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源码：# coding: utf-8

import sys, os

sys.path.append(os.pardir) # 为了导入父目录的文件而进行的设定

import numpy as np

import matplotlib.pyplot as plt

from dataset.mnist import load_mnist

from common.multi_layer_net import MultiLayerNet

from common.util import shuffle_dataset

from common.trainer import Trainer

(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True)

# 为了实现高速化，减少训练数据

x_train = x_train[:500]

t_train = t_train[:500]

# 分割验证数据

validation_rate = 0.20

validation_num = x_train.shape[0] * validation_rate

x_train, t_train = shuffle_dataset(x_train, t_train)

x_val = x_train[:validation_num]

x_val = int(x_val)

t_val = t_train[:validation_num]

t_val = int(t_val)

x_train = x_train[validation_num:]

allt_train = t_train[validation_num:]

def __train(lr, weight_decay, epocs=50):

network = MultiLayerNet(input_size=784, hidden_size_list=[100, 100, 100, 100, 100, 100],

output_size=10, weight_decay_lambda=weight_decay)

trainer = Trainer(network, x_train, t_train, x_val, t_val,

epochs=epocs, mini_batch_size=100,

optimizer='sgd', optimizer_param={'lr': lr}, verbose=False)

trainer.train()

return trainer.test_acc_list, trainer.train_acc_list

# 超参数的随机搜索======================================

optimization_trial = 100

results_val = {}

results_train = {}

for _ in range(optimization_trial):

# 指定搜索的超参数的范围===============

weight_decay = 10 ** np.random.uniform(-8, -4)

lr = 10 ** np.random.uniform(-6, -2)

# ================================================

val_acc_list, train_acc_list = __train(lr, weight_decay)

print("val acc:" + str(val_acc_list[-1]) + " | lr:" + str(lr) + ", weight decay:" + str(weight_decay))

key = "lr:" + str(lr) + ", weight decay:" + str(weight_decay)

results_val[key] = val_acc_list

results_train[key] = train_acc_list

# 绘制图形========================================================

print("=========== Hyper-Parameter Optimization Result ===========")

graph_draw_num = 20

col_num = 5

row_num = int(np.ceil(graph_draw_num / col_num))

i = 0

for key, val_acc_list in sorted(results_val.items(), key=lambda x:x[1][-1], reverse=True):

print("Best-" + str(i+1) + "(val acc:" + str(val_acc_list[-1]) + ") | " + key)

plt.subplot(row_num, col_num, i+1)

plt.title("Best-" + str(i+1))

plt.ylim(0.0, 1.0)

if i % 5: plt.yticks([])

plt.xticks([])

x = np.arange(len(val_acc_list))

plt.plot(x, val_acc_list)

plt.plot(x, results_train[key], "--")

i += 1

if i >= graph_draw_num:

break

plt.show()

以下为报错：

Reloaded modules: dataset, dataset.mnist, common, common.multi_layer_net, common.layers, common.functions, common.util, common.gradient, common.trainer, common.optimizer

Traceback (most recent call last):

File "", line 1, in

runfile('C:/Users/11/Desktop/python学习/190124.py', wdir='C:/Users/11/Desktop/python学习')

File "C:\ProgramData\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 704, in runfile

execfile(filename, namespace)

File "C:\ProgramData\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 108, in execfile

exec(compile(f.read(), filename, 'exec'), namespace)

File "C:/Users/11/Desktop/python学习/190124.py", line 29, in

x_val = x_train[:validation_num]

TypeError: slice indices must be integers or None or have an __index__ method

runfile('C:/Users/11/Desktop/python学习/190124.py', wdir='C:/Users/11/Desktop/python学习')

Reloaded modules: dataset, dataset.mnist, common, common.multi_layer_net, common.layers, common.functions, common.util, common.gradient, common.trainer, common.optimizer

Traceback (most recent call last):

File "", line 1, in

runfile('C:/Users/11/Desktop/python学习/190124.py', wdir='C:/Users/11/Desktop/python学习')

File "C:\ProgramData\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 704, in runfile

execfile(filename, namespace)

File "C:\ProgramData\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 108, in execfile

exec(compile(f.read(), filename, 'exec'), namespace)

File "C:/Users/11/Desktop/python学习/190124.py", line 29, in

x_val = x_train[:validation_num]

TypeError: slice indices must be integers or None or have an __index__ method