直接上代码：
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fig_acc2= np.zeros([n_epoch])for epoch in range(n_epoch): start_time = time.time() #training train_loss, train_acc, n_batch = 0, 0, 0 for x_train_a, y_train_a in minibatches(x_train, y_train, batch_size, shuffle=True): _,err,ac=sess.run([train_op,loss,acc], feed_dict={x: x_train_a, y_: y_train_a}) train_loss += err; train_acc += ac; n_batch += 1 summary_str = sess.run(merged_summary_op,feed_dict={x: x_train_a, y_: y_train_a}) summary_writer.add_summary(summary_str, epoch) print(" train loss: %f" % (np.sum(train_loss)/ n_batch)) print(" train acc: %f" % (np.sum(train_acc)/ n_batch)) fig_loss[epoch] = np.sum(train_loss)/ n_batch fig_acc1[epoch] = np.sum(train_acc) / n_batch #validation val_loss, val_acc, n_batch = 0, 0, 0 for x_val_a, y_val_a in minibatches(x_val, y_val, batch_size, shuffle=False): err, ac = sess.run([loss,acc], feed_dict={x: x_val_a, y_: y_val_a}) val_loss += err; val_acc += ac; n_batch += 1 print(" validation loss: %f" % (np.sum(val_loss)/ n_batch)) print(" validation acc: %f" % (np.sum(val_acc)/ n_batch)) fig_acc2[epoch] = np.sum(val_acc) / n_batch# 训练loss图fig, ax1 = plt.subplots()lns1 = ax1.plot(np.arange(n_epoch), fig_loss, label="Loss")ax1.set_xlabel('iteration')ax1.set_ylabel('training loss')# 训练和验证两种准确率曲线图放在一张图中fig2, ax2 = plt.subplots()ax3 = ax2.twinx()#由ax2图生成ax3图lns2 = ax2.plot(np.arange(n_epoch), fig_acc1, label="Loss")lns3 = ax3.plot(np.arange(n_epoch), fig_acc2, label="Loss")ax2.set_xlabel('iteration')ax2.set_ylabel('training acc')ax3.set_ylabel('val acc') # 合并图例lns = lns3 + lns2labels = ["train acc", "val acc"]plt.legend(lns, labels, loc=7)plt.show() |
结果：
补充知识：tensorflow2.x实时绘制训练时的损失和准确率
我就废话不多说了，大家还是直接看代码吧！
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sgd = SGD(lr=float(model_value[3]), decay=1e-6, momentum=0.9, nesterov=True)model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])# validation_split：0~1之间的浮点数，用来指定训练集的一定比例数据作为验证集history=model.fit(self.x_train, self.y_train, batch_size=self.batch_size, epochs=self.epoch_size, class_weight = 'auto', validation_split=0.1)# 绘制训练 & 验证的准确率值plt.plot(history.history['accuracy'])plt.plot(history.history['val_accuracy'])plt.title('Model accuracy')plt.ylabel('Accuracy')plt.xlabel('Epoch')plt.legend(['Train', 'Test'], loc='upper left')plt.show() # 绘制训练 & 验证的损失值plt.plot(history.history['loss'])plt.plot(history.history['val_loss'])plt.title('Model loss')plt.ylabel('Loss')plt.xlabel('Epoch')plt.legend(['Train', 'Test'], loc='upper left')plt.show()print("savemodel---------------")model.save(os.path.join(model_value[0],'model3_3.h5'))#输出损失和精确度score = model.evaluate(self.x_test, self.y_test, batch_size=self.batch_size) |
以上这篇在tensorflow下利用plt画论文中loss,acc等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服务器之家。
原文链接：https://blog.csdn.net/qq_40994943/article/details/86651941
