dr2 = seq(3,ncol(merged_df4wc),by = 6),如何查看dr2中有几个数值
时间: 2024-02-22 11:57:31 浏览: 12
要查看dr2中有几个数值,可以使用length()函数,将dr2作为参数传入该函数即可。具体命令如下:
```
length(dr2)
```
执行该命令后,输出的结果即为dr2中数值的个数。
相关问题
losses = tensorflow.leagcy_seq2seq.sequence_loss_by_example
抱歉,你的代码中存在一些错误。正确的函数名称是 `tf.contrib.legacy_seq2seq.sequence_loss_by_example` 而不是 `tensorflow.leagcy_seq2seq.sequence_loss_by_example`。此外,`tf.contrib` 模块已经被废弃,建议使用新的模块 `tf.compat.v1`。因此,正确的代码应该是这样的:
```
import tensorflow as tf
# Define your logits and targets
losses = tf.compat.v1.nn.seq2seq.sequence_loss_by_example(
logits=[logits],
targets=[targets],
weights=[weights]
)
# Compute the average loss
cost = tf.reduce_mean(losses)
```
请注意,你需要将 `logits`、`targets` 和 `weights` 替换为你实际使用的张量。
losses = tf.contrib.legacy_seq2seq.sequence_loss_by_example
(loss_weights=weights, logits=logits, targets=target_sequence)
This function calculates the sequence loss for a sequence-to-sequence model. It calculates the weighted cross-entropy loss for each element of the output sequence compared to the corresponding element of the target sequence. The loss_weights parameter is a list of weights for each element of the output sequence. The logits parameter is a tensor of shape [batch_size, sequence_length, vocabulary_size], representing the output sequence of the model. The targets parameter is a tensor of shape [batch_size, sequence_length], representing the target sequence. The function returns a tensor of shape [batch_size], representing the loss for each sequence in the batch.
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