本博客详细介绍了使用深度学习预测共享单车骑行需求的项目流程，包括数据预处理、特征工程、模型搭建与训练，以及最终的预测结果展示。通过实际案例，深入理解深度学习在网络架构、超参数调整及模型优化方面的应用。
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# 解决pd中print中间省略的问题
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
# 读取数据
data_path = './Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
def f1():
print(rides.head())
print(rides.describe())
rides.info()
def f2(rides):
"""
# 一、季节、天气（分类变量）、月份、小时、星期几
都是分类变量，需要调整为哑变量。
"""
dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']
for each in dummy_fields:
dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)
rides = pd.concat([rides, dummies], axis=1)
"""
二、除了将 上面的原变量，还有以下变量需要删除，思考下why?
1、instant 记录索引号；
2、dteday 具体某天的日期号；
3、atemp 体感温度，和temp重复，故删除；
4、workingday 是否工作日，和weekday重复了，故删除；
"""
fields_to_drop = ['instant', 'dteday', 'season', 'weathersit',
'weekday', 'atemp', 'mnth', 'workingday', 'hr']
data = rides.drop(fields_to_drop, axis=1)
print(data.head())
# rides[:24 * 10].plot(x='dteday', y='cnt')
# plt.show()
return data
def f3(data):
"""
连续变量的数据标准化
注意:cnt 就是target
"""
quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']
# 将换算因子进行保存，以便在预测的时候还原数据。
scaled_features = {}
for each in quant_features:
mean, std = data[each].mean(), data[each].std()
scaled_features[each] = [mean, std]
data.loc[:, each] = (data[each] - mean) / std
return data, scaled_features
def f4(data):
"""
拆分数据集，拆分 特征 和 target
"""
# 保存最后21天 作为测试数据集
test_data = data[-21 * 24:]
# 移除最后21天数据，作为训练数据集
data = data[:-21 * 24]
# 将特征值 和 target进行拆分
target_fields = ['cnt', 'casual', 'registered']
features, targets = data.drop(target_fields, axis=1), data[target_fields]
test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]
return features, targets, test_features, test_targets
def f5(features , targets):
"""
使用训练数据集的后60天数据，作为验证数据集；在模型训练过程中进行验证模型的效果。
"""
train_features, train_targets = features[:-60 * 24], targets[:-60 * 24]
val_features, val_targets = features[-60 * 24:], targets[-60 * 24:]
return train_features, train_targets, val_features, val_targets
class NeuralNetworkMultiHidden(object):
def __init__(self, input_nodes, hidden_nodes1, hidden_nodes2, output_nodes, learning_rate,
keep_prob=0.75, batch_size=128):
"""
:param input_nodes: 输入的节点数量 （特征数量）
:param hidden_nodes: 隐藏层节点数量
:param output_nodes: 输出层节点数量
:param learning_rate:
"""
# Set number of nodes in input, hidden and output layers.
self.input_nodes = input_nodes
self.hidden_nodes1 = hidden_nodes1
self.hidden_nodes2 = hidden_nodes2
self.output_nodes = output_nodes
self.batch_size = batch_size
self.lr = learning_rate
self.keep_prob = keep_prob
# 初始化权重
self.weights_input_to_h1 = np.random.normal(
0.0, 1/self.input_nodes ** -0.5, size=(self.input_nodes, self.hidden_nodes1))
self.weights_h1_to_h2 = np.random.normal(
0.0, 1/self.hidden_nodes1 ** -0.5, size=(self.hidden_nodes1, self.hidden_nodes2))
self.weights_h2_to_output = np.random.normal(
0.0, 1/self.hidden_nodes2 ** -0.5, size=(self.hidden_nodes2, self.output_nodes))
self.lr = learning_rate
# TODO: 设置 self.activation_function 来部署 sigmoid 函数
self.activation_function = lambda x: 1/(1+np.exp(-x))
def get_batches(self, features, targets):
assert len(features) == len(targets)
for i in range(0, len(features), self.batch_size):
yield features[i: i+self.batch_size], targets[i: i+self.batch_size]
def drop_out(self, x):
"""
实现dropout函数
:param x:
:return:
"""
keep_prob = (np.random.rand(*x.shape) < self.keep_prob) / self.keep_prob
return keep_prob
def train_batch(self, features, targets):
"""
MBGD的实现
:param features:
:param targets:
:return:
"""
for batch_x, batch_y in self.get_batches(features, targets):
# 1、正向传播
# TODO: 隐藏层
h1_inputs = np.matmul(batch_x, self.weights_input_to_h1)
h1_outputs = self.activation_function(h1_inputs)
h1_outputs = h1_outputs * self.drop_out(h1_outputs)
h2_inputs = np.matmul(h1_outputs, self.weights_h1_to_h2)
h2_outputs = self.activation_function(h2_inputs)
h2_outputs = h2_outputs * self.drop_out(h2_outputs)
# TODO: 输出层
final_inputs = np.matmul(h2_outputs, self.weights_h2_to_output)
y_hat = final_inputs
# 二、反向传播
# 1\Output error
error = y_hat - batch_y.reshape([-1, 1]) # [batch, 1]
# 2: 计算隐藏层对误差error的贡献
output_error_term = error * 1
# 3、 计算 h2 to output权重的梯度
delta_h2_to_output = np.matmul(np.transpose(h2_outputs), output_error_term) / self.batch_size
# 4、h2 隐藏层误差项
h2_error_term = np.matmul(output_error_term, self.weights_h2_to_output.transpose()) * h2_outputs * (1-h2_outputs)
# 5\ h1 to h2权重的梯度值
delta_h1_to_h2 = np.matmul(np.transpose(h1_outputs), h2_error_term) / self.batch_size
# 6、求 h1隐藏层误差项
h1_error_term = np.matmul(h2_error_term, self.weights_h1_to_h2.transpose()) * h1_outputs*(1-h1_outputs)
# 7、求input to h1权重的梯度
delta_input_to_h1 = np.matmul(np.transpose(batch_x), h1_error_term) / self.batch_size
# 8执行梯度下降
self.weights_input_to_h1 -= delta_input_to_h1 * self.lr
self.weights_h1_to_h2 -= delta_h1_to_h2 * self.lr
self.weights_h2_to_output -= delta_h2_to_output * self.lr
def run(self, features):
'''
预测函数。
使用输入特征，执行1次正向传播，得到预测值
features: 1D array of feature values
'''
# 1、正向传播
# TODO: 隐藏层
h1_inputs = np.matmul(features.values, self.weights_input_to_h1)
h1_outputs = self.activation_function(h1_inputs)
h2_inputs = np.matmul(h1_outputs, self.weights_h1_to_h2)
h2_outputs = self.activation_function(h2_inputs)
# TODO: 输出层
final_inputs = np.matmul(h2_outputs, self.weights_h2_to_output)
y_hat = final_inputs
return y_hat
def MSE(y, Y):
return np.mean((y-Y)**2)
# 显示训练过程中的训练 和 验证损失
def show(losses):
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
_ = plt.ylim()
plt.show()
def test(network,scaled_features, test_features, test_targets, rides):
fig, ax = plt.subplots(figsize=(8, 4))
mean, std = scaled_features['cnt']
predictions = network.run(test_features).T * std + mean
ax.plot(predictions[0], label='Prediction')
ax.plot((test_targets['cnt'] * std + mean).values, label='Data')
ax.set_xlim(right=len(predictions))
ax.legend()
dates = pd.to_datetime(rides.iloc[test_features.index]['dteday'])
dates = dates.apply(lambda d: d.strftime('%b %d'))
ax.set_xticks(np.arange(len(dates))[12::24])
_ = ax.set_xticklabels(dates[12::24], rotation=45)
plt.show()
if __name__ == '__main__':
# f1()
data = f2(rides)
data, scaled_features = f3(data)
features, targets, test_features, test_targets = f4(data)
train_features, train_targets, val_features, val_targets = f5(features, targets)
# todo 设置超参数 ###
epochs = 3000 # 迭代次数
learning_rate = 0.1 # 学习率
hidden_nodes1 = 8 # 隐藏层节点数量，决定你模型的复杂度。
hidden_nodes2 = 7
output_nodes = 1 # 输出层的节点数量。
batch_size = 256
keep_prob = 0.8
input_nodes = train_features.shape[1]
network = NeuralNetworkMultiHidden(
input_nodes, hidden_nodes1, hidden_nodes2, output_nodes,
learning_rate, keep_prob=keep_prob, batch_size=batch_size)
losses = {'train': [], 'validation': []}
for epoch in range(epochs):
network.train_batch(train_features.values, train_targets['cnt'].values)
# 打印出训练过程
train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)
val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)
if epoch % 20 == 0:
print('训练迭代次数：{},训练损失:{} ,验证损失:'
'{}'.format(epoch, train_loss, val_loss))
losses['train'].append(train_loss)
losses['validation'].append(val_loss)
show(losses)
test(network, scaled_features, test_features, test_targets, rides)
D:\Anaconda\python.exe "D:\PyCharm\PyCharm 2018.2.2\helpers\pydev\pydevconsole.py" 54140 54141
import sys; print('Python %s on %s' % (sys.version, sys.platform))
sys.path.extend(['D:\\AI20\\HJZ', 'D:/AI20/HJZ'])
Python 3.6.5 |Anaconda, Inc.| (default, Mar 29 2018, 13:32:41) [MSC v.1900 64 bit (AMD64)]
Type 'copyright', 'credits' or 'license' for more information
IPython 6.4.0 -- An enhanced Interactive Python. Type '?' for help.
PyDev console: using IPython 6.4.0
Python 3.6.5 |Anaconda, Inc.| (default, Mar 29 2018, 13:32:41) [MSC v.1900 64 bit (AMD64)] on win32
runfile('D:/AI20/HJZ/04-深度学习/1-深度学习入门/深度学习项目/AI20_单车__小批量多隐藏层_答案.py', wdir='D:/AI20/HJZ/04-深度学习/1-深度学习入门/深度学习项目')
yr holiday temp hum windspeed casual registered cnt season_1 season_2 season_3 season_4 weathersit_1 weathersit_2 weathersit_3 weathersit_4 mnth_1 mnth_2 mnth_3 mnth_4 mnth_5 mnth_6 mnth_7 mnth_8 mnth_9 mnth_10 mnth_11 mnth_12 hr_0 hr_1 hr_2 hr_3 hr_4 hr_5 hr_6 hr_7 hr_8 hr_9 hr_10 hr_11 hr_12 hr_13 hr_14 hr_15 hr_16 hr_17 hr_18 hr_19 hr_20 hr_21 hr_22 hr_23 weekday_0 weekday_1 weekday_2 weekday_3 weekday_4 weekday_5 weekday_6
0 0 0 0.24 0.81 0.0 3 13 16 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
1 0 0 0.22 0.80 0.0 8 32 40 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
2 0 0 0.22 0.80 0.0 5 27 32 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
3 0 0 0.24 0.75 0.0 3 10 13 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
4 0 0 0.24 0.75 0.0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
训练迭代次数：0,训练损失:1.4049598343209766 ,验证损失:1.4134166663142764
训练迭代次数：20,训练损失:1.191033061166735 ,验证损失:1.2931418136345434
训练迭代次数：40,训练损失:1.1984009623831025 ,验证损失:1.2920684294502167
训练迭代次数：60,训练损失:1.2128614744725161 ,验证损失:1.2890902536743876
训练迭代次数：80,训练损失:1.1920010882383159 ,验证损失:1.2781289512063165
训练迭代次数：100,训练损失:1.2006819864036042 ,验证损失:1.2669214154589938
训练迭代次数：120,训练损失:1.201754896935542 ,验证损失:1.2543690660827465
训练迭代次数：140,训练损失:1.191243393043961 ,验证损失:1.2386061002917468
训练迭代次数：160,训练损失:1.1663420353206133 ,验证损失:1.2133403530787898
训练迭代次数：180,训练损失:1.1854967589152083 ,验证损失:1.2038260345349165
训练迭代次数：200,训练损失:1.1795672537011166 ,验证损失:1.1823249575128574
训练迭代次数：220,训练损失:1.1682780833044055 ,验证损失:1.1699543809357753
训练迭代次数：240,训练损失:1.1636495190832759 ,验证损失:1.153493583812518
训练迭代次数：260,训练损失:1.1683817941847439 ,验证损失:1.1530397929143232
训练迭代次数：280,训练损失:1.1704024692290609 ,验证损失:1.1463094551941384
训练迭代次数：300,训练损失:1.167836729294609 ,验证损失:1.1380138358655458
训练迭代次数：320,训练损失:1.152206893299751 ,验证损失:1.1268447929175738
训练迭代次数：340,训练损失:1.1398153088544771 ,验证损失:1.1141464353215857
训练迭代次数：360,训练损失:1.1399555109674233 ,验证损失:1.1173589548792964
训练迭代次数：380,训练损失:1.1339803999528923 ,验证损失:1.1058483596575561
训练迭代次数：400,训练损失:1.127499637809135 ,验证损失:1.0943699867988634
训练迭代次数：420,训练损失:1.130508289149735 ,验证损失:1.094848660480854
训练迭代次数：440,训练损失:1.1226987785233933 ,验证损失:1.0868869679815947
训练迭代次数：460,训练损失:1.1124556323539798 ,验证损失:1.0880393700260946
训练迭代次数：480,训练损失:1.0947664417341665 ,验证损失:1.0822969246723875
训练迭代次数：500,训练损失:1.102019574807323 ,验证损失:1.0785993278226347
训练迭代次数：520,训练损失:1.0756653061870864 ,验证损失:1.0739512911587645
训练迭代次数：540,训练损失:1.0848392163051497 ,验证损失:1.071863377072026
训练迭代次数：560,训练损失:1.0726029326883686 ,验证损失:1.0662775420905442
训练迭代次数：580,训练损失:1.0573534852202195 ,验证损失:1.0714235515825197
训练迭代次数：600,训练损失:1.0383465103374563 ,验证损失:1.061587838801731
训练迭代次数：620,训练损失:1.025923333984107 ,验证损失:1.0653288968309416
训练迭代次数：640,训练损失:1.0369456811847073 ,验证损失:1.0713373240243294
训练迭代次数：660,训练损失:1.0364001667139042 ,验证损失:1.0640935566218894
训练迭代次数：680,训练损失:1.0400004910395533 ,验证损失:1.065681599324164
训练迭代次数：700,训练损失:1.050995284262165 ,验证损失:1.0647670884860212
训练迭代次数：720,训练损失:1.018940240035332 ,验证损失:1.059287874059955
训练迭代次数：740,训练损失:1.0363819632576774 ,验证损失:1.066276341690848
训练迭代次数：760,训练损失:1.028426813397832 ,验证损失:1.0623961167773956
训练迭代次数：780,训练损失:1.0189745034921762 ,验证损失:1.0624564548702664
训练迭代次数：800,训练损失:1.0535661337931692 ,验证损失:1.0668193865406113
训练迭代次数：820,训练损失:1.0266509285859535 ,验证损失:1.068206513591057
训练迭代次数：840,训练损失:1.0331621655804455 ,验证损失:1.0726722273363603
训练迭代次数：860,训练损失:1.0251174045688107 ,验证损失:1.0654138978355179
训练迭代次数：880,训练损失:1.0304148623045695 ,验证损失:1.0656984457855712
训练迭代次数：900,训练损失:1.024423315282759 ,验证损失:1.0700745167114185
训练迭代次数：920,训练损失:1.032328343800837 ,验证损失:1.0624130680251542
训练迭代次数：940,训练损失:1.0283687484682102 ,验证损失:1.0773623767903766
训练迭代次数：960,训练损失:1.0134267116061493 ,验证损失:1.074257335227338
训练迭代次数：980,训练损失:1.0110943715100067 ,验证损失:1.0785358764065802
训练迭代次数：1000,训练损失:1.0137399554429194 ,验证损失:1.0891244094584875
训练迭代次数：1020,训练损失:0.9976091453098368 ,验证损失:1.1036601448902938
训练迭代次数：1040,训练损失:1.0025455852046654 ,验证损失:1.1078044097173811
训练迭代次数：1060,训练损失:0.9971201783057869 ,验证损失:1.1097782177478874
训练迭代次数：1080,训练损失:1.0077135024045787 ,验证损失:1.1171342596149176
训练迭代次数：1100,训练损失:0.989435521338311 ,验证损失:1.1163947695415941
训练迭代次数：1120,训练损失:1.0046778115903863 ,验证损失:1.116652012791937
训练迭代次数：1140,训练损失:0.9890504798273926 ,验证损失:1.116016089697368
训练迭代次数：1160,训练损失:0.9843516216949189 ,验证损失:1.1052671666140421
训练迭代次数：1180,训练损失:1.0064311979803187 ,验证损失:1.1049940415771407
训练迭代次数：1200,训练损失:0.998841749230865 ,验证损失:1.0985993017086981
训练迭代次数：1220,训练损失:0.9862143891130666 ,验证损失:1.0903739899913933
训练迭代次数：1240,训练损失:0.9810610274347947 ,验证损失:1.0885378134649732
训练迭代次数：1260,训练损失:0.9792300562416918 ,验证损失:1.077161582076293
训练迭代次数：1280,训练损失:0.9760627779270655 ,验证损失:1.0730553186056613
训练迭代次数：1300,训练损失:0.967982277710496 ,验证损失:1.0630035815717724
训练迭代次数：1320,训练损失:0.9717968522519344 ,验证损失:1.053997539353016
训练迭代次数：1340,训练损失:0.9743654642216101 ,验证损失:1.0558319503303484
训练迭代次数：1360,训练损失:0.9680533949739022 ,验证损失:1.0506383794413203
训练迭代次数：1380,训练损失:0.9709026169373867 ,验证损失:1.0532973030862018
训练迭代次数：1400,训练损失:0.974128474316653 ,验证损失:1.0498922921766818
训练迭代次数：1420,训练损失:0.9785149046759605 ,验证损失:1.0408913252615668
训练迭代次数：1440,训练损失:0.9537413724379682 ,验证损失:1.0381164051752785
训练迭代次数：1460,训练损失:0.965637541159986 ,验证损失:1.035939389690247
训练迭代次数：1480,训练损失:0.9795617178366891 ,验证损失:1.0330948779622118
训练迭代次数：1500,训练损失:0.9830199380134964 ,验证损失:1.023296422546772
训练迭代次数：1520,训练损失:0.9718277653587923 ,验证损失:1.02374833235857
训练迭代次数：1540,训练损失:0.9871744709311929 ,验证损失:1.0181642903151176
训练迭代次数：1560,训练损失:0.9645553513132961 ,验证损失:1.014631074593417
训练迭代次数：1580,训练损失:0.95745665627693 ,验证损失:1.011575426466816
训练迭代次数：1600,训练损失:0.9891756647749831 ,验证损失:1.0059939313593158
训练迭代次数：1620,训练损失:0.9736988068828757 ,验证损失:1.0091536385132531
训练迭代次数：1640,训练损失:0.9960204223543437 ,验证损失:1.0033607875054935
训练迭代次数：1660,训练损失:0.9854556298410954 ,验证损失:1.0002568588738163
训练迭代次数：1680,训练损失:0.9830104575963702 ,验证损失:1.0005752641230383
训练迭代次数：1700,训练损失:0.9937353633299877 ,验证损失:0.9981942073966672
训练迭代次数：1720,训练损失:0.9930644015954275 ,验证损失:0.994249323972036
训练迭代次数：1740,训练损失:0.9921303783625928 ,验证损失:0.9930527726071848
训练迭代次数：1760,训练损失:1.0012668782395846 ,验证损失:0.9936678586296049
训练迭代次数：1780,训练损失:1.0046846570351382 ,验证损失:0.9904557116907274
训练迭代次数：1800,训练损失:0.9753986592440668 ,验证损失:0.9858319854643595
训练迭代次数：1820,训练损失:0.9862383235682017 ,验证损失:0.9807620309667786
训练迭代次数：1840,训练损失:0.997360058261536 ,验证损失:0.9883861735630757
训练迭代次数：1860,训练损失:0.993343092045557 ,验证损失:0.9859186058806008
训练迭代次数：1880,训练损失:0.9996648339110443 ,验证损失:0.9836449063128594
训练迭代次数：1900,训练损失:0.9890779499154683 ,验证损失:0.988435019455184
训练迭代次数：1920,训练损失:0.9936278333526469 ,验证损失:0.9842544956102532
训练迭代次数：1940,训练损失:0.983567206487514 ,验证损失:0.9860559344032238
训练迭代次数：1960,训练损失:0.9886528359516243 ,验证损失:0.9860672916553115
训练迭代次数：1980,训练损失:1.0003637487457928 ,验证损失:0.987138283627506
训练迭代次数：2000,训练损失:1.0123710735183136 ,验证损失:0.9902226985469503
训练迭代次数：2020,训练损失:0.9904608835359283 ,验证损失:0.9854193298272445
训练迭代次数：2040,训练损失:1.0054704041102138 ,验证损失:0.9874762958896658
训练迭代次数：2060,训练损失:1.027581260124156 ,验证损失:0.991672992135386
训练迭代次数：2080,训练损失:0.989565406914043 ,验证损失:0.984916443161018
训练迭代次数：2100,训练损失:1.0084468607171728 ,验证损失:0.9886431169322287
训练迭代次数：2120,训练损失:0.9942951112223293 ,验证损失:0.9838803991380857
训练迭代次数：2140,训练损失:1.005621205659498 ,验证损失:0.9826214487037916
训练迭代次数：2160,训练损失:0.9970232280450297 ,验证损失:0.9811441899587124
训练迭代次数：2180,训练损失:1.0109055419085602 ,验证损失:0.9817208963062901
训练迭代次数：2200,训练损失:0.9998017953503151 ,验证损失:0.9802820253130723
训练迭代次数：2220,训练损失:0.9983370483596552 ,验证损失:0.979627235574009
训练迭代次数：2240,训练损失:1.0094413724929017 ,验证损失:0.9841338397252996
训练迭代次数：2260,训练损失:1.005974330859626 ,验证损失:0.9818251741135302
训练迭代次数：2280,训练损失:1.0142675598363247 ,验证损失:0.9837754496291757
训练迭代次数：2300,训练损失:1.0127720127438498 ,验证损失:0.9828429365857131
训练迭代次数：2320,训练损失:1.0104569904204563 ,验证损失:0.9800861853389657
训练迭代次数：2340,训练损失:1.0093547783740506 ,验证损失:0.977398843006523
训练迭代次数：2360,训练损失:1.0110058263991355 ,验证损失:0.9815249536367491
训练迭代次数：2380,训练损失:1.0157069324371437 ,验证损失:0.9824352086252541
训练迭代次数：2400,训练损失:1.0069441529695864 ,验证损失:0.9802546909219414
训练迭代次数：2420,训练损失:0.998629288979768 ,验证损失:0.9757994016194351
训练迭代次数：2440,训练损失:1.0264442542787664 ,验证损失:0.9789124302218697
训练迭代次数：2460,训练损失:0.9858533087585518 ,验证损失:0.9753708653306028
训练迭代次数：2480,训练损失:1.0149051626536327 ,验证损失:0.9792163908030725
训练迭代次数：2500,训练损失:1.0257094713748163 ,验证损失:0.9790813165266272
训练迭代次数：2520,训练损失:1.0064263925393622 ,验证损失:0.9716495650211158
训练迭代次数：2540,训练损失:1.0074456081753316 ,验证损失:0.9713535169777512
训练迭代次数：2560,训练损失:1.0045156753470388 ,验证损失:0.9722775864599293
训练迭代次数：2580,训练损失:1.0116777234135117 ,验证损失:0.9734898859818268
训练迭代次数：2600,训练损失:1.008474365142438 ,验证损失:0.9676925039978643
训练迭代次数：2620,训练损失:1.0155960298865223 ,验证损失:0.9713532357071608
训练迭代次数：2640,训练损失:1.042803244913576 ,验证损失:0.9750837335994059
训练迭代次数：2660,训练损失:1.027546771694612 ,验证损失:0.9724091714158932
训练迭代次数：2680,训练损失:1.0336935638889095 ,验证损失:0.9719282835539361
训练迭代次数：2700,训练损失:1.0092632145747278 ,验证损失:0.9717977290574962
训练迭代次数：2720,训练损失:1.0271627014773743 ,验证损失:0.9742688881038355
训练迭代次数：2740,训练损失:1.0137922202722167 ,验证损失:0.9750051808328831
训练迭代次数：2760,训练损失:1.0057584026865223 ,验证损失:0.9726964652883073
训练迭代次数：2780,训练损失:1.0033088589710482 ,验证损失:0.9700467223899546
训练迭代次数：2800,训练损失:1.006040099471596 ,验证损失:0.9738564921842755
训练迭代次数：2820,训练损失:1.011735558239328 ,验证损失:0.9783865020181645
训练迭代次数：2840,训练损失:0.9833877463044207 ,验证损失:0.9769258604374474
训练迭代次数：2860,训练损失:1.027951700601824 ,验证损失:0.9849864397762739
训练迭代次数：2880,训练损失:1.0417753524731448 ,验证损失:0.9869484745302265
训练迭代次数：2900,训练损失:1.0116816678915919 ,验证损失:0.9849301034760884
训练迭代次数：2920,训练损失:1.0111170503145037 ,验证损失:0.9858138725738876
训练迭代次数：2940,训练损失:1.0329653851686955 ,验证损失:0.9948427598844941
训练迭代次数：2960,训练损失:1.0149997393003969 ,验证损失:0.9955813486192334
训练迭代次数：2980,训练损失:1.0111855326181511 ,验证损失:0.9933412663413201
如果报错test:
maybe运行得是pythontest
pycharm，默认运行单元是pythontest
而不是python
解决pytest运行问题
扫一扫
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
