Skip to content

Update lstm_seq2seq.py(from 22% to 87% acc) #13269

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 3 commits into from
Sep 11, 2019
Merged

Update lstm_seq2seq.py(from 22% to 87% acc) #13269

Changes from 1 commit
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
c846308
#13266 Update lstm_seq2seq.py(from 22% to 87% acc)
tykimos Aug 30, 2019
aa65ae7
Update lstm_seq2seq.py
tykimos Sep 3, 2019
552ffce
Update lstm_seq2seq.py
tykimos Sep 3, 2019
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Next Next commit
#13266 Update lstm_seq2seq.py(from 22% to 87% acc) 
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.
  • Loading branch information
@tykimos
tykimos authored Aug 30, 2019
commit c846308ad0d9cb894ac6e08f69baf949d00ee5e7
6 changes: 4 additions & 2 deletions examples/lstm_seq2seq.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -114,14 +114,16 @@
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.
if t > 0:
# 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)
Expand All @@ -145,7 +147,7 @@
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,
Expand Down