gwx
parent
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55e1ac7653
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{}
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{
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"attachmentFolderPath": "./"
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}
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import matplotlib.pyplot as plt
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import numpy as np
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import time
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from models import MLP
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import torch
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from torch import nn
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import mpldatacursor
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time_point = 1
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low=0.82
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high=0.83
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model = MLP.MLP()
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model.load_state_dict(torch.load('lstm_model.pt'))
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# 创建初始的折线图数据
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learning_rate = 0.01
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# learning_rate=1
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criterion = nn.MSELoss()
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# criterion=nn.L1Loss()
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optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
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x = np.array([1])
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y = np.array([model.fc0.weight.tolist()[0][0]])
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# 绘制初始的折线图
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fig, ax = plt.subplots()
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line, = ax.plot(x, y, '-o')
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# 显示初始的折线图
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plt.show(block=False)
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# scatter = ax.scatter(x, y)
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# # 定义标注文本
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# def formatter(**kwargs):
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# x, y = kwargs['x'], kwargs['y']
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# return f"x={x:.2f}\ny={y:.2f}"
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# # 配置悬浮窗
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# cursor = mpldatacursor.datacursor(
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# scatter,
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# formatter=formatter,
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# display='multiple',
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# draggable=True,
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# bbox=dict(fc='white', alpha=0.9),
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# arrowprops=dict(arrowstyle='->', connectionstyle='arc3'),
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# )
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# 定义自定义事件类型
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class AddPointEvent:
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def __init__(self, x, y):
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self.x = x
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self.y = y
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# 定义事件处理函数，用于处理自定义事件
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def on_add_point(event):
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# 添加新的点
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print(f"x:{event.x},y:{event.y}")
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new_x = np.array([event.x])
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new_y = np.array([event.y])
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# 使用set_xdata()和set_ydata()方法更新折线图的数据
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line.set_xdata(np.append(line.get_xdata(), new_x))
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line.set_ydata(np.append(line.get_ydata(), new_y))
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# 更新折线图的坐标轴范围
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ax.relim()
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ax.autoscale_view()
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ax.annotate("hi", xy=(event.x, event.y), xytext=(event.x, event.y+0.5), ha='center', va='bottom')
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# 更新折线图
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fig.canvas.draw()
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# 连接自定义事件到事件处理函数上
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cid = fig.canvas.mpl_connect(AddPointEvent, on_add_point)
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# 定义计时器回调函数，用于触发自定义事件
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def timer_callback():
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# 生成一个随机的点，并触发自定义事件
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global time_point
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time_point=time_point+1
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y=np.random.uniform(low=50, high=101)
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y_hat = y*np.random.uniform(low=low, high=high)
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batch_sequences = np.array([[y,y_hat]])
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# print(batch_sequences.shape)
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inputs = torch.tensor(batch_sequences[:, :-1], dtype=torch.float32)
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targets = torch.tensor(batch_sequences[:,-1], dtype=torch.float32)
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# 前向传播
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outputs = model(inputs)
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# print(f"targets {targets}")
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# print(f"input:{inputs}")
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# print(f"outputs {outputs}")
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outputs=outputs.reshape(targets.shape)
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# print(outputs.shape)
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# print(f"output:{outputs.shape}")
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# print(f"target:{targets.shape}")
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loss = criterion(outputs, targets)
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# print(f"loss:{loss}")
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# loss=loss*1000
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# 反向传播和优化
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loss.backward()
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optimizer.step()
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optimizer.zero_grad()
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new_y = model.fc0.weight.tolist()[0][0]
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new_event = AddPointEvent(time_point, new_y)
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fig.canvas.callbacks.process(AddPointEvent, new_event)
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# 重新启动计时器
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timer = fig.canvas.new_timer(interval=1000)
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timer.add_callback(timer_callback)
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timer.start()
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# 启动计时器
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timer = fig.canvas.new_timer(interval=1000)
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timer.add_callback(timer_callback)
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timer.start()
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def on_hover(event):
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# 检查是否有Artist在鼠标位置
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if event.inaxes == ax:
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# 获取鼠标位置
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x, y = event.xdata, event.ydata
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# 显示自定义信息
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ax.annotate(f'({x:.2f}, {y:.2f})', (x, y))
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# 将on_hover函数绑定到Figure的鼠标移动事件上
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# fig.canvas.mpl_connect('motion_notify_event', on_hover)
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# 显示动态更新的折线图，并保持程序运行
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plt.show()
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import torch
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import torch.nn as nn
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import pandas as pd
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import numpy as np
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from sklearn.preprocessing import MinMaxScaler
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input_size = 1
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hidden_size = 1
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num_layers = 2
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output_size = 1
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class MLP(nn.Module):
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def __init__(self, input_size=1, hidden_size=1, num_layers=2, output_size=1):
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super(MLP, self).__init__()
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# self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
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# self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
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# self.lstm=nn.RNN(input_size, hidden_size, num_layers, batch_first=True)
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self.fc0=nn.Linear(input_size,hidden_size,bias=False)
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self.relu = nn.ReLU()
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self.fc = nn.Linear(hidden_size, output_size)
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nn.init.constant_(self.fc0.weight,0.78)
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nn.init.xavier_uniform_(self.fc.weight)
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nn.init.zeros_(self.fc.bias)
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def forward(self, x):
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# out, _ = self.lstm(x)
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out0=self.fc0(x)
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# out1=self.relu(out0)
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# print(f"out{out[0]}")
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# print(f"out{out[-1]}")
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# out2 = self.fc(out1)
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return out0
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