如何将tensorflow张量切片成多个

Question

张量为:Batch (3) * length(5) * dim(2)

tensor = tf.constant([[[1,1],[2,2],[3,3],[4,4],[5,5]],[[1,1],[2,2],[3,3],[4,4],[5,5]],[[1,1],[2,2],[3,3],[4,4],[5,5]]] )

，我想通过length_index[0,0]，[0,1]…[3,4]，[4,4]根据length_axis_index[0,1,2,3,4]，类似

的操作

spans_length=0
with tf.variable_scope("loss_span"):
output=[]
for i in range(0,1+n_spans):
for j in range(1,seq_length):
if j + i < seq_length:
res = tf.slice(output_layer_sequence, [0, j, 0], [-1, j+i-j+1, -1])
res = tf.reduce_sum(res,axis=1)
output.append(res)
# output = tf.convert_to_tensor(output)
spans_length+=1
output = tf.convert_to_tensor(output)
vsp = tf.transpose(output, [1,0,2])#batch , spans_length,hidden_size
vsp = tf.reshape(vsp,[-1,hidden_size])#batch * span_length,hidden_size
span_logits = tf.matmul(vsp, output_span_weight, transpose_b=True)  # output:[batch * spans_length,class_labels]
span_logits = tf.nn.bias_add(span_logits, output_span_bias)  # output:[batch * spans_length,class_labels]
span_matrix = tf.reshape(span_logits,[-1,spans_length,class_labels],name="span_matrix_val")#[batch , spans_length,class_labels]


label_span_logists = tf.one_hot(indices=label_span,depth=class_labels, on_value=1, off_value=0, axis=-1, dtype=tf.int32)
label_span_logists=tf.cast(label_span_logists,tf.int64)
span_loss = tf.nn.softmax_cross_entropy_with_logits(logits=span_matrix, labels=label_span_logists)
span_loss = tf.reduce_mean(span_loss, name='loss_span')

当我做这样的操作时，训练模型的时间很长，如何加快呢?谢谢

Answer 1

下面的代码可以运行:

# tensor = tf.constant([[[1,1],[2,2],[3,3],[4,4],[5,5]],[[1,1],[2,2],[3,3],[4,4],[5,5]],[[1,1],[2,2],[3,3],[4,4],[5,5]]] )
tensor = tf.random.uniform((3, 2000, 2))
length = tf.shape(tensor)[1].numpy()
output = []
for begins in range(length):
for size in range(length - begins):
res = tf.slice(tensor, [0, begins, 0], [-1, size + 1, -1])
res = tf.reduce_sum(res)
output.append(res)
output = tf.convert_to_tensor(output)

我尝试使用tf.scan()，但我没有看到任何好处:

output = tf.constant([], tf.int32)
for begins in range(length):
t = tensor[:, begins:, :]
t = tf.transpose(t, (1, 0, 2))
t = tf.scan(lambda a, x: a + x, t)
t = tf.transpose(t, (1, 0, 2))
t = tf.reduce_sum(t, [0, 2])
output = tf.concat([output, t], 0)

编辑:

尝试在预处理中沿未使用的维度[0,2]应用reduce_sum():

tensor = tf.reduce_sum(tensor, [0, 2])
output = tf.constant([])
for begins in range(length):
t = tensor[begins:]
t = tf.scan(lambda a, x: a + x, t)
output = tf.concat([output, t], 0)

仍然没有看到性能上的好处。

Answer 2

for i in range(0,50):
for j in range(1,200):
if j + i < 200:
res = tf.slice(output_layer_sequence, [0, j, 0], [-1, j+i-j+1, -1])
res = tf.reduce_sum(res,axis=1)
output.append(res)
output = tf.convert_to_tensor(output)

当我做这种操作时，训练时间很长，如何加快呢?谢谢