tf.tensorから1つの操作で複数の列を抽出します

1.tf.gather(tensor, columns, axis=1)（TF1.x、TF2）の使用：

import tensorflow as tf

tensor = tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=tf.float32)
columns = [1, 3]

print(tf.gather(tensor, columns, axis=1).numpy())
%timeit -n 10000 tf.gather(tensor, columns, axis=1)
# [[2. 4.]
#  [6. 8.]]

82.6 µs ± 5.76 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

2.インデックス付き（TF1.x、TF2）：

import tensorflow as tf

tensor = tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=tf.float32)
columns = [1, 3] # <--columns you want to extract

transposed = tf.transpose(tensor)
sliced = [transposed[c] for c in columns]
stacked = tf.transpose(tf.stack(sliced, axis=0))
# print(stacked.numpy()) # <-- TF2, TF1.x-eager

with tf.Session() as sess:  # <-- TF1.x
    print(sess.run(stacked))
# [[2. 4.]
#  [6. 8.]]

それを関数にラップして実行%timeitしtf.__version__=='2.0.0-alpha0'ます：

154 µs ± 2.61 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

それを飾ること@tf.functionは2倍以上速いです：

import tensorflow as tf
tensor = tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=tf.float32)
columns = [1, 3] # <--columns you want to extract
@tf.function
def extract_columns(tensor=tensor, columns=columns):
    transposed = tf.transpose(tensor)
    sliced = [transposed[c] for c in columns]
    stacked = tf.transpose(tf.stack(sliced, axis=0))
    return stacked

%timeit -n 10000 extract_columns()

66.8 µs ± 2.03 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

3.熱心な実行のためのワンライナー（TF2、TF1.x-eager）：

import tensorflow as tf

tensor = tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=tf.float32)
columns = [1, 3] # <--columns you want to extract

res = tf.transpose(tf.stack([t for i, t in enumerate(tf.transpose(tensor))
                             if i in columns], 0))
print(res.numpy())
# [[2. 4.]
#  [6. 8.]]

%timeitでtf.__version__=='2.0.0-alpha0'：

242 µs ± 2.97 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

4.を使用tf.one_hot()して行/列を指定し、次にtf.boolean_mask()これらの行/列を抽出します（TF1.x、TF2）：

import tensorflow as tf

tensor = tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=tf.float32)
columns = [1, 3] # <--columns you want to extract

mask = tf.one_hot(columns, tensor.get_shape().as_list()[-1])
mask = tf.reduce_sum(mask, axis=0)
res = tf.transpose(tf.boolean_mask(tf.transpose(tensor), mask))
# print(res.numpy()) # <-- TF2, TF1.x-eager

with tf.Session() as sess: # TF1.x
    print(sess.run(res))
# [[2. 4.]
#  [6. 8.]]

%timeitでtf.__version__=='2.0.0-alpha0'：

494 µs ± 4.01 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)