Tensorflow矩阵变形

朱雀 2022-04-16 06:59 220阅读 0赞

[官方API][API]

## tf.reshape ##

# tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9]
    # tensor 't' has shape [9]
    reshape(t, [3, 3]) ==> [[1, 2, 3],
                            [4, 5, 6],
                            [7, 8, 9]]
    
    # tensor 't' is [[[1, 1], [2, 2]],
    #                [[3, 3], [4, 4]]]
    # tensor 't' has shape [2, 2, 2]
    reshape(t, [2, 4]) ==> [[1, 1, 2, 2],
                            [3, 3, 4, 4]]
    
    # tensor 't' is [[[1, 1, 1],
    #                 [2, 2, 2]],
    #                [[3, 3, 3],
    #                 [4, 4, 4]],
    #                [[5, 5, 5],
    #                 [6, 6, 6]]]
    # tensor 't' has shape [3, 2, 3]
    # pass '[-1]' to flatten 't'
    reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]
    
    # -1 can also be used to infer the shape
    
    # -1 is inferred to be 9:
    reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
                             [4, 4, 4, 5, 5, 5, 6, 6, 6]]
    # -1 is inferred to be 2:
    reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
                             [4, 4, 4, 5, 5, 5, 6, 6, 6]]
    # -1 is inferred to be 3:
    reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1],
                                  [2, 2, 2],
                                  [3, 3, 3]],
                                 [[4, 4, 4],
                                  [5, 5, 5],
                                  [6, 6, 6]]]
    
    # tensor 't' is [7]
    # shape `[]` reshapes to a scalar
    reshape(t, []) ==> 7

## tf.squeeze ##

# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
    shape(squeeze(t)) ==> [2, 3]
    
    # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
    shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]

## tf.expand\_dims ##

# 't' is a tensor of shape [2]
    shape(expand_dims(t, 0)) ==> [1, 2]
    shape(expand_dims(t, 1)) ==> [2, 1]
    shape(expand_dims(t, -1)) ==> [2, 1]
    
    # 't2' is a tensor of shape [2, 3, 5]
    shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
    shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
    shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]

## tf.concat ##

t1 = [[1, 2, 3], [4, 5, 6]]
    t2 = [[7, 8, 9], [10, 11, 12]]
    tf.concat([t1, t2], 0) ==> [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]
    tf.concat([t1, t2], 1) ==> [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]]

## tf.stack ##

# 'x' is [1, 4]
    # 'y' is [2, 5]
    # 'z' is [3, 6]
    stack([x, y, z]) => [[1, 4], [2, 5], [3, 6]]  # Pack along first dim.
    stack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]

## tf.unstack ##

# 'x' is [[1, 4], [2, 5], [3, 6]]
    unstack(y, axis=0) => [a, b, c]
    a is [1, 4]
    b is [2, 5]
    c is [3, 6]
    
    unstack(y, axis=1) => [a, b]
    a is [1, 2, 3]
    b is [4, 5, 6]

## tf.transpose ##

# 'x' is [[1 2 3]
    #         [4 5 6]]
    tf.transpose(x) ==> [[1 4]
                         [2 5]
                         [3 6]]
    
    # Equivalently
    tf.transpose(x, perm=[1, 0]) ==> [[1 4]
                                      [2 5]
                                      [3 6]]
    
    # 'perm' is more useful for n-dimensional tensors, for n > 2
    # 'x' is   [[[1  2  3]
    #            [4  5  6]]
    #           [[7  8  9]
    #            [10 11 12]]]
    # Take the transpose of the matrices in dimension-0
    tf.transpose(x, perm=[0, 2, 1]) ==> [[[1  4]
                                          [2  5]
                                          [3  6]]
    
                                         [[7 10]
                                          [8 11]
                                          [9 12]]]

## tf.one\_hot ##

one_hot([0, 1, 2], depth=3) => [[1., 0., 0.],
    							   [0., 1., 0.],
    							   [0., 0., 1.]]
    							   
    one_hot([0, 2, -1, 1], depth=3, on_value=5.0, off_value=0.0) => [5.0 0.0 0.0]  // one_hot(0)
      									   								[0.0 0.0 5.0]  // one_hot(2)
      									   								[0.0 0.0 0.0]  // one_hot(-1)
      									   								[0.0 5.0 0.0]  // one_hot(1)
    
    one_hot([[0, 2], [1, -1], depth=3, on_value=1.0, off_value=0.0) => [
    																	    [1.0, 0.0, 0.0]  // one_hot(0)
    																	    [0.0, 0.0, 1.0]  // one_hot(2)
    																	][
    																	    [0.0, 1.0, 0.0]  // one_hot(1)
    																	    [0.0, 0.0, 0.0]  // one_hot(-1)
    																	]

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[API]: https://www.tensorflow.org/versions/r1.2/api_guides/python/array_ops