时间：2021-07-01 10:21:17 帮助过：68人阅读
function async(generator) {
return new Promise(function(resolve, reject) {
var g = generator()
function next(val) {
var result = g.next(val)
var value = result.value
if (!result.done) {
value.then(next).catch(reject)
}
else {
resolve(value)
}
}
next()
})
}
IEnumerable> SomeAsyncMethod()
{
//blabla
yield return await( asyncMethod, context );
//blabla
yield return await( asyncMethod, context );
//blabla
}
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import math, random
# 需要安装的库：Numpy和Matplotlib，推荐直接Anaconda
fig, axes1 = plt.subplots()
# 设置坐标轴长度
axes1.set_ylim(0, 1.4)
axes1.set_xlim(0, 1*np.pi/0.01)
# 设置初始x、y数值数组
xdata = np.arange(0, 2*np.pi, 0.01)
ydata = np.sin(xdata)
# 获得线条
line, = axes1.plot(xdata)
# 毛刺倍率，从0开始增长，offset越大毛刺越大
offset = 0.0
#因为update的参数是调用函数data_gen,所以第一个默认参数不能是framenum
def update(data):
global offset
line.set_ydata(data)
return line,
# 每次生成10个随机数据
# 每次变化整幅图的话，yield一个整图就行了
def data_gen():
global offset
while True:
length = float(len(xdata))
for i in range(len(xdata)):
ydata[i]=math.sin(xdata[i])+0.2
if i>length/18.0 and i<(length*2.7/6.0):
ydata[i]+=offset*(random.random()-0.5)
offset += 0.05
#可以设置offset的最大值
if offset>=0.5:
offset=0.0
yield ydata
# 配置完毕，开始播放
ani = animation.FuncAnimation(fig, update, data_gen, interval=800, repeat=True)
plt.show()
def train(iter_funcs, dataset, batch_size=BATCH_SIZE):
"""Train the model with `dataset` with mini-batch training. Each
mini-batch has `batch_size` recordings.
"""
num_batches_train = dataset['num_examples_train'] // batch_size
num_batches_valid = dataset['num_examples_valid'] // batch_size
for epoch in itertools.count(1):
batch_train_losses = []
for b in range(num_batches_train):
batch_train_loss = iter_funcs['train'](b)
batch_train_losses.append(batch_train_loss)
avg_train_loss = np.mean(batch_train_losses)
batch_valid_losses = []
batch_valid_accuracies = []
for b in range(num_batches_valid):
batch_valid_loss, batch_valid_accuracy = iter_funcs['valid'](b)
batch_valid_losses.append(batch_valid_loss)
batch_valid_accuracies.append(batch_valid_accuracy)
avg_valid_loss = np.mean(batch_valid_losses)
avg_valid_accuracy = np.mean(batch_valid_accuracies)
yield {
'number': epoch,
'train_loss': avg_train_loss,
'valid_loss': avg_valid_loss,
'valid_accuracy': avg_valid_accuracy,
}