# 配置文件示例config={"weight_initialization":"xavier_uniform","bias_initialization":"zeros","seed":42}# 使用torch.manual_seed(config["seed"]) 1. 2. 3. 4. 5. 6. 7. 8. 9. 以下是类图,展示了模型与其初始化之间的关系: MyModel+forward(x)WeightInitializer+initialize_weights(model) 以下是...
具体来说，PyTorch 会使用Kaiming 初始化方法（Kaiming normal initialization）对线性层的权重进行初始化，...
np.random.seed(3) # This seed makes sure your "random" numbers will be the as ours parameters = {} # weight bias dict L = len(layers_dims) # integer representing the number of layers for l in range(1, L): parameters['W' + str(l)] = np.random.randn(layers_dims[l], layers_...
elif isinstance(layer, torch.nn.Linear): torch.nn.init.xavier_normal_(layer.weight) if layer.bias is not None: torch.nn.init.constant_(layer.bias, val=0.0) # Initialization with given tensor. layer.weight = torch.nn.Parameter(tensor) 提取模型中的某一层 modules()会返回模型中所有模块的迭代...
np.random.seed(0)torch.manual_seed(0)torch.cuda.manual_seed_all(0) torch.backends.cudnn.deterministic = Truetorch.backends.cudnn.benchmark = False 显卡设置 如果只需要一张显卡。 # Device configurationdevice = torch.device('cuda' if torch.cuda.i...
张量和Numpy中ndarrays的概念很相似,有了这个作为基础,张量也可以被运行在GPU上来加速计算,下面直接可以感受下张量的创建与Numpy有什么样的区别。
Random number generator seed for random weight initialization. Attributes --- w_ : 1d-array Weights after fitting. b_ : Scalar Bias unit after fitting. errors_ : list Number of misclassifications (updates) in each epoch. """def__init...
#weight and bias initialization wh=np.random.uniform(size=(inputlayer_neurons,hiddenlayer_neurons)) bh=np.random.uniform(size=(1,hiddenlayer_neurons)) wout=np.random.uniform(size=(hiddenlayer_neurons,output_neurons)) bout=np.random.uniform(size=(1,output_neurons)) ...
seed = 128 rng = np.random.RandomState(seed) 3. 第一步是设置目录路径,以便安全保存! root_dir = os.path.abspath('.') data_dir = os.path.join(root_dir, 'data') # check for existence os.path.exists(root_dir), os.path.exists(data_d...
最后,在每次前向传播之前应用修剪,使用 PyTorch 的forward_pre_hooks。具体来说,当module被修剪时,就像我们在这里所做的那样,它将为与之关联的每个参数获取一个forward_pre_hook。在这种情况下,因为我们目前只修剪了名为weight的原始参数,所以只有一个钩子存在。 代码语言:javas...