# 融合了SwinTransformer中MHA的qk_result + relative_position_bias + mask + softmax部分 from torchacc.runtime.nn.fmha import FusedSwinFmha FusedSwinFmha.apply(attn, relative_pos_bias, attn_mask, batch_size, window_num, num_head, window_len) nms/nms_normal/soft_nms/batched_soft_nms # ...
normal(0,10,(num_examples,len(w))) Y = torch.matmul(X,w)+b Y += torch.normal(0,0.01,Y.shape) return X,Y.reshape(-1,1) true_w = torch.tensor([2,3.4]) true_b = 4.2 features,labels = synthetic_data(true_w,true_b,num_examples=1000) print('features',features[0],"\nlable...
num_examples=1000 true_w=[2,-3.4] true_b=4.2 features=torch.from_numpy(np.random.normal(0,1,(num_examples,num_inputs))) labels=true_w[0]*features[:,0]+true_w[1]*features[:,1]+true_b labels+=torch.from_numpy(np.random.normal(0,0.01,size=labels.size())) ''' def use_svg_...
[2, -3.4]) # b = 4.2 # num_examples = 1000 X = torch.normal(0, # 均值 1, # 方差 (num_examples, len(w))) # 生成一个1000行,2列的矩阵 y = torch.matmul(X, w) + b # 噪音 y += torch.normal(0, 0.01, y.shape) return X, y.reshape((-1, 1)) true_w = torch.tensor...
(i + batch_size, num_examples)]) # 最后一次可能不足一个batch8. yield features.index_select(0, j), labels.index_select(0, j)9.10. #初始化W:[2,1]和b[1]11. w = torch.tensor(np.random.normal(0, 0.01, (num_inputs, 1)), dtype=torch.float32)12. b = torch.zeros(1, dtype=...
首先我们分别创建使用 CPU 和 GPU 运算的 2 个矩阵: import tensorflow as tf import timeit n = 10 # 创建在 CPU 上运算的 2 个矩阵 with tf.device('/cpu:0'): cpu_a = tf.random.normal([1, n]) cpu_b = tf.random.normal([n, 1]) print(cpu_a.device, cpu_b.device) # 创建使用 ...
3.3初始化参数W,b 1.# 输入与输出2.num_inputs =7843.num_outputs =104.5.W = torch.tensor(np.random.normal(0,0.01,(num_inputs,num_outputs)),dtype=torch.float)6.b = torch.zeros(num_outputs,dtype = torch.float)7.#开启梯度track8.W.requires_grad_(requires_grad =True)9.b.requires_grad...
optimizer.step() def update_one_episode(self): # 效果不好,不建议使用 print( f"[INFO] update network per batch [{len(self.replay_buffer)} | {len(self.replay_buffer)} | {self.configer.num_epochs}...") for _ in range(self.configer.num_epochs): batch_data = self.replay_buffer....
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normal(0, 0.01, size=shape), device=device, dtype=torch.float32) return torch.nn.Parameter(ts, requires_grad=True) def _three(): return (_one((num_inputs, num_hiddens)), _one((num_hiddens, num_hiddens)), torch.nn.Parameter(torch.zeros(num_hiddens, device=device, dtype=torch.float...