# Initialize random batch of input vectors, for *size = (T,N,C) input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) # Initialize random batch of targets (0 = blank, 1:C = classes) ...
os.environ['MASTER_PORT'] = '12355' # initialize the process group dist.init_process_group("gloo", rank=rank, world_size=world_size) # Explicitly setting seed to make sure that models created in two processes # start from same random weights and biases. torch.manual_seed(42) def cleanup...
dtype=torch.float device=torch.device("cpu")# device=torch.device("cuda:0")# Uncommentthisto run onGPU# Create random input and output data x=torch.linspace(-math.pi,math.pi,2000,device=device,dtype=dtype)y=torch.sin(x)# Randomly initialize weights a=torch.randn((),device=device,dtype=...
x= F.dropout(x, 0.5, training=self.training) x1=self.fc1(x)#x = F.dropout(x1, 0.5, training=self.training)x =self.fc2(x1)ifself.training:returnx, x1returnxdef_initialize_weights(self):forminself.modules():ifisinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_...
def_initialize_weights(self):forminself.modules():ifisinstance(m,nn.Conv2d):nn.init.kaiming_normal_(m.weight,mode='fan_out',nonlinearity='relu')ifm.bias is not None:nn.init.constant_(m.bias,0)elifisinstance(m,nn.BatchNorm2d):nn.init.constant_(m.weight,1)nn.init.constant_(m.bias,0...
(N, D_out)# Randomly initialize weightsw1 = np.random.randn(D_in, H)w2 = np.random.randn(H, D_out)learning_rate = 1e-6for t in range(500):# Forward pass: compute predicted yh = x.dot(w1)h_relu = np.maximum(h, 0)y_pred = h_relu.dot(w2)# Compute and print lossloss ...
如前所述,每一层具有不同的扩张因子,并且可以为每一层的扩张卷积节点创建传递该因子。 由于跨步为 1,所以填充保持为 0,目的不是上采样或下采样。init_weights_for_test是通过将权重矩阵填充 1 来进行测试的便捷函数。 PyTorch 提供的灵活性使用户可以在线调整参数,这对于调试网络更加有用。forward传递仅调用 PyTor...
self.initialize_weights()# 创建对象时自动执行,初始化权重 def kernel_fun(self, batches): n_input = batches.size(0) # number of inputs,0代表行数 A = self.centers.view(self.num_centers, -1).repeat(n_input, 1, 1) #view成num_centers行,然后再重复n_input次 ...
def initialize(self):for m in self.modules():# 判断这一层是否为线性层,如果为线性层则初始化权值if isinstance(m, nn.Linear):# 计算均匀分布的上限、下限a = np.sqrt(6 / (self.neural_num + self.neural_num))# 把a变换到 tanh,计算增益。观察数据输入到激活函数之后,标准差的变化tanh_gain = ...
# dataset returns numpy.random.randint(1, 10000) ctx = mp.get_context('fork') gen = torch.Generator().manual_seed(0) dl = DataLoader(dataset, batch_size=2, num_workers=2, multiprocessing_context=ctx, generator=gen) for epoch in range(2): print("=" * 4, "Epoch", epoch, "=" *...