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_functionimport numpy as npnp.random.seed(1337) # for reproducibilityimport kerasfrom keras.datasets import mnistfrom keras.models import Sequentialfrom keras.layers.core import Dense, Dropout, Activationfrom keras.optimizers import SGD, Adam, RMSpropfrom keras.utils import np_utilsimport matplotlib.pyplot as plt%matplotlib inline#写一个LossHistory类，保存loss和accclass 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 = 10nb_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 /= 255X_test /= 255print(X_train.shape[0], 'train samples')print(X_test.shape[0], 'test samples')# convert class vectors to binary class matricesY_train = np_utils.to_categorical(y_train, nb_classes)Y_test = np_utils.to_categorical(y_test, nb_classes)#建立模型 使用Sequential（）model = Sequential()model.add(Dense(512, input_shape=(784,)))model.add(Activation('relu'))model.add(Dropout(0.2))model.add(Dense(512))model.add(Activation('relu'))model.add(Dropout(0.2))model.add(Dense(10))model.add(Activation('softmax'))#打印模型model.summary()#训练与评估#编译模型model.compile(loss='categorical_crossentropy', optimizer=RMSprop(), metrics=['accuracy'])#创建一个实例historyhistory = LossHistory()#迭代训练（注意这个地方要加入callbacks）model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, validation_data=(X_test, Y_test), callbacks=[history])#模型评估score = model.evaluate(X_test, Y_test, verbose=0)print('Test score:', score[0])print('Test accuracy:', score[1])#绘制acc-loss曲线history.loss_plot('epoch')

补充知识：keras中自定义验证集的性能评估（ROC,AUC）

在keras中自带的性能评估有准确性以及loss，当需要以auc作为评价验证集的好坏时，就得自己写个评价函数了：

from sklearn.metrics import roc_auc_scorefrom keras import backend as K# AUC for a binary classifierdef auc(y_true, y_pred): ptas = tf.stack([binary_PTA(y_true,y_pred,k) for k in np.linspace(0, 1, 1000)],axis=0) pfas = tf.stack([binary_PFA(y_true,y_pred,k) for k in np.linspace(0, 1, 1000)],axis=0) pfas = tf.concat([tf.ones((1,)) ,pfas],axis=0) binSizes = -(pfas[1:]-pfas[:-1]) s = ptas*binSizes return K.sum(s, axis=0)#------------------------------------------------------------------------------------# PFA, prob false alert for binary classifierdef binary_PFA(y_true, y_pred, threshold=K.variable(value=0.5)): y_pred = K.cast(y_pred >= threshold, 'float32') # N = total number of negative labels N = K.sum(1 - y_true) # FP = total number of false alerts, alerts from the negative class labels FP = K.sum(y_pred - y_pred * y_true) return FP/N#-----------------------------------------------------------------------------------# P_TA prob true alerts for binary classifierdef binary_PTA(y_true, y_pred, threshold=K.variable(value=0.5)): y_pred = K.cast(y_pred >= threshold, 'float32') # P = total number of positive labels P = K.sum(y_true) # TP = total number of correct alerts, alerts from the positive class labels TP = K.sum(y_pred * y_true) return TP/P #接着在模型的compile中设置metrics#如下例子，我用的是RNN做分类from keras.models import Sequentialfrom keras.layers import Dense, Dropoutimport kerasfrom keras.layers import GRUmodel = Sequential()model.add(keras.layers.core.Masking(mask_value=0., input_shape=(max_lenth, max_features))) #masking用于变长序列输入model.add(GRU(units=n_hidden_units,activation='selu',kernel_initializer='orthogonal', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=regularizers.l2(0.01), recurrent_regularizer=regularizers.l2(0.01), bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0.5, recurrent_dropout=0.0, implementation=1, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False)) model.add(Dropout(0.5))model.add(Dense(1, activation='sigmoid'))model.compile(loss='binary_crossentropy', optimizer='adam', metrics=[auc]) #写入自定义评价函数

接下来就自己作预测了...

方法二：

from sklearn.metrics import roc_auc_scoreimport kerasclass RocAucMetricCallback(keras.callbacks.Callback): def __init__(self, predict_batch_size=1024, include_on_batch=False): super(RocAucMetricCallback, self).__init__() self.predict_batch_size=predict_batch_size self.include_on_batch=include_on_batch def on_batch_begin(self, batch, logs={}): pass def on_batch_end(self, batch, logs={}): if(self.include_on_batch): logs['roc_auc_val']=float('-inf') if(self.validation_data): logs['roc_auc_val']=roc_auc_score(self.validation_data[1], self.model.predict(self.validation_data[0], batch_size=self.predict_batch_size)) def on_train_begin(self, logs={}): if not ('roc_auc_val' in self.params['metrics']): self.params['metrics'].append('roc_auc_val') def on_train_end(self, logs={}): pass def on_epoch_begin(self, epoch, logs={}): pass def on_epoch_end(self, epoch, logs={}): logs['roc_auc_val']=float('-inf') if(self.validation_data): logs['roc_auc_val']=roc_auc_score(self.validation_data[1], self.model.predict(self.validation_data[0], batch_size=self.predict_batch_size))import numpy as npimport tensorflow as tffrom keras.models import Sequentialfrom keras.layers import Dense, Dropoutfrom keras.layers import GRUimport kerasfrom keras.callbacks import EarlyStoppingfrom sklearn.metrics import roc_auc_scorefrom keras import metrics cb = [ my_callbacks.RocAucMetricCallback(), # include it before EarlyStopping! EarlyStopping(monitor='roc_auc_val',patience=300, verbose=2,mode='max')]model = Sequential()model.add(keras.layers.core.Masking(mask_value=0., input_shape=(max_lenth, max_features)))# model.add(Embedding(input_dim=max_features+1, output_dim=64,mask_zero=True))model.add(GRU(units=n_hidden_units,activation='selu',kernel_initializer='orthogonal', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=regularizers.l2(0.01), recurrent_regularizer=regularizers.l2(0.01), bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0.5, recurrent_dropout=0.0, implementation=1, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False)) #input_shape=(max_lenth, max_features),model.add(Dropout(0.5))model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=[auc]) #这里就可以写其他评估标准model.fit(x_train, y_train, batch_size=train_batch_size, epochs=training_iters, verbose=2, callbacks=cb,validation_split=0.2, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0)

亲测有效！

以上这篇keras绘制acc和loss曲线图实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。