categorical) # Save target and predictors self.X = self.students_frame.drop(self.target, axis=1) self.y = self.students_frame[self.target] def __len__(self): return len(self.students_frame) def __getitem__(self, idx): # Convert idx from tensor to list due to pandas bug (that ...
probs = policy_network(state) # Note that this is equivalent to what used to be called multinomial m = Categorical(probs) action = m.sample() next_state, reward = env.step(action) loss = -m.log_prob(action) * reward loss.backward() Pathwise derivative 实现这些随机/策略梯度的另一种方...
X_train=X_train.reshape(-1,28,28,1).astype("float32")/255.0X_test=X_test.reshape(-1,28,28,1).astype("float32")/255.0Y_train=np_utils.to_categorical(Y_train,10) Y_test=np_utils.to_categorical(Y_test,10)print(X_train.shape)print(Y_train.shape)print(X_train.shape)defvgg16()...
RuntimeError: invalid argument 2: invalid multinomial distribution (with replacement=False, not enough non-negative category to sample) at ../aten/src/TH/generic/THTensorRandom.cpp:320 >>>torch.multinomial(weights,4, replacement=True) tensor([ 2, 1, 1, 1]) torch.distributions.categorical.Cat...
y_train = to_categorical(y_train, num_classes=10) #使用2D卷积需要用到一个三维的矩阵 X_train = X_train.reshape(-1, 16, 16, 16) X_test = X_test.reshape(-1, 16, 16, 16) X_train,X_val,y_train,y_val = train_test_split(X_train, y_train, ...
There is some unfortunate confusion in that many libraries implement random categorical samplers but name them multinomial samplers: PyTorch, Numpy, TensorFlow. On the one hand it would be nice to have an actual multinomial; on the other hand it would be nice to follow the naming convention of...
🐛 Bug When sampling from a torch.distributions.Categorical distribution initialized with a few distributions (e.g. 2 distributions each with 2**16 categories) the sample() functions allocates unnecessary huge amount of memory. To Reprodu...
torch.distributions.categorical.Categorical( probs=None, logits=None, validate_args=None) 样本是来{0,...,K−1} 的整数,其中 K 是 probs.size(-1)。 2.6.1 函数 2.6.2 注意: 创建分类分布时候的Tensor中元素的和可以不是1,最后归一化到1即可 ...
def to_categorical(y, num_classes): """ 1-hot encodes a tensor """ new_y = torch.eye(num_classes)[y.cpu().data.numpy(),] if (y.is_cuda): return new_y.cuda() return new_y Example #20Source File: train_partseg.py From Pointnet_Pointnet2_pytorch with MIT License 5 votes ...
(according to iteration distribution)tsks = Categorical(probs=self.tasks_probs_over_iterations[iter_num]).sample(torch.Size([self.samples_in_batch]))# Generate samples indices for iter_numiter_indices = torch.zeros(0, dtype=torch.int32)fortask_idxinrange(self.num_of_tasks):ifself.tasks_...