废话不多说，直接上代码，代码有注释，不懂得评论问博主即可
# -*- coding: utf-8 -*-import kerasfrom keras.models import Sequentialfrom keras.layers import Denseimport numpy as npimport matplotlib.pyplot as pltimport time# 输入训练数据 keras接收numpy数组类型的数据x = np.array([[0, 1, 0],[0, 0, 1],[1, 3, 2],[3, 2, 1]])y = np.array([0, 0, 1, 1]).T# 最简单的序贯模型，序贯模型是多个网络层的线性堆叠simple_model = Sequential()# dense层为全连接层# 第一层隐含层为全连接层 5个神经元 输入数据的维度为3simple_model.add(Dense(5, input_dim=3, activation='relu'))# 第二个隐含层 4个神经元simple_model.add(Dense(4, activation='relu'))# 输出层为1个神经元simple_model.add(Dense(1, activation='sigmoid'))# 编译模型,训练模型之前需要编译模型# 编译模型的三个参数：优化器、损失函数、指标列表pile(optimizer='sgd', loss='mean_squared_error', metrics=['accuracy'])class LossHistory(keras.callbacks.Callback):#函数开始时创建盛放loss与acc的容器def on_train_begin(self, logs={}):self.losses = {'batch': [], 'epoch': []}self.accuracy = {'batch': [], 'epoch': []}self.val_loss = {'batch': [], 'epoch': []}self.val_acc = {'batch': [], 'epoch': []}#按照batch来进行追加数据def on_batch_end(self, batch, logs={}):#每一个batch完成后向容器里面追加loss，accself.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'))#每五秒按照当前容器里的值来绘图if int(time.time()) % 5 == 0:self.draw_p(self.losses['batch'], 'loss', 'train_batch')self.draw_p(self.accuracy['batch'], 'acc', 'train_batch')self.draw_p(self.val_loss['batch'], 'loss', 'val_batch')self.draw_p(self.val_acc['batch'], 'acc', 'val_batch')def on_epoch_end(self, batch, logs={}):# 每一个epoch完成后向容器里面追加loss，accself.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'))# 每五秒按照当前容器里的值来绘图if int(time.time()) % 5 == 0:self.draw_p(self.losses['epoch'], 'loss', 'train_epoch')self.draw_p(self.accuracy['epoch'], 'acc', 'train_epoch')self.draw_p(self.val_loss['epoch'], 'loss', 'val_epoch')self.draw_p(self.val_acc['epoch'], 'acc', 'val_epoch')#绘图，这里把每一种曲线都单独绘图，若想把各种曲线绘制在一张图上的话可修改此方法def draw_p(self, lists, label, type):plt.figure()plt.plot(range(len(lists)), lists, 'r', label=label)plt.ylabel(label)plt.xlabel(type)plt.legend(loc="upper right")plt.savefig(type+'_'+label+'.jpg')#由于这里的绘图设置的是5s绘制一次，当训练结束后得到的图可能不是一个完整的训练过程（最后一次绘图结束，有训练了0-5秒的时间）#所以这里的方法会在整个训练结束以后调用def end_draw(self):self.draw_p(self.losses['batch'], 'loss', 'train_batch')self.draw_p(self.accuracy['batch'], 'acc', 'train_batch')self.draw_p(self.val_loss['batch'], 'loss', 'val_batch')self.draw_p(self.val_acc['batch'], 'acc', 'val_batch')self.draw_p(self.losses['epoch'], 'loss', 'train_epoch')self.draw_p(self.accuracy['epoch'], 'acc', 'train_epoch')self.draw_p(self.val_loss['epoch'], 'loss', 'val_epoch')self.draw_p(self.val_acc['epoch'], 'acc', 'val_epoch')logs_loss = LossHistory()# 训练网络 2000次# Keras以Numpy数组作为输入数据和标签的数据类型。训练模型一般使用fit函数simple_model.fit(x, y, epochs=20000, callbacks=[logs_loss])# 应用模型 进行预测y_ = simple_model.predict_classes(x[0:1])print("[0,1,0]的分类结果：" + str(y[0]))logs_loss.end_draw()
