sqrt_mc_h /= sqrt(N)returnsqrt_mc_h 开发者ID:f-dangel,项目名称:backpack,代码行数:23,代码来源:crossentropyloss.py 示例5: gen_step ▲点赞 6▼ # 需要导入模块: import torch [as 别名]# 或者: from torch importmultinomial[as 别名]defgen_step(self, src, rel, dst, n_sample=1, temperat...
loss = tf.nn.softmax_cross_entropy_with_logits_v2( labels=latents_discrete_hot, logits=latents_logits)# TODO(trandustin): tease this out from ae_latent_softmax.# we use just the loss portion to anchor prior / encoder on text.sample = multinomial_sample(latents_logits, vocab_size, hpar...
Source File: crossentropyloss.py From backpack with MIT License 6 votes def _sqrt_hessian_sampled(self,module, g_inp, g_out, mc_samples=1):self._check_2nd_order_parameters(module) M = mc_samples C =module.input0.shape[1] probs =...
第二个参数num_samples表示抽样的个数。 例如:tf.multinomial(tf.log([[0.01]]),3)不管重复运行多少次结果都是 [0,0,0]tf.multinomial(tf.log([[0.1, 0.6]]),3)结果可能 [0,0,0],也可能是[0,1,1],当然也有其他可能。
To avoid this loss of information we could, in principle, add an associated continuous node to the discrete node in the original BN. However, there are major practical and computational challenges involved in doing this. Supporting broader types of constraints. In this paper we have considered ...
loss = tf.nn.sparse_softmax_cross_entropy_with_logits( labels=latents_discrete, logits=latents_logits) sample = multinomial_sample( latents_logits, vocab_size, hparams.sampling_temp)returnsample, loss# Multi-block case.vocab_bits = int(math.log(vocab_size,2))assertvocab_size ==2**vocab_...
loss = tf.nn.softmax_cross_entropy_with_logits_v2( labels=latents_discrete_hot, logits=latents_logits) sample = multinomial_sample(latents_logits, vocab_size, hparams.sampling_method, hparams.sampling_temp)returnsample, loss 开发者ID:akzaidi,项目名称:fine-lm,代码行数:28,代码来源:latent_layer...
Thus, the MDPDEs provide increased robustness at a cost of slight loss in efficiency as 𝛼α increases. Please refer to references [34,35] for more details and examples. Due to various favorable properties of the MDPDE, it has now been extended to several important complex data structures ...
Such an agreement suggests that, although the number of categories was reduced in 1998 from five to three, the tests based on entropies cope with the loss of information. Table 7. p-values of the hypotheses of equal entropies using collapsed data in 1998. 6. Conclusions We presented ...
Moreover, Table 2 shows examples of the computed values of the average intrinsic dimension, the dimension of the NCA-based selected features, least loss, and best 𝜆λ values taken from 10 trials. Figure 7. Example of the average loss values versus 𝜆λ values computed from the reduced ...