我想在張量流中進行切片分配。我知道我可以使用：my_var = my_var[4:8].assign(tf.zeros(4))基于此鏈接。正如您在中看到的，my_var[4:8]我們在這里有特定的索引 4、8 用于切片然后分配。我的情況不同，我想根據張量進行切片，然后進行分配。out = tf.Variable(tf.zeros(shape=[8,4], dtype=tf.float32)) rows_tf = tf.constant ([[1, 2, 5], [1, 2, 5], [1, 2, 5], [1, 4, 6], [1, 4, 6], [2, 3, 6], [2, 3, 6], [2, 4, 7]])columns_tf = tf.constant([[1], [2], [3], [2], [3], [2], [3], [2]])changed_tensor = [[8.3356, 0., 8.457685 ], [0., 6.103182, 8.602337 ], [8.8974, 7.330564, 0. ], [0., 3.8914037, 5.826657 ], [8.8974, 0., 8.283971 ], [6.103182, 3.0614321, 5.826657 ], [7.330564, 0., 8.283971 ], [6.103182, 3.8914037, 0. ]]此外，這是sparse_indices張量，它是需要更新的整個索引的連接rows_tf和制作（以防它可以提供幫助:)columns_tfsparse_indices = tf.constant([[1 1] [2 1] [5 1] [1 2] [2 2] [5 2] [1 3] [2 3] [5 3] [1 2] [4 2] [6 2] [1 3] [4 3] [6 3] [2 2] [3 2] [6 2] [2 3] [3 3] [6 3] [2 2] [4 2] [4 2]])我想做的是做這個簡單的任務：out[rows_tf, columns_tf] = changed_tensor為此，我正在這樣做：out[rows_tf:column_tf].assign(changed_tensor)但是，我收到了這個錯誤：tensorflow.python.framework.errors_impl.InvalidArgumentError: Expected begin, end, and strides to be 1D equal size tensors, but got shapes [1,8,3], [1,8,1], and [1] instead. [Op:StridedSlice] name: strided_slice/這是預期的輸出：[[0. 0. 0. 0. ] [0. 8.3356 0. 8.8974 ] [0. 0. 6.103182 7.330564 ] [0. 0. 3.0614321 0. ] [0. 0. 3.8914037 0. ] [0. 8.457685 8.602337 0. ] [0. 0. 5.826657 8.283971 ] [0. 0. 0. 0. ]]知道如何完成這個任務嗎？先感謝您：）
1 回答
哈士奇WWW
TA貢獻1799條經驗 獲得超6個贊
tf.scatter_nd_update 此示例（從此處的 tf 文檔擴展）應該有所幫助。
您想首先將您的 row_indices 和 column_indices 組合成一個二維索引列表，這indices是tf.scatter_nd_update. 然后你輸入了一個期望值列表，即updates.
ref = tf.Variable(tf.zeros(shape=[8,4], dtype=tf.float32))
indices = tf.constant([[0, 2], [2, 2]])
updates = tf.constant([1.0, 2.0])
update = tf.scatter_nd_update(ref, indices, updates)
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
print sess.run(update)
Result:
[[ 0. 0. 1. 0.]
[ 0. 0. 0. 0.]
[ 0. 0. 2. 0.]
[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]]
專門針對您的數據，
ref = tf.Variable(tf.zeros(shape=[8,4], dtype=tf.float32))
changed_tensor = [[8.3356, 0., 8.457685 ],
[0., 6.103182, 8.602337 ],
[8.8974, 7.330564, 0. ],
[0., 3.8914037, 5.826657 ],
[8.8974, 0., 8.283971 ],
[6.103182, 3.0614321, 5.826657 ],
[7.330564, 0., 8.283971 ],
[6.103182, 3.8914037, 0. ]]
updates = tf.reshape(changed_tensor, shape=[-1])
sparse_indices = tf.constant(
[[1, 1],
[2, 1],
[5, 1],
[1, 2],
[2, 2],
[5, 2],
[1, 3],
[2, 3],
[5, 3],
[1, 2],
[4, 2],
[6, 2],
[1, 3],
[4, 3],
[6, 3],
[2, 2],
[3, 2],
[6, 2],
[2, 3],
[3, 3],
[6, 3],
[2, 2],
[4, 2],
[4, 2]])
update = tf.scatter_nd_update(ref, sparse_indices, updates)
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
print sess.run(update)
Result:
[[ 0. 0. 0. 0. ]
[ 0. 8.3355999 0. 8.8973999 ]
[ 0. 0. 6.10318184 7.33056402]
[ 0. 0. 3.06143212 0. ]
[ 0. 0. 0. 0. ]
[ 0. 8.45768547 8.60233688 0. ]
[ 0. 0. 5.82665682 8.28397083]
[ 0. 0. 0. 0. ]]
添加回答
舉報
0/150
提交
取消