我想用Python写一个Op.本教程仅解释如何使用Python包装器在c ++中执行此操作. https://www.tensorflow.org/versions/master/how_tos/adding_an_op/index.html#adding-a-new-op
我怎样才能在Python中完全编写它？
我的问题和问题在两个代码块下面说明.
def loss(labels, logits, sequence_lengths, label_lengths, logit_lengths):
scores = []
for i in xrange(runner.batch_size):
sequence_length = sequence_lengths[i]
for j in xrange(length):
label_length = label_lengths[i, j]
logit_length = logit_lengths[i, j]
# get top k indices <==> argmax_k(labels[i, j, 0, :], label_length)
top_labels = np.argpartition(labels[i, j, 0, :], -label_length)[-label_length:]
top_logits = np.argpartition(logits[i, j, 0, :], -logit_length)[-logit_length:]
scores.append(edit_distance(top_labels, top_logits))
return np.mean(scores)
# Levenshtein distance
def edit_distance(s, t):
n = s.size
m = t.size
d = np.zeros((n+1, m+1))
d[:, 0] = np.arrange(n+1)
d[0, …python neural-network python-2.7 tensorflow recurrent-neural-network
我想要一种方法来降低TensorFlow(大约:截断尾数)中浮点数的精度到定义的整个范围内的任意位数.我不需要完全以降低的精度编写代码(如tf.float16),而是需要提出一系列操作来降低张量的精度,同时保留原始类型(例如tf.float32).
例如,如果整个范围是0到1,精度是8位,则0.1234将变为圆形(0.1234*256)/ 256 = 0.125.这使用简单的舍入.
我还想进行统计舍入,其中每个方向的舍入概率与该值的距离成正比.例如,0.1234*256 = 31.5904,这将在59%的时间内达到32/256,并且在41%的时间内达到31/256.
额外的问题:如何获取现有图表并修改它以在每次卷积后添加舍入？
I am using Keras with tensorflow backend and I am curious whether it is possible to skip a layer during backpropagation but have it execute in the forward pass. So here is what I mean
Lambda (lambda x: a(x))
I want to apply a to x in the forward pass but I do not want a to be included in the derivation when the backprop takes place.
我试图找到解决方案，但找不到任何东西。有人可以帮我吗？