random.shuffle (data) for batch in get_batches (data ,batch_size =64): params_grad = evaluate_gradient(loss_function , batch , params) params = params - learning_rate * params_grad Momentum(动量) Momentum是一种有助于抑制SGD振荡并加快SGD向最小值收敛的方法。Momentum将过去时间的梯度向量添加...
params,grads):ifself.visNone:self.v={}forkey,valinparams.items():self.v[key]=np.zeros_like(val)forkeyinparams.keys():self.v[key]=self.momentum*self.v[key]-self.lr*grads[key]params[key]+=self.momentum*self.v[key]-self.lr*grads[key]...
can make use of highly optimized matrix optimizations common to state-of-the-art deep learning libraries that make computing the gradient w.r.t. a mini-batch very efficient 常用的 batch 大小: 50、100、128、256... 有的观点认为用 2 的幂次会更快。 伪代码如下: for i in range(number_epoch...
This project aims to build a deep learning compiler and optimizer infrastructure that can provide automatic scalability and efficiency optimization for distributed and local execution. Overall, this stack covers two types of general optimizations: fast distributed training over large-scale servers and effic...
Based on my read of Algorithm 1 in the paper, decreasing β1β1 and β2β2 of Adam will make the learning slower, so if training is going too fast, that could help. People using Adam might set β1β1 and β2β2 to high values (above 0.9) because they are...
deep-learning pytorch Share Follow asked Jun 8, 2020 at 11:25 Eric Kani 83944 gold badges1010 silver badges1717 bronze badges Add a comment 1 Answer Sorted by: 15 In contrast to model's state_dict, which saves learnable parameters, the optimizer's state_dict contains ...
I think that since the model is then set in eval mode, those two line should be useless, but something clearly happens, does this have something to do with the affine parameters of the batch norm layers? UPDATE: Ok I misunderstood something: eval mode does not block paramet...
# 定义步长调度器 scheduler = StepLR(optimizer, step_size=30, gamma=0.1) for epoch in range...
2.Layer-Specific Adaptive Learning Rates for Deep Networks 15年的这个LARS是 layer-specific 假设每层梯度的magnitude差不多,所以每层的local learning rate 相等,17年Large Batch training 提出的LARS是param-specific 每个参数有自己的loacal learning rate。
θ是我们此时要更新的parameter;α是我们的learning rate(步子迈多大);∇θJ(θ)是我们的loss function在θ上的偏导。我们会事先定义一个迭代次数 epoch,首先计算梯度向量∇θJ(θ),然后沿着梯度的方向更新参数 params。 缺点: 由于这种方法是在一次更新中,就对整个数据集计算梯度,所以计算起来非常慢,遇到很大...