question about accuracy about tensorflow-examples HOT 5 OPEN

I am very confused with the terms batch_size and n_steps. Does the lstm updates his parameters after n_steps ? In the recurrent network, the lstm is feed with a n_steps list of [batch_size,n_input]. So, in the case of the MNIST classification, the cell is feed with 128 samples of 28pixels every step in range(n_steps) ??

from tensorflow-examples.

It is working fine because it is using argmax of pred. Last layer of RNN example is a 10 dimension vector (for every label). So the predicted class is represented by that layer highest value index (that you get with argmax). Softmax is just used to squash values between 0 and 1.
Softmax is used while training because having a normalized probabilities distribution may help for better performances, but when predicting, softmax is actually not needed.

from tensorflow-examples.

It is hard to say without knowing your data, because network structure is also dependent of your data. If you are parsing words (ids), then your need to add an embedding layer.

from tensorflow-examples.

I also have a question to recurrent network. The RNN function returns only the last model output. return tf.matmul(outputs[-1], weights['out']) + biases['out'] which means the loss is also only calculated from the last output. One example in the Tensorflow repo (ptb_word_lm) uses the every output, not only the output of the last step. What is the right approach, or on what does it depend?

from tensorflow-examples.

It is because seq2seq is an encoding/decoding process that output a sequence, so calculating loss for every output is important. However in our example, we are simply doing classification over a whole sequence with a single output (predicted class), so only the last output is meaningful (which is the output after all timesteps have been 'processed').

from tensorflow-examples.