt.scatter(x[i*N:(i+1)*N, 0], x[i*N:(i+1)*N, 1], s=40, marker=markers[i])
plt.show()

如图1-31所示，输入是二维数据，类别数是3。观察这个数据集可知，它不能被直线分割。因此，我们需要学习非线性的分割线。那么，我们的神经网络（具有使用非线性的sigmoid激活函数的隐藏层的神经网络）能否正确学习这种非线性模式呢？让我们实验一下。

Tip:数据集划分

因为这个实验相对简单，所以我们不把数据集分成训练数据、验证数据和测试数据。不过，实际任务中会将数据集分为训练数据和测试数据（以及验证数据）来进行学习和评估。

python 复制代码

import sys
sys.path.append('..')
import numpy as np
from common.layers import Affine,Sigmoid,SoftmaxWithLoss

class TwoLayerNet:
    def __init__(self,input_size,hidden_size,output_size):
        I,H,O = input_size,hidden_size,output_size
        # 初始化权重和偏置
        W1 = 0.01*np.random.randn(I,H)# (1,I)*(I,H)
        b1 = np.zeros(H)
        W2 = 0.01*np.random.randn(H,O)# (1,H)*(H,O)
        b2 = np.zeros(O)
        # 生成层
        self.layers = [
            Affine(W1,b1),
            Sigmoid(),
            Affine(W2,b2)
        ]
        # 损失函数
        self.loss_layer = SoftmaxWithLoss()
        # 将所有的权重和梯度整理到列表中
        self.params, self.grads = [],[]
        for layer in self.layers:
            self.params += layer.params
            self.grads += layer.grads

    def predict(self,x):
        for layer in self.layers:
            x = layer.forward(x)
        return x
    
    def forward(self,x,t):
        score = self.predict(x)
        loss = self.loss_layer.forward(score,t)
        return loss
    
    def backward(self,dout=1):
        dout = self.loss_layer.backward(dout)
        for layer in reversed(self.layers):# 反向传播
            dout = layer.backward(dout)
        return dout

初始化程序接收3个参数。input_size是输入层的神经元数，hidden_size是隐藏层的神经元数，output_size是输出层的神经元数。在内部实现中，首先用零向量（np.zeros（））初始化偏置，再用小的随机数（0.01 *np.random.randn（））初始化权重。通过将权重设成小的随机数，学习可以更容易地进行。接着，生成必要的层，并将它们整理到实例变量layers列表中。最后，将这个模型使用到的参数和梯度归纳在一起。

学习使用的代码

python 复制代码

# 学习使用的代码
import sys
sys.path.append('..')
import numpy as np
from common.optimizer import SGD
from dataset import spiral
import matplotlib.pyplot as plt
# from two_layer_net import TwoLayerNet

# 设定超参数
max_epoch = 300
batch_size = 30
hidden_size = 10
learning_rate = 1.0

# 读入数据，生成模型和优化器
x, t = spiral.load_data()
model = TwoLayerNet(input_size=2, hidden_size=hidden_size, output_size=3)
optimizer = SGD(lr=learning_rate)

# 学习用的变量
data_size = len(x)
max_iters = data_size // batch_size  # 每个epoch中的批次数
total_loss = 0
loss_count = 0
loss_list = []

for epoch in range(max_epoch):
    # 打乱数据
    idx = np.random.permutation(data_size)
    x = x[idx]  # data sample
    t = t[idx]  # data label

    for iters in range(max_iters):
        batch_x = x[iters * batch_size:(iters + 1) * batch_size]
        batch_t = t[iters * batch_size:(iters + 1) * batch_size]

        # 确保batch_x和batch_t都是二维数组
        if batch_x.ndim == 1:
            batch_x = batch_x.reshape(1, -1)  # 如果是1D数组，变成2D（1, 特征数）
        if batch_t.ndim == 1:
            batch_t = batch_t.reshape(1, -1)  # 如果是1D数组，变成2D（1, 类别数）

        loss = model.forward(batch_x, batch_t)
        model.backward()
        optimizer.update(model.params, model.grads)
        total_loss += loss
        loss_count += 1

    # 定期输出学习过程
    if (iters + 1) % 10 == 0:
        avg_loss = total_loss / loss_count
        print('|epoch %d | iter %d / %d | loss %.2f' % (epoch + 1, iters + 1, max_iters, avg_loss))
        loss_list.append(avg_loss)
        total_loss, loss_count = 0, 0

|epoch 1 | iter 10 / 10 | loss 1.13
|epoch 2 | iter 10 / 10 | loss 1.13
|epoch 3 | iter 10 / 10 | loss 1.12
|epoch 4 | iter 10 / 10 | loss 1.12
|epoch 5 | iter 10 / 10 | loss 1.11
|epoch 6 | iter 10 / 10 | loss 1.14
|epoch 7 | iter 10 / 10 | loss 1.16
|epoch 8 | iter 10 / 10 | loss 1.11
|epoch 9 | iter 10 / 10 | loss 1.12
|epoch 10 | iter 10 / 10 | loss 1.13
|epoch 11 | iter 10 / 10 | loss 1.12
|epoch 12 | iter 10 / 10 | loss 1.11
|epoch 13 | iter 10 / 10 | loss 1.09
|epoch 14 | iter 10 / 10 | loss 1.08
|epoch 15 | iter 10 / 10 | loss 1.04
|epoch 16 | iter 10 / 10 | loss 1.03
|epoch 17 | iter 10 / 10 | loss 0.96
|epoch 18 | iter 10 / 10 | loss 0.92
|epoch 19 | iter 10 / 10 | loss 0.92
|epoch 20 | iter 10 / 10 | loss 0.87
|epoch 21 | iter 10 / 10 | loss 0.85
|epoch 22 | iter 10 / 10 | loss 0.82
|epoch 23 | iter 10 / 10 | loss 0.79
|epoch 24 | iter 10 / 10 | loss 0.78
|epoch 25 | iter 10 / 10 | loss 0.82
|epoch 26 | iter 10 / 10 | loss 0.78
|epoch 27 | iter 10 / 10 | loss 0.76
|epoch 28 | iter 10 / 10 | loss 0.76
|epoch 29 | iter 10 / 10 | loss 0.78
|epoch 30 | iter 10 / 10 | loss 0.75
|epoch 31 | iter 10 / 10 | loss 0.78
|epoch 32 | iter 10 / 10 | loss 0.77
|epoch 33 | iter 10 / 10 | loss 0.77
|epoch 34 | iter 10 / 10 | loss 0.78
|epoch 35 | iter 10 / 10 | loss 0.75
|epoch 36 | iter 10 / 10 | loss 0.74
|epoch 37 | iter 10 / 10 | loss 0.76
|epoch 38 | iter 10 / 10 | loss 0.76
|epoch 39 | iter 10 / 10 | loss 0.73
|epoch 40 | iter 10 / 10 | loss 0.75
|epoch 41 | iter 10 / 10 | loss 0.76
|epoch 42 | iter 10 / 10 | loss 0.76
|epoch 43 | iter 10 / 10 | loss 0.76
|epoch 44 | iter 10 / 10 | loss 0.74
|epoch 45 | iter 10 / 10 | loss 0.75
|epoch 46 | iter 10 / 10 | loss 0.73
|epoch 47 | iter 10 / 10 | loss 0.72
|epoch 48 | iter 10 / 10 | loss 0.73
|epoch 49 | iter 10 / 10 | loss 0.72
|epoch 50 | iter 10 / 10 | loss 0.72
|epoch 51 | iter 10 / 10 | loss 0.72
|epoch 52 | iter 10 / 10 | loss 0.72
|epoch 53 | iter 10 / 10 | loss 0.74
|epoch 54 | iter 10 / 10 | loss 0.74
|epoch 55 | iter 10 / 10 | loss 0.72
|epoch 56 | iter 10 / 10 | loss 0.72
|epoch 57 | iter 10 / 10 | loss 0.71
|epoch 58 | iter 10 / 10 | loss 0.70
|epoch 59 | iter 10 / 10 | loss 0.72
|epoch 60 | iter 10 / 10 | loss 0.70
|epoch 61 | iter 10 / 10 | loss 0.71
|epoch 62 | iter 10 / 10 | loss 0.72
|epoch 63 | iter 10 / 10 | loss 0.70
|epoch 64 | iter 10 / 10 | loss 0.71
|epoch 65 | iter 10 / 10 | loss 0.73
|epoch 66 | iter 10 / 10 | loss 0.70
|epoch 67 | iter 10 / 10 | loss 0.71
|epoch 68 | iter 10 / 10 | loss 0.69
|epoch 69 | iter 10 / 10 | loss 0.70
|epoch 70 | iter 10 / 10 | loss 0.71
|epoch 71 | iter 10 / 10 | loss 0.68
|epoch 72 | iter 10 / 10 | loss 0.69
|epoch 73 | iter 10 / 10 | loss 0.67
|epoch 74 | iter 10 / 10 | loss 0.68
|epoch 75 | iter 10 / 10 | loss 0.67
|epoch 76 | iter 10 / 10 | loss 0.66
|epoch 77 | iter 10 / 10 | loss 0.69
|epoch 78 | iter 10 / 10 | loss 0.64
|epoch 79 | iter 10 / 10 | loss 0.68
|epoch 80 | iter 10 / 10 | loss 0.64
|epoch 81 | iter 10 / 10 | loss 0.64
|epoch 82 | iter 10 / 10 | loss 0.66
|epoch 83 | iter 10 / 10 | loss 0.62
|epoch 84 | iter 10 / 10 | loss 0.62
|epoch 85 | iter 10 / 10 | loss 0.61
|epoch 86 | iter 10 / 10 | loss 0.60
|epoch 87 | iter 10 / 10 | loss 0.60
|epoch 88 | iter 10 / 10 | loss 0.61
|epoch 89 | iter 10 / 10 | loss 0.59
|epoch 90 | iter 10 / 10 | loss 0.58
|epoch 91 | iter 10 / 10 | loss 0.56
|epoch 92 | iter 10 / 10 | loss 0.56
|epoch 93 | iter 10 / 10 | loss 0.54
|epoch 94 | iter 10 / 10 | loss 0.53
|epoch 95 | iter 10 / 10 | loss 0.53
|epoch 96 | iter 10 / 10 | loss 0.52
|epoch 97 | iter 10 / 10 | loss 0.51
|epoch 98 | iter 10 / 10 | loss 0.50
|epoch 99 | iter 10 / 10 | loss 0.48
|epoch 100 | iter 10 / 10 | loss 0.48
|epoch 101 | iter 10 / 10 | loss 0.46
|epoch 102 | iter 10 / 10 | loss 0.45
|epoch 103 | iter 10 / 10 | loss 0.45
|epoch 104 | iter 10 / 10 | loss 0.44
|epoch 105 | iter 10 / 10 | loss 0.44
|epoch 106 | iter 10 / 10 | loss 0.41
|epoch 107 | iter 10 / 10 | loss 0.40
|epoch 108 | iter 10 / 10 | loss 0.41
|epoch 109 | iter 10 / 10 | loss 0.40
|epoch 110 | iter 10 / 10 | loss 0.40
|epoch 111 | iter 10 / 10 | loss 0.38
|epoch 112 | iter 10 / 10 | loss 0.38
|epoch 113 | iter 10 / 10 | loss 0.36
|epoch 114 | iter 10 / 10 | loss 0.37
|epoch 115 | iter 10 / 10 | loss 0.35
|epoch 116 | iter 10 / 10 | loss 0.34
|epoch 117 | iter 10 / 10 | loss 0.34
|epoch 118 | iter 10 / 10 | loss 0.34
|epoch 119 | iter 10 / 10 | loss 0.33
|epoch 120 | iter 10 / 10 | loss 0.34
|epoch 121 | iter 10 / 10 | loss 0.32
|epoch 122 | iter 10 / 10 | loss 0.32
|epoch 123 | iter 10 / 10 | loss 0.31
|epoch 124 | iter 10 / 10 | loss 0.31
|epoch 125 | iter 10 / 10 | loss 0.30
|epoch 126 | iter 10 / 10 | loss 0.30
|epoch 127 | iter 10 / 10 | loss 0.28
|epoch 128 | iter 10 / 10 | loss 0.28
|epoch 129 | iter 10 / 10 | loss 0.28
|epoch 130 | iter 10 / 10 | loss 0.28
|epoch 131 | iter 10 / 10 | loss 0.27
|epoch 132 | iter 10 / 10 | loss 0.27
|epoch 133 | iter 10 / 10 | loss 0.27
|epoch 134 | iter 10 / 10 | loss 0.27
|epoch 135 | iter 10 / 10 | loss 0.27
|epoch 136 | iter 10 / 10 | loss 0.26
|epoch 137 | iter 10 / 10 | loss 0.26
|epoch 138 | iter 10 / 10 | loss 0.26
|epoch 139 | iter 10 / 10 | loss 0.25
|epoch 140 | iter 10 / 10 | loss 0.24
|epoch 141 | iter 10 / 10 | loss 0.24
|epoch 142 | iter 10 / 10 | loss 0.25
|epoch 143 | iter 10 / 10 | loss 0.24
|epoch 144 | iter 10 / 10 | loss 0.24
|epoch 145 | iter 10 / 10 | loss 0.23
|epoch 146 | iter 10 / 10 | loss 0.24
|epoch 147 | iter 10 / 10 | loss 0.23
|epoch 148 | iter 10 / 10 | loss 0.23
|epoch 149 | iter 10 / 10 | loss 0.22
|epoch 150 | iter 10 / 10 | loss 0.22
|epoch 151 | iter 10 / 10 | loss 0.22
|epoch 152 | iter 10 / 10 | loss 0.22
|epoch 153 | iter 10 / 10 | loss 0.22
|epoch 154 | iter 10 / 10 | loss 0.22
|epoch 155 | iter 10 / 10 | loss 0.22
|epoch 156 | iter 10 / 10 | loss 0.21
|epoch 157 | iter 10 / 10 | loss 0.21
|epoch 158 | iter 10 / 10 | loss 0.20
|epoch 159 | iter 10 / 10 | loss 0.21
|epoch 160 | iter 10 / 10 | loss 0.20
|epoch 161 | iter 10 / 10 | loss 0.20
|epoch 162 | iter 10 / 10 | loss 0.20
|epoch 163 | iter 10 / 10 | loss 0.21
|epoch 164 | iter 10 / 10 | loss 0.20
|epoch 165 | iter 10 / 10 | loss 0.20
|epoch 166 | iter 10 / 10 | loss 0.19
|epoch 167 | iter 10 / 10 | loss 0.19
|epoch 168 | iter 10 / 10 | loss 0.19
|epoch 169 | iter 10 / 10 | loss 0.19
|epoch 170 | iter 10 / 10 | loss 0.19
|epoch 171 | iter 10 / 10 | loss 0.19
|epoch 172 | iter 10 / 10 | loss 0.18
|epoch 173 | iter 10 / 10 | loss 0.18
|epoch 174 | iter 10 / 10 | loss 0.18
|epoch 175 | iter 10 / 10 | loss 0.18
|epoch 176 | iter 10 / 10 | loss 0.18
|epoch 177 | iter 10 / 10 | loss 0.18
|epoch 178 | iter 10 / 10 | loss 0.18
|epoch 179 | iter 10 / 10 | loss 0.17
|epoch 180 | iter 10 / 10 | loss 0.17
|epoch 181 | iter 10 / 10 | loss 0.18
|epoch 182 | iter 10 / 10 | loss 0.17
|epoch 183 | iter 10 / 10 | loss 0.18
|epoch 184 | iter 10 / 10 | loss 0.17
|epoch 185 | iter 10 / 10 | loss 0.17
|epoch 186 | iter 10 / 10 | loss 0.18
|epoch 187 | iter 10 / 10 | loss 0.17
|epoch 188 | iter 10 / 10 | loss 0.17
|epoch 189 | iter 10 / 10 | loss 0.17
|epoch 190 | iter 10 / 10 | loss 0.17
|epoch 191 | iter 10 / 10 | loss 0.16
|epoch 192 | iter 10 / 10 | loss 0.17
|epoch 193 | iter 10 / 10 | loss 0.16
|epoch 194 | iter 10 / 10 | loss 0.16
|epoch 195 | iter 10 / 10 | loss 0.16
|epoch 196 | iter 10 / 10 | loss 0.16
|epoch 197 | iter 10 / 10 | loss 0.16
|epoch 198 | iter 10 / 10 | loss 0.15
|epoch 199 | iter 10 / 10 | loss 0.16
|epoch 200 | iter 10 / 10 | loss 0.16
|epoch 201 | iter 10 / 10 | loss 0.15
|epoch 202 | iter 10 / 10 | loss 0.16
|epoch 203 | iter 10 / 10 | loss 0.16
|epoch 204 | iter 10 / 10 | loss 0.15
|epoch 205 | iter 10 / 10 | loss 0.16
|epoch 206 | iter 10 / 10 | loss 0.15
|epoch 207 | iter 10 / 10 | loss 0.15
|epoch 208 | iter 10 / 10 | loss 0.15
|epoch 209 | iter 10 / 10 | loss 0.15
|epoch 210 | iter 10 / 10 | loss 0.15
|epoch 211 | iter 10 / 10 | loss 0.15
|epoch 212 | iter 10 / 10 | loss 0.15
|epoch 213 | iter 10 / 10 | loss 0.15
|epoch 214 | iter 10 / 10 | loss 0.15
|epoch 215 | iter 10 / 10 | loss 0.15
|epoch 216 | iter 10 / 10 | loss 0.14
|epoch 217 | iter 10 / 10 | loss 0.14
|epoch 218 | iter 10 / 10 | loss 0.15
|epoch 219 | iter 10 / 10 | loss 0.14
|epoch 220 | iter 10 / 10 | loss 0.14
|epoch 221 | iter 10 / 10 | loss 0.14
|epoch 222 | iter 10 / 10 | loss 0.14
|epoch 223 | iter 10 / 10 | loss 0.14
|epoch 224 | iter 10 / 10 | loss 0.14
|epoch 225 | iter 10 / 10 | loss 0.14
|epoch 226 | iter 10 / 10 | loss 0.14
|epoch 227 | iter 10 / 10 | loss 0.14
|epoch 228 | iter 10 / 10 | loss 0.14
|epoch 229 | iter 10 / 10 | loss 0.13
|epoch 230 | iter 10 / 10 | loss 0.14
|epoch 231 | iter 10 / 10 | loss 0.13
|epoch 232 | iter 10 / 10 | loss 0.14
|epoch 233 | iter 10 / 10 | loss 0.13
|epoch 234 | iter 10 / 10 | loss 0.13
|epoch 235 | iter 10 / 10 | loss 0.13
|epoch 236 | iter 10 / 10 | loss 0.13
|epoch 237 | iter 10 / 10 | loss 0.14
|epoch 238 | iter 10 / 10 | loss 0.13
|epoch 239 | iter 10 / 10 | loss 0.13
|epoch 240 | iter 10 / 10 | loss 0.14
|epoch 241 | iter 10 / 10 | loss 0.13
|epoch 242 | iter 10 / 10 | loss 0.13
|epoch 243 | iter 10 / 10 | loss 0.13
|epoch 244 | iter 10 / 10 | loss 0.13
|epoch 245 | iter 10 / 10 | loss 0.13
|epoch 246 | iter 10 / 10 | loss 0.13
|epoch 247 | iter 10 / 10 | loss 0.13
|epoch 248 | iter 10 / 10 | loss 0.13
|epoch 249 | iter 10 / 10 | loss 0.13
|epoch 250 | iter 10 / 10 | loss 0.13
|epoch 251 | iter 10 / 10 | loss 0.13
|epoch 252 | iter 10 / 10 | loss 0.12
|epoch 253 | iter 10 / 10 | loss 0.12
|epoch 254 | iter 10 / 10 | loss 0.12
|epoch 255 | iter 10 / 10 | loss 0.12
|epoch 256 | iter 10 / 10 | loss 0.12
|epoch 257 | iter 10 / 10 | loss 0.12
|epoch 258 | iter 10 / 10 | loss 0.12
|epoch 259 | iter 10 / 10 | loss 0.13
|epoch 260 | iter 10 / 10 | loss 0.12
|epoch 261 | iter 10 / 10 | loss 0.13
|epoch 262 | iter 10 / 10 | loss 0.12
|epoch 263 | iter 10 / 10 | loss 0.12
|epoch 264 | iter 10 / 10 | loss 0.13
|epoch 265 | iter 10 / 10 | loss 0.12
|epoch 266 | iter 10 / 10 | loss 0.12
|epoch 267 | iter 10 / 10 | loss 0.12
|epoch 268 | iter 10 / 10 | loss 0.12
|epoch 269 | iter 10 / 10 | loss 0.11
|epoch 270 | iter 10 / 10 | loss 0.12
|epoch 271 | iter 10 / 10 | loss 0.12
|epoch 272 | iter 10 / 10 | loss 0.12
|epoch 273 | iter 10 / 10 | loss 0.12
|epoch 274 | iter 10 / 10 | loss 0.12
|epoch 275 | iter 10 / 10 | loss 0.11
|epoch 276 | iter 10 / 10 | loss 0.12
|epoch 277 | iter 10 / 10 | loss 0.12
|epoch 278 | iter 10 / 10 | loss 0.11
|epoch 279 | iter 10 / 10 | loss 0.11
|epoch 280 | iter 10 / 10 | loss 0.11
|epoch 281 | iter 10 / 10 | loss 0.11
|epoch 282 | iter 10 / 10 | loss 0.12
|epoch 283 | iter 10 / 10 | loss 0.11
|epoch 284 | iter 10 / 10 | loss 0.11
|epoch 285 | iter 10 / 10 | loss 0.11
|epoch 286 | iter 10 / 10 | loss 0.11
|epoch 287 | iter 10 / 10 | loss 0.11
|epoch 288 | iter 10 / 10 | loss 0.12
|epoch 289 | iter 10 / 10 | loss 0.11
|epoch 290 | iter 10 / 10 | loss 0.11
|epoch 291 | iter 10 / 10 | loss 0.11
|epoch 292 | iter 10 / 10 | loss 0.11
|epoch 293 | iter 10 / 10 | loss 0.11
|epoch 294 | iter 10 / 10 | loss 0.11
|epoch 295 | iter 10 / 10 | loss 0.12
|epoch 296 | iter 10 / 10 | loss 0.11
|epoch 297 | iter 10 / 10 | loss 0.12
|epoch 298 | iter 10 / 10 | loss 0.11
|epoch 299 | iter 10 / 10 | loss 0.11
|epoch 300 | iter 10 / 10 | loss 0.11

Tip:epoch

Epoch表示学习的单位。1个epoch相当于模型"看过"一遍所有的学习数据（遍历数据集）。这里我们进行300个epoch的学习。

Tip:数据打乱

在进行学习时，需要随机选择数据作为mini-batch。这里，我们以epoch为单位打乱数据，对于打乱后的数据，按顺序从头开始抽取数据。数据的打乱（准确地说，是数据索引的打乱）使用np.random.permutation（）方法。给定参数N，该方法可以返回0到N-1的随机序列，其实际的使用示例如下所示。

python 复制代码

import numpy as np
np.random.permutation(10)

array([5, 1, 8, 4, 9, 7, 0, 2, 6, 3])

python 复制代码

np.random.permutation(10)

array([3, 4, 2, 7, 8, 5, 6, 0, 9, 1])

像这样，调用np.random.permutation（）可以随机打乱数据的索引。

python 复制代码

loss_list

[1.1256062166823237,
 1.1255202354489933,
 1.1162613752115285,
 1.1162867078413503,
 1.1123000112951948,
 1.1384639824108038,
 1.1590961883070312,
 1.1086316143023154,
 1.1173305676924539,
 1.1287957712269248,
 1.1168438089353867,
 1.108338779101816,
 1.0876149200499459,
 1.076681386581935,
 1.0442376735950387,
 1.0345782626337772,
 0.9572932039643971,
 0.918385321087945,
 0.9241491096212101,
 0.8685139076509195,
 0.849380704784154,
 0.8171629191788113,
 0.7924414711357766,
 0.7826646392986113,
 0.8235432039035636,
 0.7754573601774306,
 0.7557857636797779,
 0.7644773546985875,
 0.783489908441849,
 0.7507895610696304,
 0.7773067036165259,
 0.7650839562418821,
 0.7727897179944694,
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python 复制代码

# 绘制损失曲线
plt.plot(loss_list)
plt.xlabel('Epoch (every 10 epochs)')
plt.ylabel('Loss')
plt.title('Loss Curve')
plt.grid()
plt.show()

Trainer类

如前所述，本书中有很多机会执行神经网络的学习。为此，就需要编写前面那样的学习用的代码。然而，每次都写相同的代码太无聊了，因此我们将进行学习的类作为Trainer类提供出来。Trainer类的内部实现和刚才的源代码几乎相同，只是添加了一些新的功能而已，我们在需要的时候再详细说明其用法。

这个类的初始化程序接收神经网络（模型）和优化器，具体如下所示。

python 复制代码

# model = TwoLayerNet(...)
# optimizer = SGD(lr=1.0)
# trainer = Trainer(model,optimizer)

然后，调用fit（）方法开始学习。fit（）方法的参数如表1-1所示。

另外，Trainer类有plot（）方法，它将fit（）方法记录的损失（准确地说，是按照eval_interval评价的平均损失）在图上画出来。使用Trainer类进行学习的代码如下所示

python 复制代码

import sys
sys.path.append('..')
from common.optimizer import SGD
from common.trainer import Trainer
from dataset import spiral
# from two_layer_net import TwoLayerNet

max_epoch = 300
batch_size = 30
hidden_size = 10
learning_rate = 1.0

x,t = spiral.load_data()

model = TwoLayerNet(input_size=2,hidden_size=hidden_size,output_size=3)

optimizer = SGD(lr=learning_rate)
trainer = Trainer(model,optimizer)
trainer.fit(x,t,max_epoch,batch_size,eval_interval=10)
trainer.plot()

| epoch 1 |  iter 1 / 10 | time 0[s] | loss 1.10
| epoch 2 |  iter 1 / 10 | time 0[s] | loss 1.12
| epoch 3 |  iter 1 / 10 | time 0[s] | loss 1.13
| epoch 4 |  iter 1 / 10 | time 0[s] | loss 1.12
| epoch 5 |  iter 1 / 10 | time 0[s] | loss 1.12
| epoch 6 |  iter 1 / 10 | time 0[s] | loss 1.10
| epoch 7 |  iter 1 / 10 | time 0[s] | loss 1.14
| epoch 8 |  iter 1 / 10 | time 0[s] | loss 1.16
| epoch 9 |  iter 1 / 10 | time 0[s] | loss 1.11
| epoch 10 |  iter 1 / 10 | time 0[s] | loss 1.12
| epoch 11 |  iter 1 / 10 | time 0[s] | loss 1.12
| epoch 12 |  iter 1 / 10 | time 0[s] | loss 1.12
| epoch 13 |  iter 1 / 10 | time 0[s] | loss 1.10
| epoch 14 |  iter 1 / 10 | time 0[s] | loss 1.09
| epoch 15 |  iter 1 / 10 | time 0[s] | loss 1.08
| epoch 16 |  iter 1 / 10 | time 0[s] | loss 1.04
| epoch 17 |  iter 1 / 10 | time 0[s] | loss 1.03
| epoch 18 |  iter 1 / 10 | time 0[s] | loss 0.94
| epoch 19 |  iter 1 / 10 | time 0[s] | loss 0.92
| epoch 20 |  iter 1 / 10 | time 0[s] | loss 0.92
| epoch 21 |  iter 1 / 10 | time 0[s] | loss 0.87
| epoch 22 |  iter 1 / 10 | time 0[s] | loss 0.85
| epoch 23 |  iter 1 / 10 | time 0[s] | loss 0.80
| epoch 24 |  iter 1 / 10 | time 0[s] | loss 0.79
| epoch 25 |  iter 1 / 10 | time 0[s] | loss 0.78
| epoch 26 |  iter 1 / 10 | time 0[s] | loss 0.83
| epoch 27 |  iter 1 / 10 | time 0[s] | loss 0.77
| epoch 28 |  iter 1 / 10 | time 0[s] | loss 0.76
| epoch 29 |  iter 1 / 10 | time 0[s] | loss 0.77
| epoch 30 |  iter 1 / 10 | time 0[s] | loss 0.76
| epoch 31 |  iter 1 / 10 | time 0[s] | loss 0.77
| epoch 32 |  iter 1 / 10 | time 0[s] | loss 0.75
| epoch 33 |  iter 1 / 10 | time 0[s] | loss 0.78
| epoch 34 |  iter 1 / 10 | time 0[s] | loss 0.77
| epoch 35 |  iter 1 / 10 | time 0[s] | loss 0.78
| epoch 36 |  iter 1 / 10 | time 0[s] | loss 0.74
| epoch 37 |  iter 1 / 10 | time 0[s] | loss 0.75
| epoch 38 |  iter 1 / 10 | time 0[s] | loss 0.77
| epoch 39 |  iter 1 / 10 | time 0[s] | loss 0.75
| epoch 40 |  iter 1 / 10 | time 0[s] | loss 0.73
| epoch 41 |  iter 1 / 10 | time 0[s] | loss 0.75
| epoch 42 |  iter 1 / 10 | time 0[s] | loss 0.76
| epoch 43 |  iter 1 / 10 | time 0[s] | loss 0.79
| epoch 44 |  iter 1 / 10 | time 0[s] | loss 0.74
| epoch 45 |  iter 1 / 10 | time 0[s] | loss 0.75
| epoch 46 |  iter 1 / 10 | time 0[s] | loss 0.73
| epoch 47 |  iter 1 / 10 | time 0[s] | loss 0.73
| epoch 48 |  iter 1 / 10 | time 0[s] | loss 0.73
| epoch 49 |  iter 1 / 10 | time 0[s] | loss 0.73
| epoch 50 |  iter 1 / 10 | time 0[s] | loss 0.72
| epoch 51 |  iter 1 / 10 | time 0[s] | loss 0.72
| epoch 52 |  iter 1 / 10 | time 0[s] | loss 0.72
| epoch 53 |  iter 1 / 10 | time 0[s] | loss 0.72
| epoch 54 |  iter 1 / 10 | time 0[s] | loss 0.74
| epoch 55 |  iter 1 / 10 | time 0[s] | loss 0.74
| epoch 56 |  iter 1 / 10 | time 0[s] | loss 0.73
| epoch 57 |  iter 1 / 10 | time 0[s] | loss 0.72
| epoch 58 |  iter 1 / 10 | time 0[s] | loss 0.69
| epoch 59 |  iter 1 / 10 | time 0[s] | loss 0.72
| epoch 60 |  iter 1 / 10 | time 0[s] | loss 0.70
| epoch 61 |  iter 1 / 10 | time 0[s] | loss 0.69
| epoch 62 |  iter 1 / 10 | time 0[s] | loss 0.71
| epoch 63 |  iter 1 / 10 | time 0[s] | loss 0.70
| epoch 64 |  iter 1 / 10 | time 0[s] | loss 0.71
| epoch 65 |  iter 1 / 10 | time 0[s] | loss 0.72
| epoch 66 |  iter 1 / 10 | time 0[s] | loss 0.71
| epoch 67 |  iter 1 / 10 | time 0[s] | loss 0.71
| epoch 68 |  iter 1 / 10 | time 0[s] | loss 0.71
| epoch 69 |  iter 1 / 10 | time 0[s] | loss 0.70
| epoch 70 |  iter 1 / 10 | time 0[s] | loss 0.68
| epoch 71 |  iter 1 / 10 | time 0[s] | loss 0.73
| epoch 72 |  iter 1 / 10 | time 0[s] | loss 0.66
| epoch 73 |  iter 1 / 10 | time 0[s] | loss 0.69
| epoch 74 |  iter 1 / 10 | time 0[s] | loss 0.66
| epoch 75 |  iter 1 / 10 | time 0[s] | loss 0.70
| epoch 76 |  iter 1 / 10 | time 0[s] | loss 0.65
| epoch 77 |  iter 1 / 10 | time 0[s] | loss 0.67
| epoch 78 |  iter 1 / 10 | time 0[s] | loss 0.70
| epoch 79 |  iter 1 / 10 | time 0[s] | loss 0.63
| epoch 80 |  iter 1 / 10 | time 0[s] | loss 0.66
| epoch 81 |  iter 1 / 10 | time 0[s] | loss 0.65
| epoch 82 |  iter 1 / 10 | time 0[s] | loss 0.66
| epoch 83 |  iter 1 / 10 | time 0[s] | loss 0.64
| epoch 84 |  iter 1 / 10 | time 0[s] | loss 0.62
| epoch 85 |  iter 1 / 10 | time 0[s] | loss 0.62
| epoch 86 |  iter 1 / 10 | time 0[s] | loss 0.63
| epoch 87 |  iter 1 / 10 | time 0[s] | loss 0.59
| epoch 88 |  iter 1 / 10 | time 0[s] | loss 0.58
| epoch 89 |  iter 1 / 10 | time 0[s] | loss 0.61
| epoch 90 |  iter 1 / 10 | time 0[s] | loss 0.59
| epoch 91 |  iter 1 / 10 | time 0[s] | loss 0.58
| epoch 92 |  iter 1 / 10 | time 0[s] | loss 0.57
| epoch 93 |  iter 1 / 10 | time 0[s] | loss 0.55
| epoch 94 |  iter 1 / 10 | time 0[s] | loss 0.54
| epoch 95 |  iter 1 / 10 | time 0[s] | loss 0.53
| epoch 96 |  iter 1 / 10 | time 0[s] | loss 0.54
| epoch 97 |  iter 1 / 10 | time 0[s] | loss 0.51
| epoch 98 |  iter 1 / 10 | time 0[s] | loss 0.51
| epoch 99 |  iter 1 / 10 | time 0[s] | loss 0.50
| epoch 100 |  iter 1 / 10 | time 0[s] | loss 0.47
| epoch 101 |  iter 1 / 10 | time 0[s] | loss 0.49
| epoch 102 |  iter 1 / 10 | time 0[s] | loss 0.46
| epoch 103 |  iter 1 / 10 | time 0[s] | loss 0.44
| epoch 104 |  iter 1 / 10 | time 0[s] | loss 0.47
| epoch 105 |  iter 1 / 10 | time 0[s] | loss 0.44
| epoch 106 |  iter 1 / 10 | time 0[s] | loss 0.43
| epoch 107 |  iter 1 / 10 | time 0[s] | loss 0.43
| epoch 108 |  iter 1 / 10 | time 0[s] | loss 0.39
| epoch 109 |  iter 1 / 10 | time 0[s] | loss 0.40
| epoch 110 |  iter 1 / 10 | time 0[s] | loss 0.41
| epoch 111 |  iter 1 / 10 | time 0[s] | loss 0.38
| epoch 112 |  iter 1 / 10 | time 0[s] | loss 0.38
| epoch 113 |  iter 1 / 10 | time 0[s] | loss 0.38
| epoch 114 |  iter 1 / 10 | time 0[s] | loss 0.37
| epoch 115 |  iter 1 / 10 | time 0[s] | loss 0.36
| epoch 116 |  iter 1 / 10 | time 0[s] | loss 0.34
| epoch 117 |  iter 1 / 10 | time 0[s] | loss 0.35
| epoch 118 |  iter 1 / 10 | time 0[s] | loss 0.33
| epoch 119 |  iter 1 / 10 | time 0[s] | loss 0.35
| epoch 120 |  iter 1 / 10 | time 0[s] | loss 0.33
| epoch 121 |  iter 1 / 10 | time 0[s] | loss 0.33
| epoch 122 |  iter 1 / 10 | time 0[s] | loss 0.32
| epoch 123 |  iter 1 / 10 | time 0[s] | loss 0.31
| epoch 124 |  iter 1 / 10 | time 0[s] | loss 0.31
| epoch 125 |  iter 1 / 10 | time 0[s] | loss 0.31
| epoch 126 |  iter 1 / 10 | time 0[s] | loss 0.30
| epoch 127 |  iter 1 / 10 | time 0[s] | loss 0.30
| epoch 128 |  iter 1 / 10 | time 0[s] | loss 0.27
| epoch 129 |  iter 1 / 10 | time 0[s] | loss 0.30
| epoch 130 |  iter 1 / 10 | time 0[s] | loss 0.28
| epoch 131 |  iter 1 / 10 | time 0[s] | loss 0.26
| epoch 132 |  iter 1 / 10 | time 0[s] | loss 0.27
| epoch 133 |  iter 1 / 10 | time 0[s] | loss 0.27
| epoch 134 |  iter 1 / 10 | time 0[s] | loss 0.28
| epoch 135 |  iter 1 / 10 | time 0[s] | loss 0.26
| epoch 136 |  iter 1 / 10 | time 0[s] | loss 0.28
| epoch 137 |  iter 1 / 10 | time 0[s] | loss 0.25
| epoch 138 |  iter 1 / 10 | time 0[s] | loss 0.26
| epoch 139 |  iter 1 / 10 | time 0[s] | loss 0.26
| epoch 140 |  iter 1 / 10 | time 0[s] | loss 0.26
| epoch 141 |  iter 1 / 10 | time 0[s] | loss 0.23
| epoch 142 |  iter 1 / 10 | time 0[s] | loss 0.23
| epoch 143 |  iter 1 / 10 | time 0[s] | loss 0.26
| epoch 144 |  iter 1 / 10 | time 0[s] | loss 0.23
| epoch 145 |  iter 1 / 10 | time 0[s] | loss 0.24
| epoch 146 |  iter 1 / 10 | time 0[s] | loss 0.24
| epoch 147 |  iter 1 / 10 | time 0[s] | loss 0.25
| epoch 148 |  iter 1 / 10 | time 0[s] | loss 0.21
| epoch 149 |  iter 1 / 10 | time 0[s] | loss 0.23
| epoch 150 |  iter 1 / 10 | time 0[s] | loss 0.22
| epoch 151 |  iter 1 / 10 | time 0[s] | loss 0.22
| epoch 152 |  iter 1 / 10 | time 0[s] | loss 0.23
| epoch 153 |  iter 1 / 10 | time 0[s] | loss 0.23
| epoch 154 |  iter 1 / 10 | time 0[s] | loss 0.20
| epoch 155 |  iter 1 / 10 | time 0[s] | loss 0.22
| epoch 156 |  iter 1 / 10 | time 0[s] | loss 0.21
| epoch 157 |  iter 1 / 10 | time 0[s] | loss 0.21
| epoch 158 |  iter 1 / 10 | time 0[s] | loss 0.20
| epoch 159 |  iter 1 / 10 | time 0[s] | loss 0.21
| epoch 160 |  iter 1 / 10 | time 0[s] | loss 0.20
| epoch 161 |  iter 1 / 10 | time 0[s] | loss 0.19
| epoch 162 |  iter 1 / 10 | time 0[s] | loss 0.22
| epoch 163 |  iter 1 / 10 | time 0[s] | loss 0.19
| epoch 164 |  iter 1 / 10 | time 0[s] | loss 0.21
| epoch 165 |  iter 1 / 10 | time 0[s] | loss 0.20
| epoch 166 |  iter 1 / 10 | time 0[s] | loss 0.20
| epoch 167 |  iter 1 / 10 | time 0[s] | loss 0.20
| epoch 168 |  iter 1 / 10 | time 0[s] | loss 0.19
| epoch 169 |  iter 1 / 10 | time 0[s] | loss 0.18
| epoch 170 |  iter 1 / 10 | time 0[s] | loss 0.19
| epoch 171 |  iter 1 / 10 | time 0[s] | loss 0.19
| epoch 172 |  iter 1 / 10 | time 0[s] | loss 0.20
| epoch 173 |  iter 1 / 10 | time 0[s] | loss 0.16
| epoch 174 |  iter 1 / 10 | time 0[s] | loss 0.20
| epoch 175 |  iter 1 / 10 | time 0[s] | loss 0.18
| epoch 176 |  iter 1 / 10 | time 0[s] | loss 0.17
| epoch 177 |  iter 1 / 10 | time 0[s] | loss 0.17
| epoch 178 |  iter 1 / 10 | time 0[s] | loss 0.17
| epoch 179 |  iter 1 / 10 | time 0[s] | loss 0.18
| epoch 180 |  iter 1 / 10 | time 0[s] | loss 0.19
| epoch 181 |  iter 1 / 10 | time 0[s] | loss 0.17
| epoch 182 |  iter 1 / 10 | time 0[s] | loss 0.18
| epoch 183 |  iter 1 / 10 | time 0[s] | loss 0.16
| epoch 184 |  iter 1 / 10 | time 0[s] | loss 0.18
| epoch 185 |  iter 1 / 10 | time 0[s] | loss 0.18
| epoch 186 |  iter 1 / 10 | time 0[s] | loss 0.17
| epoch 187 |  iter 1 / 10 | time 0[s] | loss 0.17
| epoch 188 |  iter 1 / 10 | time 0[s] | loss 0.18
| epoch 189 |  iter 1 / 10 | time 0[s] | loss 0.16
| epoch 190 |  iter 1 / 10 | time 0[s] | loss 0.16
| epoch 191 |  iter 1 / 10 | time 0[s] | loss 0.17
| epoch 192 |  iter 1 / 10 | time 0[s] | loss 0.17
| epoch 193 |  iter 1 / 10 | time 0[s] | loss 0.16
| epoch 194 |  iter 1 / 10 | time 0[s] | loss 0.16
| epoch 195 |  iter 1 / 10 | time 0[s] | loss 0.16
| epoch 196 |  iter 1 / 10 | time 0[s] | loss 0.17
| epoch 197 |  iter 1 / 10 | time 0[s] | loss 0.16
| epoch 198 |  iter 1 / 10 | time 0[s] | loss 0.17
| epoch 199 |  iter 1 / 10 | time 0[s] | loss 0.16
| epoch 200 |  iter 1 / 10 | time 0[s] | loss 0.14
| epoch 201 |  iter 1 / 10 | time 0[s] | loss 0.16
| epoch 202 |  iter 1 / 10 | time 0[s] | loss 0.16
| epoch 203 |  iter 1 / 10 | time 0[s] | loss 0.15
| epoch 204 |  iter 1 / 10 | time 0[s] | loss 0.16
| epoch 205 |  iter 1 / 10 | time 0[s] | loss 0.14
| epoch 206 |  iter 1 / 10 | time 0[s] | loss 0.16
| epoch 207 |  iter 1 / 10 | time 0[s] | loss 0.16
| epoch 208 |  iter 1 / 10 | time 0[s] | loss 0.14
| epoch 209 |  iter 1 / 10 | time 0[s] | loss 0.15
| epoch 210 |  iter 1 / 10 | time 0[s] | loss 0.16
| epoch 211 |  iter 1 / 10 | time 0[s] | loss 0.14
| epoch 212 |  iter 1 / 10 | time 0[s] | loss 0.15
| epoch 213 |  iter 1 / 10 | time 0[s] | loss 0.15
| epoch 214 |  iter 1 / 10 | time 0[s] | loss 0.15
| epoch 215 |  iter 1 / 10 | time 0[s] | loss 0.14
| epoch 216 |  iter 1 / 10 | time 0[s] | loss 0.14
| epoch 217 |  iter 1 / 10 | time 0[s] | loss 0.15
| epoch 218 |  iter 1 / 10 | time 0[s] | loss 0.14
| epoch 219 |  iter 1 / 10 | time 0[s] | loss 0.15
| epoch 220 |  iter 1 / 10 | time 0[s] | loss 0.15
| epoch 221 |  iter 1 / 10 | time 0[s] | loss 0.14
| epoch 222 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 223 |  iter 1 / 10 | time 0[s] | loss 0.15
| epoch 224 |  iter 1 / 10 | time 0[s] | loss 0.14
| epoch 225 |  iter 1 / 10 | time 0[s] | loss 0.16
| epoch 226 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 227 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 228 |  iter 1 / 10 | time 0[s] | loss 0.15
| epoch 229 |  iter 1 / 10 | time 0[s] | loss 0.14
| epoch 230 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 231 |  iter 1 / 10 | time 0[s] | loss 0.14
| epoch 232 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 233 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 234 |  iter 1 / 10 | time 0[s] | loss 0.14
| epoch 235 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 236 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 237 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 238 |  iter 1 / 10 | time 0[s] | loss 0.14
| epoch 239 |  iter 1 / 10 | time 0[s] | loss 0.14
| epoch 240 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 241 |  iter 1 / 10 | time 0[s] | loss 0.14
| epoch 242 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 243 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 244 |  iter 1 / 10 | time 0[s] | loss 0.14
| epoch 245 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 246 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 247 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 248 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 249 |  iter 1 / 10 | time 0[s] | loss 0.14
| epoch 250 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 251 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 252 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 253 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 254 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 255 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 256 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 257 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 258 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 259 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 260 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 261 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 262 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 263 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 264 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 265 |  iter 1 / 10 | time 0[s] | loss 0.14
| epoch 266 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 267 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 268 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 269 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 270 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 271 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 272 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 273 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 274 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 275 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 276 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 277 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 278 |  iter 1 / 10 | time 0[s] | loss 0.13
| epoch 279 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 280 |  iter 1 / 10 | time 0[s] | loss 0.10
| epoch 281 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 282 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 283 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 284 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 285 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 286 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 287 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 288 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 289 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 290 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 291 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 292 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 293 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 294 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 295 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 296 |  iter 1 / 10 | time 0[s] | loss 0.12
| epoch 297 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 298 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 299 |  iter 1 / 10 | time 0[s] | loss 0.11
| epoch 300 |  iter 1 / 10 | time 0[s] | loss 0.11

执行这段代码，会进行和之前一样的神经网络的学习。通过将之前展示的学习用的代码交给Trainer类负责，代码变简洁了。本书今后都将使用Trainer类进行学习。

Tip-高速化计算

安装NVIDIA Cuda管理器，并使用Cupy调用GPU进行告高速计算。

https://baijiahao.baidu.com/s?id=1781877547348944869\&wfr=spider\&for=pc