Posted in Python onJune 15, 2020
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 X_test /= 255 print(X_train.shape[0], 'train samples') print(X_test.shape[0], 'test samples') # convert class vectors to binary class matrices Y_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history history = 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_score from keras import backend as K # AUC for a binary classifier def 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 classifier def 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 classifier def 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 Sequential from keras.layers import Dense, Dropout import keras from keras.layers import GRU model = 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_score import keras class 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 np import tensorflow as tf from keras.models import Sequential from keras.layers import Dense, Dropout from keras.layers import GRU import keras from keras.callbacks import EarlyStopping from sklearn.metrics import roc_auc_score from 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三水点靠木。
keras绘制acc和loss曲线图实例
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ninesun11声明:登载此文出于传递更多信息之目的,并不意味着赞同其观点或证实其描述。
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