nmt | machine translation with open license | Translation library

According to the documentation: layers.Embedding: the largest integer in the input should be smaller than the vocabulary size / input_dim.

input_dim: Integer. Size of the vocabulary, i.e. maximum integer index + 1.

That's why

Buying new PN532 NFC RFID Module solved the issue.

Your PRINT token can only be matched by the blk_expr rule through this path:

There is no path for retblock_expr to recognize anything that begins with the PRINT token.

As a result, it will not matter which order you have elk_expr or retblock_expr.

There is no parser rule in your grammar that will match a PRINT token followed by a LPR token. a block_expr is matched by the program rule, and it only matches (ignoring wsp) block_expr or retblock_expr. Neither of these have alternatives that begin with an LPR token, so ANTLR can't match that token.

print(...) would normally be matched as a function call expression that accepts 0 or more comma-separated parameters. You have no sure rule/alternative defined. (I'd guess that it should be an alternative on either retblock_expr or block_expr

That's the immediate cause of this error. ANTLR really does not have any rule/alternative that can accept a LPR token in this position.

Both global and a are listed in your grammer under kwr rule.

kwr is mentioned in the inl rule which isn't used anywhere. So your parser don't know how to deal with inl and don't know what to do with two inl chained together (global a)

@user7331538 try replace path=f os.path.join(self.dir, 'model_{}'.format(self.timestamp)) with path='anymodel_name.h5'

Finally I have resolved the issue. I am using a third-party-attention layer by Thushan Ganegedara. Used it's Attentionlayer class. And integrated that in my architecture as following.

You are aware that the training is non-deterministic anyway, right? Did you try to rerun each case a couple of times? Also the baseline? Maybe the baseline itself is an outlier.

Also, changing the computation graph, even if this will be a no-op, can also have an effect. Unfortunately it can be sensitive.

You might want to try setting deterministic_train = True in your config. This might make it a bit more deterministic. Maybe you get the same result then in each of your cases. This might make it a bit slower, though.

The order of parameter initialization might be different as well. The order depends on the order of when the layers are created. Maybe compare that in the log. It is always the same random initializer, but would use a different seed offset then, so you would get another initialization. You could play around by explicitly setting random_seed in the config, and see how much variance you get by that. Maybe all these values are within this range.

For a more in-depth debugging, you could really compare directly the computation graph (in TensorBoard). Maybe there is a difference which you did not notice. Also, maybe make a diff on the log output during net construction, for the case pretrain vs baseline. There should be no diff.

(As this is maybe a mistake, for now only as a side comment: Of course, different RETURNN versions might have some different behavior. So this should be the same.)

Another note: You do not need this tf.reduce_sum in your loss. Actually that might not be such a good idea. Now it will forget about number of frames, and number of seqs. If you just do not use tf.reduce_sum, it should also work, but now you get the correct normalization.

Another note: Instead of your lambda, you can also use loss_scale, which is simpler, and you get the original value in the log.

So basically, you could write it this way:

A 503 error implies that this issue is on Google's side, which leads me to believe you're possibly getting rate limited. As Raphael mentioned, is there a Retry-After header in the response? I recommend taking a look into the response headers as it'll likely tell you what's going on more specifically, and possibly give you info on how to fix it.

• first, A typical memory representation of C program consists of following sections. (https://www.geeksforgeeks.org/memory-layout-of-c-program/)