所以，我有一個串列，其中包含從其他地方生成的一些種子。
我需要使用這個種子來創建一個隨機索引串列，然后創建一個新的張量，除了這些索引外，其他地方都是 0。
現在的代碼是這樣的：
import tensorflow as tf
size_for_layer = [800, 32, 51200, 64, 1605632, 512, 31744, 62] # TODO : Renderlo autonomo
size_for_layer_submodel = [150, 6, 1950, 13, 261170, 410, 25420, 62] # TODO : Renderlo autonomo
shape_for_layer = [[5, 5, 1, 32], [32], [5, 5, 32, 64], [64], [3136, 512], [512], [512, 62],
[62]] # TODO : Renderlo autonomo
if __name__ == '__main__':
seed = 1254 # Only for testing now
index = 1
# Tensor with X elements all 0s
tensor_testing = tf.zeros(size_for_layer[index], tf.float32)
# Tensor with Y random generated indices
indices = tf.random.uniform(shape=[size_for_layer_submodel[index], ], minval=0,
maxval=size_for_layer[index], dtype=tf.dtypes.int64, seed=seed, name=None)
values = tf.fill([tf.shape(indices)[0], ], 15.4)
print(tensor_testing)
print(indices)
print(values)
tensor_testing = tf.tensor_scatter_nd_update(tensor_testing, indices, values)
錯誤是這個：
tensorflow.python.framework.errors_impl.InvalidArgumentError: Inner dimensions of output shape must match inner dimensions of updates shape. Output: [32] updates: [6] [Op:TensorScatterUpdate]
uj5u.com熱心網友回復：
你真的很親近。像這樣的事情應該對你有用：
import tensorflow as tf
size_for_layer = [800, 32, 51200, 64, 1605632, 512, 31744, 62] # TODO : Renderlo autonomo
size_for_layer_submodel = [150, 6, 1950, 13, 261170, 410, 25420, 62] # TODO : Renderlo autonomo
shape_for_layer = [[5, 5, 1, 32], [32], [5, 5, 32, 64], [64], [3136, 512], [512], [512, 62],
[62]]
seed = 1254 # Only for testing now
index = 1
# Tensor with X elements all 0s
tensor_testing = tf.zeros(size_for_layer[index], tf.float32)
# Tensor with Y random generated indices
indices = tf.random.uniform(shape=[size_for_layer_submodel[index], ], minval=0,
maxval=size_for_layer[index], dtype=tf.dtypes.int64, seed=seed, name=None)
values = tf.fill([tf.shape(indices)[0], ], 15.4)
print(tensor_testing)
print(indices)
print(values)
tensor_testing = tf.tensor_scatter_nd_update(tensor_testing, tf.expand_dims(indices, 1), values)
print(tensor_testing)
tf.Tensor(
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0.], shape=(32,), dtype=float32)
tf.Tensor([ 9 4 17 16 28 7], shape=(6,), dtype=int64)
tf.Tensor([15.4 15.4 15.4 15.4 15.4 15.4], shape=(6,), dtype=float32)
tf.Tensor(
[ 0. 0. 0. 0. 15.4 0. 0. 15.4 0. 15.4 0. 0. 0. 0.
0. 0. 15.4 15.4 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
15.4 0. 0. 0. ], shape=(32,), dtype=float32)
更新：如果您想使用現有張量而不是tf.fill，請嘗試：
some_tensor = tf.random.uniform((32,), maxval=20)
tensor_testing = tf.tensor_scatter_nd_update(tensor_testing, tf.expand_dims(indices, 1), tf.gather(some_tensor, indices))
tf.Tensor(
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0.], shape=(32,), dtype=float32)
tf.Tensor([17 10 21 0 23 27], shape=(6,), dtype=int64)
tf.Tensor(
[19.655283 0.73073626 5.853808 10.993803 2.2646523 1.0580301
3.7602305 13.081946 10.444834 13.0695915 19.739437 13.987379
14.613118 14.325147 3.355515 15.57209 13.402302 17.103617
12.819632 7.440541 16.09658 1.3479114 1.6912937 0.5928588
13.784771 14.848431 18.457924 10.463061 13.597097 3.3686733
8.239708 16.517185 ], shape=(32,), dtype=float32)
tf.Tensor(
[19.655283 0. 0. 0. 0. 0.
0. 0. 0. 0. 19.739437 0.
0. 0. 0. 0. 0. 17.103617
0. 0. 0. 1.3479114 0. 0.5928588
0. 0. 0. 10.463061 0. 0.
0. 0. ], shape=(32,), dtype=float32)
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