D_in, H, D_out = 64, 1000, 100, 10# Create random input and output datax = np.random.randn(N, D_in)y = np.random.randn(N, D_out)# Randomly initialize weightsw1 = np.random.randn(D_in, H)w2 = np.random.
y=np.random.randn(N, D_out) # Randomly initialize weights w1=np.random.randn(D_in, H) w2=np.random.randn(H, D_out) learning_rate=1e-6 fortinrange(500): # Forward pass: compute predicted y h=x.dot(w1) h_relu=np.maximum(h,0) y_pred=h_relu.dot(w2) # Compute and print lo...
netD = Discriminator().to(device) #apply the weights_init function to randomly initialize all weights to mean=0,stdev=0.2. netD.apply(weights_init) #print the model print(netD) 这两个里面很值得借鉴得就是应该定义哪些层才会使得维度发生变化,这个很重要,每个层之后数据到底怎么变了。下面得训练过程...
y = torch.sin(x) # Create random Tensors for weights. For this example, we need # 4 weights: y = a + b * P3(c + d * x), these weights need to be initialized # not too far from the correct result to ensure convergence. # Setting requires_grad=True indicates that we want to...
dtype = torch.float device = torch.device("mps") # 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=dtype) b = torch.randn((), ...
# Randomly initialize weights w1 = np.random.randn(D_in, H) w2 = np.random.randn(H, D_out) learning_rate = 1e-6 for t in range(500): # Forward pass: compute predicted y h = x.dot(w1) h_relu = np.maximum(h, 0)
# Create the Discriminator netD = Discriminator(ngpu).to(device) # Handle multi-GPU if desired if (device.type == 'cuda') and (ngpu > 1): netD = nn.DataParallel(netD, list(range(ngpu))) # Apply the ``weights_init`` function to randomly initialize all weights # like this: ``to...
# Randomly initialize weightsw1 = torch.randn(D_in, H, device=device, dtype=dtype)w2 = torch.randn(H, D_out, device=device, dtype=dtype) learning_rate = 1e-6for t in range(500):# Forward pass: compute predicted yh =...
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=dtype)b=torch.randn((),device=device,dtype=dtype)c=torch.randn((),device=device,dtype=dtype)d=torch.randn((),device=device,dtype=dtype)learn...
# -*- coding: utf-8 -*-import numpy as npimport math# Create random input and output datax = np.linspace(-math.pi, math.pi, 2000)y = np.sin(x)# Randomly initialize weightsa = np.random.randn()b = np.random.randn()c = np.random.randn()d = np.random.randn()learning_rate ...