A, pytorch and python different data types:
* Pytorch supports only numeric data, does not support string type data.
* If we want to represent pytorch string, string will be converted to a digital code (matrix or vector) to handle. Common methods: One-hot, Embedding (Word2vec, glove)
Two, pytorch data types View
* A.type () # View tensor digital type
* Type (a) # return a Tensor is a type, but does not return a specific number of types Tensor
* Isinstance (a, torch.FloatTensor) # floattensor determines whether a digital type of torch, True is returned
isinstance (data, torch.cuda.DoubleTensor) # If the data is initially deployed in the CPU above, if compared with cuda, is False
After data = data.cuda () # on the CPU to transfer data to cuda above, is then compared True
Third, the scalar
* Defined appreciated: constant can be appreciated, the direction vector does not (only values, no direction), dimension = 0
* Pytorch as defined: torch.tensor (1.), The output tensor (1.)
* Role: used in computing loss. The error between the predicted value and the true value is calculated scalar
* Check the size of the scalar
Note: .shape method without brackets
In addition to the method of the above figure, there a.dim ()
The difference is: dim is the dimension, i.e. a high matrix, one value. size / shape represented by a plurality of height and width values .... Matrix.
Fourth, the tensor
* Tensor and the scalar difference:
Tensor directional, need to add [] is defined
* Numpy and tensor type swap
1) torch.from_numpy (data) # converts data tensor numpy
2) tensor_data.numpy () # tensor directly convert numpy
* Tensor multidimensional data use
Tensor torch.randn (2, 3) # 2 generates a random rows and three columns: 1) two-dimensional data for: linear batch input, such as
2) three-dimensional data: torch.rand (1, 2, 3); three for RNN, [10, 20, 100] 20 sentences, 10 words, each word is represented by a 100-dimensional vector
3) four-dimensional data: torch.rand (2, 3, 28, 28) 2 photos, 3 channels, picture size 28 (h) * 28 (w);
* Tensor memory View
a.numel() # number of element
* Definition of tensor
* Torch.tensor () # lowercase
Reception existing list or matrix of data [];
* Torch.Tensor (), torch.FloatTensor () # uppercase
Receiving dimensions (d1, d2, d3 ...), random initialization data; of course you can also receive existing data, but recommended a small written data, written large dimensions, and dimension data or easy to confuse.
* torch.empty()
Given dimension. Uninitialized data, very irregular, very large, very small may be the case, as far as possible to cover the complete data
* torch.FloatTensor(d1, d2, d3)
Generating a random vector, followed by modify assigned values
* Recommended Data Types
Enhanced learning inside with a double, higher precision; other parts of general use float
* Set the default data type tensor
torch.set_default_tensor_type (torch.DoubleTensor) # Note: The original data must be decimal, if all integer, this setting is invalid
* Torch.rand (2, 3) # recommended
Generating two rows and three columns, the data value between 0 and 1 randomly distributed (uniformly distributed), the default is N (0, 1) mean 0 and variance 1
* torch.rand_like(a)
The shape of a randomly generated
* torch.randint(1, 10, [d1, d2, ...])
Integer followed by the range [1-10) is given dimension
* torch.normal(mean=torch.full([2, 3], 0), std=torch.arange(0.6, 0, -0.1))
Generating normal, two rows and three columns, with mean 0 and variance from 0.6-0.1, can be seen from the figure, the output data is almost decreasing trend.
* torch.full([2, 3], 7)
Assign all three rows 2 is 7; torch.full ([], 7) is generated tensor (7) scalar; torch.full ([1], 7) is generated by a vector of length 1
* torch.arange(0, 10)
Generating [0, 10) of the tensor ([0, 1, 2, ... 9]); torch.arange (0, 10) # [0, 10), tensor data interval 2; tensor.range () not recommended.
* torch.linspace(0, 10, steps=4)
The [0,10] was cut into 4 equally divided parts
* torch.ones(3, 3)
Given shape, to generate a full tensor matrix 1; torch.ones_like (a), generated according to a shape of a matrix are all 1's.
* torch.zeros(3, 3)
Given shape, to generate a full tensor matrix 0
* torch.eye(3, 4)
Given shape, generating a diagonal matrix. If it is not a diagonal matrix, is padded with 0, as shown below:
* torch.randperm(10)
Generating a one-dimensional tensor [0, 9] is scrambled.
It can be used: two equal dimensions, one relationship data, if the same random order in a way that can be generated by the random index this method, as shown below:
