Update lstm_seq2seq.py(from 22% to 87% acc)
diff --git a/examples/lstm_seq2seq.py b/examples/lstm_seq2seq.py
index 88fa5768858..e320fd1bf85 100644
--- a/examples/lstm_seq2seq.py
+++ b/examples/lstm_seq2seq.py
@@ -114,6 +114,7 @@
 for i, (input_text, target_text) in enumerate(zip(input_texts, target_texts)):
     for t, char in enumerate(input_text):
         encoder_input_data[i, t, input_token_index[char]] = 1.
+    encoder_input_data[i, t + 1:, input_token_index[' ']] = 1.
     for t, char in enumerate(target_text):
         # decoder_target_data is ahead of decoder_input_data by one timestep
         decoder_input_data[i, t, target_token_index[char]] = 1.
@@ -121,7 +122,8 @@
             # decoder_target_data will be ahead by one timestep
             # and will not include the start character.
             decoder_target_data[i, t - 1, target_token_index[char]] = 1.
-
+    decoder_input_data[i, t + 1:, target_token_index[' ']] = 1.
+    decoder_target_data[i, t:, target_token_index[' ']] = 1.
 # Define an input sequence and process it.
 encoder_inputs = Input(shape=(None, num_encoder_tokens))
 encoder = LSTM(latent_dim, return_state=True)
@@ -145,7 +147,8 @@
 model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
 
 # Run training
-model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
+model.compile(optimizer='rmsprop', loss='categorical_crossentropy',
+              metrics=['accuracy'])
 model.fit([encoder_input_data, decoder_input_data], decoder_target_data,
           batch_size=batch_size,
           epochs=epochs,

I added the codes for applying one-hot encoding on the end of sentences about encoder_input_data, decoder_input_data, and decoder_target_data. I added an accuracy metric for model training. The original code has 22% accuracy, but the proposed code had 87% validation accuracy.

<!--
Please make sure you've read and understood our contributing guidelines;
https://github.com/keras-team/keras/blob/master/CONTRIBUTING.md
-->

### Summary
Applying one-hot encoding on the end of sentences

### Related Issues
#13266

### PR Overview

- [n] This PR requires new unit tests [y/n] (make sure tests are included)
- [n] This PR requires to update the documentation [y/n] (make sure the docs are up-to-date)
- [y] This PR is backwards compatible [y/n]
- [n] This PR changes the current API [y/n] (all API changes need to be approved by fchollet)
