keras绘制acc和loss曲线图实例_keras画loss曲线

123 浏览量更新于2023-05-03 评论收藏 65KB PDF 举报

身份认证购VIP最低享 7 折!

领优惠券(最高得80元）

资源详情

资源评论

资源推荐

keras绘制绘制acc和和loss曲线图实例曲线图实例

我就废话不多说了，大家还是直接看代码吧！

#加载keras模块

from __future__ import print_function

import numpy as np

np.random.seed(1337) # for reproducibility

import keras

from keras.datasets import mnist

from keras.models import Sequential

from keras.layers.core import Dense, Dropout, Activation

from keras.optimizers import SGD, Adam, RMSprop

from keras.utils import np_utils

import matplotlib.pyplot as plt

%matplotlib inline

#写一个LossHistory类，保存loss和acc

class LossHistory(keras.callbacks.Callback):

def on_train_begin(self, logs={}):

self.losses = {'batch':[], 'epoch':[]}

self.accuracy = {'batch':[], 'epoch':[]}

self.val_loss = {'batch':[], 'epoch':[]}

self.val_acc = {'batch':[], 'epoch':[]}

def on_batch_end(self, batch, logs={}):

self.losses['batch'].append(logs.get('loss'))

self.accuracy['batch'].append(logs.get('acc'))

self.val_loss['batch'].append(logs.get('val_loss'))

self.val_acc['batch'].append(logs.get('val_acc'))

def on_epoch_end(self, batch, logs={}):

self.losses['epoch'].append(logs.get('loss'))

self.accuracy['epoch'].append(logs.get('acc'))

self.val_loss['epoch'].append(logs.get('val_loss'))

self.val_acc['epoch'].append(logs.get('val_acc'))

def loss_plot(self, loss_type):

iters = range(len(self.losses[loss_type]))

plt.figure()

# acc

plt.plot(iters, self.accuracy[loss_type], 'r', label='train acc')

# loss

plt.plot(iters, self.losses[loss_type], 'g', label='train loss')

if loss_type == 'epoch':

# val_acc

plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc')

# val_loss

plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss')

plt.grid(True)

plt.xlabel(loss_type)

plt.ylabel('acc-loss')

plt.legend(loc="upper right")

plt.show()

#变量初始化

batch_size = 128

nb_classes = 10

nb_epoch = 20

# the data, shuffled and split between train and test sets

(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train = X_train.reshape(60000, 784)

X_test = X_test.reshape(10000, 784)

X_train = X_train.astype('float32')

X_test = X_test.astype('float32')

X_train /= 255

本内容试读结束，登录后可阅读更多

下载后可阅读完整内容，剩余3页未读，立即下载

weixin_38622475

粉丝: 0
资源: 913

会员权益专享

keras绘制acc和loss曲线图实例

评论0

会员权益专享

最新资源

keras绘制acc和loss曲线图实例

评论0

keras2.0中文文档

Keras中文手册

keras2.0中文文档.pdf

Keras图像分类：通过实例学习图像分类技术

keras用train_on_batch训练怎么画loss曲线

keras绘制损失函数

tensorboard绘制loss曲线

在下面这段代码中加入画ｌｏｓｓ曲线和ａｃｃ曲线的代码

train loss 和val loss曲线怎么画

在keras怎么实现Focal Loss 代码

unet模型训练结果可视化acc与vail-loss曲线分析

keras绘制混淆矩阵

怎么在用keras训练模型的时候用matplotlib动态显示loss曲线？train loss和test loss都要有。

导入数据用模糊神经网络预测三分类并画出loss曲线，accuracy曲线和auc曲线python代码

keras实现resnet对四种天气分类，并绘制图像

利用tensorboard分别将train_acc和val_acc，train_loss和val_loss画在一张图上代码

lstm预测绘制loss图

tensorboard查看loss曲线

会员权益专享

最新资源