添加更多组合 ] # 实验结果记录 results = [] for params in hyperparameters: optimizer = optim.SGD(model.parameters(), **params) criterion = nn.MSELoss() # 模型训练(简化版) for epoch in range(10): # 假设训练10个epoch for inputs, targets in dataloader: optimizer.zero_grad() outputs = ...
https://ray.readthedocs.io/en/latest/raysgd/raysgd_pytorch.html#advanced-hyperparameter-tuning
# 定义模型model=...# 定义优化器optimizer=torch.optim.SGD(model.parameters(),lr=0.1)# 训练模型...
import matplotlib.pyplot as plt #hyper parameters LR = 0.01 BATCH_SIZE = 32 EPOCH = 12 #data x = torch.unsqueeze(torch.linspace(-1,1,1000),dim=1) y = x.pow(2) + 0.1*torch.normal(torch.zeros(*x.size())) torch_dataset = Data.TensorDataset(data_tensor=x, target_tensor=y) loader...
parameters -- python dictionary containing your updated parameters """ L = len(parameters) // 2 # number of layers in the neural networks # Update rule for each parameter for l in range(L): parameters['W' + str(l+1)] = parameters['W' + str(l+1)] - learning_rate * grads['dW...
问在R (Keras)的SGD中实现温暖的重新启动EN作为第一次实验，我的想法是让学习率从0.3开始，在每一...
parameters: grads['dW' + str(l)] = dWl grads['db' + str(l)] = dbl v -- python dictionary containing the current velocity: v['dW' + str(l)] = ... v['db' + str(l)] = ... beta -- the momentum hyperparameter, scalar learning_rate -- the learning rate, scalar Returns: ...
returnparameters 在上述代码中,我们传入含有权值和偏置的字典、梯度字段和更新的学习率作为参数,按照开头的公式编写权值更新代码,一个简单的多层网络的梯度下降算法就写出来了。 小批量梯度下降法 mini-batch Gradient Descent 在工业数据环境下,直接对大数据执行梯度下降法训练往往处理速度缓慢,这时候将训练集分割成小一点...
optimizer = optimizer_fn(net.parameters(), **optimizer_hyperparams) def eval_loss(): return loss(net(features).view(-1), labels).item() / 2 ls = [eval_loss()] data_iter = torch.utils.data.DataLoader( torch.utils.data.TensorDataset(features, labels), batch_size, shuffle=True) ...
An imaginary continuous gradient descent will smoothly move to the bottom of the well and end up withW=2. A stepwise gradient descent needs a hyperparameterTtelling it how much to move the parameters each step. Let's start with this at 1. ...