标签：tensor Numpy torch tf np GPU 速查 numpy axis
类型（Types）
| Numpy |
PyTorch |
| np.ndarray |
torch.Tensor |
| np.float32 |
torch.float32; torch.float |
| np.float64 |
torch.float64; torch.double |
| np.float |
torch.float16; torch.half |
| np.int8 |
torch.int8 |
| np.uint8 |
torch.uint8 |
| np.int16 |
torch.int16; torch.short |
| np.int32 |
torch.int32; torch.int |
| np.int64 |
torch.int64; torch.long |
构造器（Constructor）
零和一（Ones and zeros）
| Numpy |
PyTorch |
| np.empty((2, 3)) |
torch.empty(2, 3) |
| np.empty_like(x) |
torch.empty_like(x) |
| np.eye |
torch.eye |
| np.identity |
torch.eye |
| np.ones |
torch.ones |
| np.ones_like |
torch.ones_like |
| np.zeros |
torch.zeros |
| np.zeros_like |
torch.zeros_like |
从已知数据构造
| Numpy |
PyTorch |
| np.array([[1, 2], [3, 4]]) |
torch.tensor([[1, 2], [3, 4]]) |
| np.array([3.2, 4.3], dtype=np.float16)np.float16([3.2, 4.3]) |
torch.tensor([3.2, 4.3], dtype=torch.float16) |
| x.copy() |
x.clone() |
| np.fromfile(file) |
torch.tensor(torch.Storage(file)) |
| np.frombuffer |
|
| np.fromfunction |
|
| np.fromiter |
|
| np.fromstring |
|
| np.load |
torch.load |
| np.loadtxt |
|
| np.concatenate |
torch.cat |
数值范围
| Numpy |
PyTorch |
| np.arange(10) |
torch.arange(10) |
| np.arange(2, 3, 0.1) |
torch.arange(2, 3, 0.1) |
| np.linspace |
torch.linspace |
| np.logspace |
torch.logspace |
构造矩阵
| Numpy |
PyTorch |
| np.diag |
torch.diag |
| np.tril |
torch.tril |
| np.triu |
torch.triu |
参数
| Numpy |
PyTorch |
| x.shape |
x.shape |
| x.strides |
x.stride() |
| x.ndim |
x.dim() |
| x.data |
x.data |
| x.size |
x.nelement() |
| x.dtype |
x.dtype |
索引
| Numpy |
PyTorch |
| x[0] |
x[0] |
| x[:, 0] |
x[:, 0] |
| x[indices] |
x[indices] |
| np.take(x, indices) |
torch.take(x, torch.LongTensor(indices)) |
| x[x != 0] |
x[x != 0] |
形状（Shape）变换
| Numpy |
PyTorch |
| x.reshape |
x.reshape; x.view |
| x.resize() |
x.resize_ |
| null |
x.resize_as_ |
| x.transpose |
x.transpose or x.permute |
| x.flatten |
x.view(-1) |
| x.squeeze() |
x.squeeze() |
| x[:, np.newaxis]; np.expand_dims(x, 1) |
x.unsqueeze(1) |
数据选择
| Numpy |
PyTorch |
| np.put |
|
| x.put |
x.put_ |
| x = np.array([1, 2, 3])x.repeat(2) # [1, 1, 2, 2, 3, 3] |
x = torch.tensor([1, 2, 3])x.repeat(2) # [1, 2, 3, 1, 2, 3]x.repeat(2).reshape(2, -1).transpose(1, 0).reshape(-1) # [1, 1, 2, 2, 3, 3] |
| np.tile(x, (3, 2)) |
x.repeat(3, 2) |
| np.choose |
|
| np.sort |
sorted, indices = torch.sort(x, [dim]) |
| np.argsort |
sorted, indices = torch.sort(x, [dim]) |
| np.nonzero |
torch.nonzero |
| np.where |
torch.where |
| x[::-1] |
|
数值计算
| Numpy |
PyTorch |
| x.min |
x.min |
| x.argmin |
x.argmin |
| x.max |
x.max |
| x.argmax |
x.argmax |
| x.clip |
x.clamp |
| x.round |
x.round |
| np.floor(x) |
torch.floor(x); x.floor() |
| np.ceil(x) |
torch.ceil(x); x.ceil() |
| x.trace |
x.trace |
| x.sum |
x.sum |
| x.cumsum |
x.cumsum |
| x.mean |
x.mean |
| x.std |
x.std |
| x.prod |
x.prod |
| x.cumprod |
x.cumprod |
| x.all |
(x == 1).sum() == x.nelement() |
| x.any |
(x == 1).sum() > 0 |
数值比较
| Numpy |
PyTorch |
| np.less |
x.lt |
| np.less_equal |
x.le |
| np.greater |
x.gt |
| np.greater_equal |
x.ge |
| np.equal |
x.eq |
| np.not_equal |
x.ne |
pytorch与tensorflow API速查表
| 方法名称 |
pytroch |
tensorflow |
numpy |
| 裁剪 |
torch.clamp(x, min, max) |
tf.clip_by_value(x, min, max) |
np.clip(x, min, max) |
| 取最小值 |
torch.min(x, dim)[0] |
tf.min(x, axis) |
np.min(x , axis) |
| 取两个tensor的最大值 |
torch.max(x, y) |
tf.maximum(x, y) |
np.maximum(x, y) |
| 取两个tensor的最小值 |
torch.min(x, y) |
torch.minimum(x, y) |
np.minmum(x, y) |
| 取最大值索引 |
torch.max(x, dim)[1] |
tf.argmax(x, axis) |
np.argmax(x, axis) |
| 取最小值索引 |
torch.min(x, dim)[1] |
tf.argmin(x, axis) |
np.argmin(x, axis) |
| 比较(x > y) |
torch.gt(x, y) |
tf.greater(x, y) |
np.greater(x, y) |
| 比较(x < y) |
torch.le(x, y) |
tf.less(x, y) |
np.less(x, y) |
| 比较(x==y) |
torch.eq(x, y) |
tf.equal(x, y) |
np.equal(x, y) |
| 比较(x!=y) |
torch.ne(x, y) |
tf.not_equal(x, y) |
np.not_queal(x , y) |
| 取符合条件值的索引 |
torch.nonzero(cond) |
tf.where(cond) |
np.where(cond) |
| 多个tensor聚合 |
torch.cat([x, y], dim) |
tf.concat([x,y], axis) |
np.concatenate([x,y], axis) |
| 堆叠成一个tensor |
torch.stack([x1, x2], dim) |
tf.stack([x1, x2], axis) |
np.stack([x, y], axis) |
| tensor切成多个tensor |
torch.split(x1, split_size_or_sections, dim) |
tf.split(x1, num_or_size_splits, axis) |
np.split(x1, indices_or_sections, axis) |
| ` |
torch.unbind(x1, dim) |
tf.unstack(x1,axis) |
NULL |
| 随机扰乱 |
torch.randperm(n) 1 |
tf.random_shuffle(x) |
np.random.shuffle(x) 2 np.random.permutation(x ) 3 |
| 前k个值 |
torch.topk(x, n, sorted, dim) |
tf.nn.top_k(x, n, sorted) |
NULL |
- 该方法只能对0~n-1自然数随机扰乱，所以先对索引随机扰乱，然后再根据扰乱后的索引取相应的数据得到扰乱后的数据
- 该方法会修改原值，没有返回值
- 该方法不会修改原值，返回扰乱后的值
标签：tensor,
Numpy,
torch,
tf,
np,
GPU,
速查,
numpy,
axis
From： https://www.cnblogs.com/mfmufeng/p/numpy-tensor.html