<NDArray 4x1 @cpu(0)> //调用attach_grad函数来申请存储梯度所需要的内存 x.attach_grad() //调用record函数来要求MXNet记录与求梯度有关的计算。 with autograd.record(): y = 2 * nd.dot(x.T, x) ---做矩阵X的转置和X的dot运算,1行4列*4行1列,得到一个标量 //调用backward函数自动求梯度 y....
for param in params: param.attach_grad() 定义模型和损失函数 这里要注意的是:我们的维度不是1,所以要把数组的维度reshape一下变成一维数组 def net(X): return nd.dot(X.reshape((-1,num_inputs)), w) + b def square_loss(yhat, y): return (yhat - y.reshape(yhat.shape)) ** 2 然后是优...
num_hiddens))b1 = nd.zeros(num_hiddens)W2 = nd.ones((num_hiddens, num_hiddens2))b2 = nd.zeros(num_hiddens2)W2 = nd.ones((num_hiddens2, num_outputs))b3 = nd.zeros(num_outputs)params = [W1, b1, W2, b2, W3, b3]for param in params:param.attach_grad() ...
p.attach_grad() return params #定义模型 #隐藏状态初始化函数,需要返回记忆细胞,比较门控RNN多一个 def init_lstm_state(batch_size,num_hiddens,ctx): return (nd.zeros(shape=(batch_size,num_hiddens),ctx=ctx), nd.zeros(shape=(batch_size,num_hiddens),ctx=ctx)) def lstm(inputs,state,params)...
param.attach_grad()2. 🌲全相同值初始化 考虑到很可能是权值参数导致的每次训练的结果有浮动，那不如就试试每次都从相同的权值参数开始训练，这样我后面调超参数的时候，应该就好观察一点。我一开始想的是简单起见，把所有的权值都设置为 1 试试。改动后的参数初始化代码：num_inputs, num_outputs = 784, ...
# conv.bias(255) to (3,85)b.data[:, 4] += math.log(8/ (640/s) **2) # obj (8 objects per 640 image)b.data[:, 5:] += math.log(0.6/ (m.nc-0.999999)) ifcfisNoneelsetorch.log(cf/cf.sum()) # clsmi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True...
model=MyModel()gm=GradManager().attach(model.parameters())optimizer=optim.SGD(model.parameters(),lr=0.01)# lr may vary with different modelfordata,label in dataset:with gm:pred=model(data)loss=loss_fn(pred,label)gm.backward()optimizer.step().clear_grad() ...
size = 10for x,y in data_iter(batch_size,features,labels):print(x,y)break;w = nd.random.normal(scale=0.01, shape=(num_inputs,1))b = nd.zeros(shape=(1,))w.attach_grad()b.attach_grad()print(w)print(b)def linreg(x,w,b):return nd.dot(x,w)+bdef squared_loss(y_hat,y):...
args.grad_clip) optimizer = tf.train.AdamOptimizer(self.lr) self.train_op = optimizer.apply_gradients(zip(grads, tvars)) def sample(self, sess, words, vocab, num=200, prime='first all', sampling_type=1, pick=0, width=4):
5.3 对同一网站，如果开多个浏览器登录不同账号在浏览器自动化插件模式下，采用的是attach的方式，这种...