我正在尝试重新创建我在tensorflow中编写的numpy代码片段，但我一直在努力寻找正确/最佳的tensorflow操作。
考虑以下numpy解决方案：
import numpy as np
# Initialize a random numpy array:
my_dummy = np.random.random((6, 2, 2, 10))
print(my_dummy)
> [[[[0.6715164 0.58915908 0.36607568 0.73404715 0.69455375 0.52177771
0.91810873 0.85010461 0.37485212 0.35634401]
[0.55885052 0.13041019 0.89774818 0.3363019 0.66634638 0.32054576
0.46174629 0.59975141 0.02283781 0.02997967]]
....
]]]]
# Create random floats, based on channel 0 of my dummy:
random_floats = np.random.random(my_dummy.shape[0])
print(random_floats)
> [0.89351759 0.76734892 0.36810602 0.08513434 0.65511941 0.61297472]
# Create a mask with ones and a shape based on my_dummy:
my_mask = np.ones((my_dummy.shape[0], 1, 1, my_dummy.shape[-1]))
print(my_mask)
> [[[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]]
[[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]]
[[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]]
[[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]]
[[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]]
[[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]]]
# Initialize a rate parameter:
my_rate = 0.5
# Based on my_rate, change the array accordingly:
my_mask[my_rate > random_floats] = [1, 0, 1, 0, 1, 0, 1, 0, 1, 0]
print(my_mask)
[[[[1. 0. 1. 0. 1. 0. 1. 0. 1. 0.]]]
[[[1. 0. 1. 0. 1. 0. 1. 0. 1. 0.]]]
[[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]]
[[[1. 0. 1. 0. 1. 0. 1. 0. 1. 0.]]]
[[[1. 0. 1. 0. 1. 0. 1. 0. 1. 0.]]]
[[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]]]
# Multiply my_dummy with the new mask:
np.multiply(my_dummy, my_mask)
array([[[[0.6715164 , 0.58915908, 0.36607568, 0.73404715, 0.69455375,
0.52177771, 0.91810873, 0.85010461, 0.37485212, 0.35634401],
[0.55885052, 0.13041019, 0.89774818, 0.3363019 , 0.66634638,
0.32054576, 0.46174629, 0.59975141, 0.02283781, 0.02997967]],
[[0.22358676, 0.74959561, 0.11109368, 0.56021714, 0.2767754 ,
0.55156506, 0.15488703, 0.25738564, 0.18588607, 0.57593545],
[0.15804289, 0.87858207, 0.12890992, 0.78828551, 0.52467083,
0.45117698, 0.2605117 , 0.46659721, 0.855278 , 0.29630581]]],
[[[0.381445 , 0. , 0.48308211, 0. , 0.5136352 ,
0. , 0.84428703, 0. , 0.20532641, 0. ],
[0.696645 , 0. , 0.84184568, 0. , 0.01369105,
0. , 0.27683334, 0. , 0.59356542, 0. ]],
[[0.5281193 , 0. , 0.82336821, 0. , 0.63435181,
0. , 0.12824084, 0. , 0.35045286, 0. ],
[0.02205884, 0. , 0.22927706, 0. , 0.45538199,
0. , 0.81220918, 0. , 0.46427429, 0. ]]],
.....
]]]])
在tensorflow中，我这样做了（警告，很多导入，我尝试了很多事情，不再确定是否全部都是必要的，只是想确保可以立即重现）：
from keras.engine.base_layer import InputSpec
from tensorflow.python.util import deprecation
from tensorflow.python.framework import ops
from tensorflow.python.eager import context
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import tf_logging as logging
import numbers
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import math_ops
from keras import backend as K
# Create my_dummy and convert to tensor object:
my_dummy = np.random.random((6, 2, 2, 4))
my_dummy = ops.convert_to_tensor(my_dummy)
my_dummy.get_shape()
> TensorShape([Dimension(6), Dimension(2), Dimension(2), Dimension(4)])
# Create random floats, like before and inspect tensor with Keras (instead of running a tf session):
random_floats = random_ops.random_uniform([my_dummy.get_shape().as_list()[0]], dtype=my_dummy.dtype)
K.eval(random_floats)
> array([0.74018297, 0.76996447, 0.52047441, 0.28215968, 0.91457724,
0.64637448])
# Like before, create a mask with ones, like before shape (almost completely) based on my_dummy:
my_mask = tf.ones([my_dummy.get_shape()[0], 1, 1, my_dummy.get_shape()[-1]], dtype=x.dtype)
K.eval(my_mask)
> array([[[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]]])
不幸的是，这是我所坚持的。我没有找到一种基于比率值更改my_mask Tensor对象中条目的方法。我尝试过的一件事是tf.where：
tf.where(rate > random_floats, my_mask, tf.constant([1, 0, 1, 0, 1, 0, 1, 0, 1, 0], dtype = my_dummy.dtype))
但收到错误：
ValueError: Shapes must be equal rank, but are 4 and 1 for 'Select_1' (op: 'Select') with input shapes: [6], [6,1,1,10], [10].
感谢您的任何建议/帮助：）
答案 0 :(得分：1)
张量流基本上相同。为方便起见，显示较小的形状数据：
import tensorflow as tf
value_to_assign = tf.constant([[1., 0., 1., 0., 1.]])
rate = tf.constant(.5)
dummy = tf.random_normal(shape=(4, 1, 1, 5))
# random_floats = tf.random_normal(shape=(tf.shape(dummy)[0], ))
random_floats = tf.constant([0.4, 0.6, .7, .2]) # <--using const values to illustrate
init_val = tf.ones((tf.shape(dummy)[0], 1, 1, tf.shape(dummy)[-1]))
mask = tf.Variable(init_val,
trainable=False)
indices = tf.where(tf.equal(True, rate > random_floats))
tiled = tf.tile(value_to_assign,
multiples=[tf.shape(indices)[0], 1])[:, tf.newaxis, tf.newaxis, :]
mask = tf.scatter_nd_update(mask,
indices=indices,
updates=tiled)
res = mask * dummy
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print('MASK')
print(sess.run(mask))
print('DUMMY')
print(sess.run(dummy))
print('RESULT')
print(sess.run(res))
MASK
[[[[1. 0. 1. 0. 1.]]]
[[[1. 1. 1. 1. 1.]]]
[[[1. 1. 1. 1. 1.]]]
[[[1. 0. 1. 0. 1.]]]]
DUMMY
[[[[-1.2031308 -1.6657363 -1.5552464 0.8540495 0.37618718]]]
[[[-0.4468031 0.46417323 -0.3764856 1.1906835 -1.4670093 ]]]
[[[ 1.2066191 -1.4767337 -0.9487017 -0.49180242 -0.33098853]]]
[[[-0.1621628 0.61168176 0.10006899 0.7585997 -0.23903783]]]]
RESULT
[[[[ 1.7753109 0. -0.5451439 -0. -0.53782284]]]
[[[ 0.08024058 -1.8178499 1.183356 1.0895957 -0.9272436 ]]]
[[[-0.5266396 -2.0316153 -1.0043124 -1.1657876 0.6106227 ]]]
[[[-0.46503183 0. 0.01983969 -0. 0.58563703]]]]