Tensorflow常用函数笔记

Tensorflow常用函数笔记

---

tf.concat

把一组向量从某一维上拼接起来，很向numpy中的Concatenate，官网例子：

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]

其实，如果是list类型的话也是可以的，只要是形似Tensor，最后tf.concat返回的还是Tensor类型

tf.gather

类似于数组的索引，可以把向量中某些索引值提取出来，得到新的向量，适用于要提取的索引为不连续的情况。这个函数似乎只适合在一维的情况下使用。

import tensorflow as tf 

a = tf.Variable([[1,2,3,4,5], [6,7,8,9,10], [11,12,13,14,15]])
index_a = tf.Variable([0,2])

b = tf.Variable([1,2,3,4,5,6,7,8,9,10])
index_b = tf.Variable([2,4,6,8])

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
print(sess.run(tf.gather(a, index_a)))
print(sess.run(tf.gather(b, index_b)))

#  [[ 1 2 3 4 5]
# [11 12 13 14 15]]

#  [3 5 7 9]

tf.gather_nd

同上，但允许在多维上进行索引，例子只展示了一种很简单的用法，更复杂的用法可见官网。

import tensorflow as tf 

a = tf.Variable([[1,2,3,4,5], [6,7,8,9,10], [11,12,13,14,15]])
index_a = tf.Variable([[0,2], [0,4], [2,2]])

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
print(sess.run(tf.gather_nd(a, index_a)))

#  [ 3  5 13]

tf.greater

判断函数。首先张量x和张量y的尺寸要相同，输出的tf.greater(x, y)也是一个和x，y尺寸相同的张量。如果x的某个元素比y中对应位置的元素大，则tf.greater(x, y)对应位置返回True，否则返回False。与此类似的函数还有tf.greater_equal。

import tensorflow as tf 

x = tf.Variable([[1,2,3], [6,7,8], [11,12,13]])
y = tf.Variable([[0,1,2], [5,6,7], [10,11,12]])

x1 = tf.Variable([[1,2,3], [6,7,8], [11,12,13]])
y1 = tf.Variable([[10,1,2], [15,6,7], [10,21,12]])

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
print(sess.run(tf.greater(x, y)))
print(sess.run(tf.greater(x1, y1)))

#  [[ True True True]
# [ True True True]
# [ True True True]]

#  [[False True True]
# [False True True]
# [ True False True]]

tf.cast

转换数据类型。

a = tf.constant([0, 2, 0, 4, 2, 2], dtype='int32')
print(a)
# <tf.Tensor 'Const_1:0' shape=(6,) dtype=int32>

b = tf.cast(a, 'float32')
print(b)
# <tf.Tensor 'Cast:0' shape=(6,) dtype=float32>

tf.expand_dims & tf.squeeze

增加 / 压缩张量的维度。

a = tf.constant([0, 2, 0, 4, 2, 2], dtype='int32')
print(a)
# <tf.Tensor 'Const_1:0' shape=(6,) dtype=int32>

b = tf.expand_dims(a, 0)
print(b)
# <tf.Tensor 'ExpandDims:0' shape=(1, 6) dtype=int32>

print(tf.squeeze(b, 0))
# <tf.Tensor 'Squeeze:0' shape=(6,) dtype=int32>