矩阵拼接的函数tf.stack()与矩阵分解的函数tf.unstack()
tf.unstack(value, num=None, axis=0, name='unstack')
Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
Unpacks `num` tensors from `value` by chipping it along the `axis` dimension.
Args:
value: A rank `R > 0` `Tensor` to be unstacked.
num: An `int`. The length of the dimension `axis`. Automatically inferred
//一个“int”。尺寸“轴”的长度。自动推断为“无”（默认）。
if `None` (the default).
axis: An `int`. The axis to unstack along. Defaults to the first
dimension. Supports negative indexes.
name: A name for the operation (optional).
Returns:
The list of `Tensor` objects unstacked from `value`.
Raises:
ValueError: If `num` is unspecified and cannot be inferred.
ValueError: If `axis` is out of the range [-R, R).
import tensorflow as tf
a = tf.constant([1,2,3])
b = tf.constant([4,5,6])
c = tf.stack([a,b],axis=1)
d = tf.unstack(c,axis=0)
e = tf.unstack(c,axis=1)
print(c.get_shape())
with tf.Session() as sess:
print(sess.run(c))
print(sess.run(d))
print(sess.run(e))
(3, 2)
[[1 4]
[2 5]
[3 6]]
[array([1, 4]), array([2, 5]), array([3, 6])]
[array([1, 2, 3]), array([4, 5, 6])]
numpy.delete删除行或列
import numpy as np
A = np.delete(B, 2, axis=0)
B = np.delete(C, 1, axis=1)
>>> dataset=[[1,2,3],[2,3,4],[4,5,6]]
>>> import numpy as np
>>> dataset = np.delete(dataset, -1, axis=1)
>>> dataset
array([[1, 2],
[2, 3],
[4, 5]])
arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
np.delete(arr, [1,2], axis=1)
array([[ 1, 4],
[ 5, 8],
[ 9, 12]])
拼接_numpy列表索引方式
a=np.array([[1,2],[3,4],[5,6],[7,8]])
b=a[:2]
- numpy.concatenate
numpy.concatenate((a1, a2, …), axis=0)
Join a sequence of arrays along an existing axis.
Parameters:
a1, a2, … : sequence of array_like
The arrays must have the same shape, except in the dimension corresponding to axis (the first, by default).
axis : int, optional
The axis along which the arrays will be joined. Default is 0.
Returns:
res : ndarray
The concatenated array.
>>> a = np.array([[1, 2], [3, 4]])
>>> b = np.array([[5, 6]])
>>> np.concatenate((a, b), axis=0)
array([[1, 2],
[3, 4],
[5, 6]])
>>> np.concatenate((a, b.T), axis=1)
array([[1, 2, 5],
[3, 4, 6]])
###############################
>>> a = np.ma.arange(3)
>>> a[1] = np.ma.masked
>>> b = np.arange(2, 5)
>>> a
masked_array(data = [0
mask = [False True False],
fill_value = 999999)
>>> b
array([2, 3, 4])
>>> np.concatenate([a, b])
masked_array(data = [0 1 2 2 3 4],
mask = False,
fill_value = 999999)
>>> np.ma.concatenate([a, b])
masked_array(data = [0
mask = [False True False False False False],
fill_value = 999999)
拼接_tf.concat
python
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]]
# tensor t3 with shape [2, 3]
# tensor t4 with shape [2, 3]
tf.shape(tf.concat([t3, t4], 0)) ==> [4, 3]
tf.shape(tf.concat([t3, t4], 1)) ==> [2, 6]
拼接_keras.legacy.layers.merge
Help on function merge in module keras.legacy.layers:
merge(inputs, mode='sum', concat_axis=-1, dot_axes=-1, output_shape=None, output_mask=None, arguments=None, name=None)
Functional merge, to apply to Keras tensors (NOT layers).
Returns a Keras tensor.
# Example
# Arguments
mode: String or lambda/function. If string, must be one
of: 'sum', 'mul', 'concat', 'ave', 'cos', 'dot', 'max'.
If lambda/function, it should take as input a list of tensors
and return a single tensor.
concat_axis: Integer, axis to use in mode `concat`.
dot_axes: Integer or tuple of integers,
axes to use in mode `dot` or `cos`.
output_shape: Shape tuple (tuple of integers), or lambda/function
to compute output_shape (only if merge mode is a lambda/function).
If the latter case, it should take as input a list of shape tuples
(1:1 mapping to input tensors) and return a single shape tuple,
including the batch size
(same convention as the `compute_output_shape` method of layers).
node_indices: Optional list of integers containing
the output node index for each input layer
(in case some input layers have multiple output nodes).
will default to an array of 0s if not provided.
tensor_indices: Optional list of indices of output tensors
to consider for merging
(in case some input layer node returns multiple tensors).
concat_feat = merge([concat_feat, x], mode='concat', concat_axis=concat_axis, name='concat_'+str(stage)+'_'+str(branch))
拼接_keras.legacy.layers.merge.concatenate()
>>> import keras.legacy.layers as layers
>>> dir(layers)
['AtrousConvolution1D', 'AtrousConvolution2D', 'Highway', 'InputSpec', 'K', 'Layer', 'MaxoutDense', 'Merge', '__builtins__', '__doc__', '__file__', '__name__', '__package__', 'activations', 'constraints', 'func_dump', 'func_load', 'initializers', 'inspect', 'merge', 'python_types', 'regularizers', 'warnings']
>>>
>>> dir(layers.Merge)
['__call__', '__class__', '__delattr__', '__dict__', '__doc__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_add_inbound_node', '_arguments_validation', '_get_node_attribute_at_index', 'add_loss', 'add_update', 'add_weight', 'assert_input_compatibility', 'build', 'built', 'call', 'compute_mask', 'compute_output_shape', 'constraints', 'count_params', 'from_config', 'get_config', 'get_input_at', 'get_input_mask_at', 'get_input_shape_at', 'get_losses_for', 'get_output_at', 'get_output_mask_at', 'get_output_shape_at', 'get_updates_for', 'get_weights', 'input', 'input_mask', 'input_shape', 'losses', 'non_trainable_weights', 'output', 'output_mask', 'output_shape', 'set_weights', 'trainable_weights', 'updates', 'weights']
def concatenate(inputs, axis=-1, **kwargs):
"""Functional interface to the `Concatenate` layer.
# Arguments
inputs: A list of input tensors (at least 2).
axis: Concatenation axis.
**kwargs: Standard layer keyword arguments.
# Returns
A tensor, the concatenation of the inputs alongside axis `axis`.
"""
return Concatenate(axis=axis, **kwargs)(inputs)
x = layers.concatenate(
[branch1x1, branch5x5, branch3x3dbl, branch_pool],
axis=channel_axis,
name='mixed1')
mxnet.ndarray.concat(*data, **kwargs)
x = [[1,1],[2,2]]
y = [[3,3],[4,4],[5,5]]
z = [[6,6], [7,7],[8,8]]
concat(x,y,z,dim=0) = [[ 1., 1.],
[ 2., 2.],
[ 3., 3.],
[ 4., 4.],
[ 5., 5.],
[ 6., 6.],
[ 7., 7.],
[ 8., 8.]]
Note that you cannot concat x,y,z along dimension 1 since dimension
0 is not the same for all the input arrays.
concat(y,z,dim=1) = [[ 3., 3., 6., 6.],
[ 4., 4., 7., 7.],
[ 5., 5., 8., 8.]]
print('real...')
test_iter.reset()
batch = test_iter.next()
y_te = batch.label[0]
# print(batch.label[0], batch.data[0].shape)
for batch in test_iter:
y_te = mx.ndarray.concat(y_te, batch.label[0], dim=0)
print(len(batch.label[0]), len(y_te))
mxnet.symbol.Concat(*data, **kwargs)
Parameters:
data (Symbol[]) – List of arrays to concatenate
dim (int, optional, default='1') – the dimension to be concated.
name (string, optional.) – Name of the resulting symbol.
Returns:
The result symbol.
Return type:
Symbol
Examples
Concat two (or more) inputs along a specific dimension:
>>> a = Variable('a')
>>> b = Variable('b')
>>> c = Concat(a, b, dim=1, name='my-concat')
>>> c
>>> SymbolDoc.get_output_shape(c, a=(128, 10, 3, 3), b=(128, 15, 3, 3))
{'my-concat_output': (128L, 25L, 3L, 3L)}
mxnet.symbol.slice_axis(data=None, axis=_Null, begin=_Null, end=_Null, name=None, attr=None, out=None, **kwargs)
Parameters:
data (Symbol) – Source input
axis (int, required) – Axis along which to be sliced, supports negative indexes.
begin (int, required) – The beginning index along the axis to be sliced, supports negative indexes.
end (int or None, required) – The ending index along the axis to be sliced, supports negative indexes.
name (string, optional.) – Name of the resulting symbol.
Returns:
The result symbol.
Return type:
Examples:
x = [[ 1., 2., 3., 4.],
[ 5., 6., 7., 8.],
[ 9., 10., 11., 12.]]
slice_axis(x, axis=0, begin=1, end=3) = [[ 5., 6., 7., 8.],
[ 9., 10., 11., 12.]]
slice_axis(x, axis=1, begin=0, end=2) = [[ 1., 2.],
[ 5., 6.],
[ 9., 10.]]
slice_axis(x, axis=1, begin=-3, end=-1) = [[ 2., 3.],
[ 6., 7.],
[ 10., 11.]]
