目录
使用神经网络解决问题
python
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
print(x)
print(t)[[-0.00000000e+00 0.00000000e+00]
[-9.76986432e-04 9.95216044e-03]
[ 5.12668241e-03 1.93317647e-02]
[-3.86043324e-04 2.99975161e-02]
[ 1.42509650e-02 3.73752591e-02]
[ 9.41914082e-04 4.99911272e-02]
[ 2.25361319e-02 5.56068589e-02]
[ 6.52848904e-03 6.96948982e-02]
[ 2.50649535e-02 7.59720219e-02]
[ 2.03287580e-02 8.76740646e-02]
[ 5.98440862e-02 8.01166983e-02]
[ 6.19050693e-02 9.09272368e-02]
[ 3.22809763e-02 1.15576549e-01]
[ 8.28423530e-02 1.00185551e-01]
[ 1.09856959e-01 8.67839183e-02]
[ 9.33208222e-02 1.17436043e-01]
[ 7.82976217e-02 1.39533087e-01]
[ 1.23994559e-01 1.16298535e-01]
[ 8.06199110e-02 1.60936105e-01]
[ 1.39235917e-01 1.29280158e-01]
[ 1.53599653e-01 1.28090384e-01]
[ 1.38981638e-01 1.57429680e-01]
[ 1.89873275e-01 1.11122183e-01]
[ 1.41160729e-01 1.81586477e-01]
[ 1.50631465e-01 1.86842613e-01]
[ 1.71714639e-01 1.81697778e-01]
[ 2.10050449e-01 1.53227963e-01]
[ 2.32019716e-01 1.38082770e-01]
[ 2.31219125e-01 1.57916802e-01]
[ 2.51798226e-01 1.43866791e-01]
[ 2.59928236e-01 1.49790894e-01]
[ 3.03168799e-01 6.47200053e-02]
[ 2.99776174e-01 1.11956445e-01]
[ 2.71902926e-01 1.86999462e-01]
[ 3.39649101e-01 1.54430706e-02]
[ 3.41955960e-01 7.46064458e-02]
[ 3.56153446e-01 5.24854558e-02]
[ 3.69893826e-01 -8.86325970e-03]
[ 3.78749506e-01 3.08027885e-02]
[ 3.89323347e-01 -2.29636922e-02]
[ 3.97756477e-01 -4.23058548e-02]
[ 3.97790750e-01 -9.93102174e-02]
[ 4.19082447e-01 -2.77471188e-02]
[ 4.26755751e-01 -5.27212418e-02]
[ 4.27342016e-01 -1.04779774e-01]
[ 4.37333586e-01 -1.06015726e-01]
[ 4.54778403e-01 -6.91129800e-02]
[ 4.33582415e-01 -1.81400909e-01]
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[ 4.24040623e-01 -3.50270681e-01]
[ 2.80512809e-01 -4.84677794e-01]
[ 5.30623098e-01 -2.08180518e-01]
[ 4.11038907e-01 -4.09202904e-01]
[ 4.62137267e-01 -3.66782151e-01]
[ 3.95281189e-01 -4.51389834e-01]
[ 3.67613445e-01 -4.86785738e-01]
[ 4.94556678e-01 -3.73916691e-01]
[ 4.35611632e-01 -4.55129109e-01]
[ 3.28234290e-01 -5.49419922e-01]
[ 2.78014129e-01 -5.87544164e-01]
[ 3.04441838e-01 -5.85589590e-01]
[-3.84353415e-02 -6.68896647e-01]
[ 4.32663510e-01 -5.24597262e-01]
[ 3.67995385e-01 -5.83677477e-01]
[ 1.62513204e-01 -6.80874040e-01]
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[ 5.02643212e-01 4.57984499e-01]
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[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
# 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
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
# 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 0.11Tip: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
import numpy as np
np.random.permutation(10)array([5, 1, 8, 4, 9, 7, 0, 2, 6, 3])
python
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
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,
0.7819402998382252,
0.7479802970891092,
0.7449918634368045,
0.7560347486336814,
0.762136567723541,
0.7308895411004578,
0.7530268898576871,
0.7598416342022494,
0.7594443798911804,
0.7609245612331736,
0.7385235003122192,
0.7483287079215573,
0.732212322256757,
0.7226947264484566,
0.7329453633807874,
0.7228591003722222,
0.7225109819294906,
0.7151355271138069,
0.7195462325887142,
0.7375188235491987,
0.7361580823220941,
0.7224648808897995,
0.7182891638960814,
0.7074840271194414,
0.7004036126944774,
0.7172821745385198,
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python
# 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
# 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
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