PyTorch教程

from torch.utils.from torch.utils.xy = np.loadtxt(filepath, delimiter=',', dtype=np.self.len = xy.self.x_data = torch.self.y_data = torch.re...

import matplotlib.xy = np.loadtxt('diabetes.csv.gz', delimiter=',', dtype=np.x_data = torch.y_data = torch.class Model(torch.nn.super(Model,...

# import torch.nn.x_data = torch.Tensor([[1.0], [2.0], [3.y_data = torch.class LogisticRegressionModel(torch.nn.super(LogisticRegressionMode...

x_data = torch.Tensor([[1.0], [2.0], [3.y_data = torch.Tensor([[2.0], [4.0], [6.class LinearModel(torch.nn.super(LinearModel, self).# 该线性层需要...

x_data = [1.0, 2.0, 3.y_data = [2.0, 4.0, 6.w = torch.Tensor([1.0]) # w的初值为1.w.print("predict (before training)", 4, forward(4).l.print('\tg...

import matplotlib.x_data = [1.0, 2.0, 3.y_data = [2.0, 4.0, 6.w = 1.w -= 0.01 * grad_val # 0.epoch_list.cost_list.plt.plt.plt.plt.随机梯度下降法在神经...

import matplotlib.x_data = [1.0, 2.0, 3.y_data = [2.0, 4.0, 6.for w in np.arange(0.0, 4.1, 0.w_list.mse_list.plt.plt.plt.plt.作业题目：实现线性模型（y=w...