Deep Learning is, to a large extent, about solving massive, nasty optimization problems. A Neural Network is merely a very complicated function, consisting of millions of parameters, that represents a mathematical solution to a problem. Consider the task of image classification. AlexNet is a mathem...
We had a two-sided saturation in the sigmoid functions. That is the activation function would saturate in both the positive and the negative direction. In contrast, ReLUs provide one-sided saturations. Though it is not exactly precise to call the zero part of a ReLU a saturation. However, ...
Learning as an Optimization Problem: 一般而言,we aim to minimize a loss function, which is typically the average of individual loss (or so) functions associated with each data point. Challenges in Deep Learning Optimization: Large-scale data High-dimensional Parameter Space Non-convexity Mysteries...
为了可以apply到optimality gap的RHS,w^*是最优参数,gt是wt处loss function的梯度。 (简单)推理: (中间项相互消去) 想象,可以在该不等式左右除以T. 考虑额外假设:G-Lipschitz的loss function 这时,我们知道Lemma1成立,即确定在某一训练轮次,有optimality gap<=RHS,但这个RHS的变量(比如学习率)比较多、形式还...
9、【李宏毅机器学习(2017)】Tips for Deep Learning(深度学习优化) 在上一篇博客中介绍了Keras,并使用Keras训练数据进行预测,得到的效果并不理想,接下来将以此为基础优化模型,提高预测的精度。 目录 误差分析 模型误差原因分析 模型优化方案 New activation function Vanishing Gradient Problem ReLU Maxout Maxout介绍...
activation_layer = {'sigmoid': Sigmoid, 'relu': Relu}38self.layers = OrderedDict()39for idx in range(1, self.hidden_layer_num+1):40self.layers['Affine' + str(idx)] = Affine(self.params['W' + str(idx)],41self.params['b' + str(idx)])42self.layers['Activation_function' + str...
1. Deep learning 1.1 Step 1:define a set of function Define 一个function,实际上就是设计一个Neural Network,Neural Network有很多种,最常见的有Feedforward Network。 Input层叫做Input Layer,output层叫做Output layer,中间层叫做Hidden Layrrs...
defshared_dataset(data_xy):""" Function that loads the dataset into shared variables The reason we store our dataset in shared variables is to allow Theano to copy it into the GPU memory (when code is run on GPU). Since copying data into the GPU is slow, copying a minibatch everytime...
In this post we’ll show how to use SigOpt’s Bayesian optimization platform to jointly optimize competing objectives in deep learning pipelines on NVIDIA GPUs more than ten times faster than traditional approaches like random search. A screenshot of the SigOpt web dashboard where users track the...
for i in range(epochs):params_grad = evaluate_gradient(loss_function, data, params)params = params - learning_rate * params_grad 对于预定义数量的epochs,我们首先计算整个数据集对应损失函数的梯度向量params_grad。我们的参数向量params。请注意,最先进的深度学习库提供了自动微分,可以有效地计算梯度。如果...