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 + lns2
labels = ["train acc", "val acc"]
plt.legend(lns, labels, loc=7)
plt.show()
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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)
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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