tensorflow下利用plt画论文中loss，acc等曲线图

直接上代码：

fig_loss = np.zeros([n_epoch])
fig_acc1 = np.zeros([n_epoch])
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()

结果：

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