Update readme and example script

README.md

@@ -8,7 +8,7 @@ This is a pytorch implementation of a sequence to sequence learning toolkit for
 
 # Requirements
 
-This library runs with PyTorch 0.3.0. We refer to the [PyTorch website](http://pytorch.org/) to install the right version for your environment.
+This library runs with PyTorch 0.4.0. We refer to the [PyTorch website](http://pytorch.org/) to install the right version for your environment.
 To install additional requirements (including numpy and torchtext), run:
 
 `pip install -r requirements.txt`
@@ -29,6 +29,7 @@ The script `train_model.py` can be used to train a new model, resume the trainin
     `python train_model.py --train $train_path --dev $dev_path --output_dir $expt_dir  --embedding_size 128 --hidden_size 256 --rnn_cell gru --epoch 20 
 
 Several options are available from the command line, including changing the optimizer, batch size, using attention/bidirectionality and using teacher forcing. 
+Additionally, models can be trained using *attentive guidance* [1](https://arxiv.org/abs/1805.09657), which can be provided in the training file.
 For a complete overview, use the *help* function of the script.
 
 ## Evaluation and inference
@@ -50,6 +51,7 @@ Once training is complete, you will be prompted to enter a new sequence to trans
     Input:  1 3 5 7 9
 	Expected output: 9 7 5 3 1 EOS
 
+Additional example scripts, as well as different datasets can be found in our auxiliary repository [machine-tasks](https://github.com/i-machine-think/machine-tasks).
 
 ## Checkpoints
 

example.sh

@@ -5,8 +5,8 @@ DEV_PATH=test/test_data/dev.txt
 EXPT_DIR=example
 
 # set values
-EMB_SIZE=128
-H_SIZE=128
+EMB_SIZE=16
+H_SIZE=64
 N_LAYERS=1
 CELL='gru'
 EPOCH=6
@@ -15,10 +15,10 @@ TF=0.5
 
 # Start training
 echo "Train model on example data"
-python train_model.py --train $TRAIN_PATH --dev $DEV_PATH --output_dir $EXPT_DIR --print_every $PRINT_EVERY --embedding_size $EMB_SIZE --hidden_size $H_SIZE --rnn_cell $CELL --n_layers $N_LAYERS --epoch $EPOCH --print_every $PRINT_EVERY --teacher_forcing $TF --bidirectional --attention
+python train_model.py --train $TRAIN_PATH --output_dir $EXPT_DIR --print_every $PRINT_EVERY --embedding_size $EMB_SIZE --hidden_size $H_SIZE --rnn_cell $CELL --n_layers $N_LAYERS --epoch $EPOCH --print_every $PRINT_EVERY --teacher_forcing $TF --attention 'pre-rnn' --attention_method 'mlp'
 
-echo "Evaluate model on test data"
-python evaluate.py --checkpoint_path $EXPT_DIR/$(ls -t $EXPT_DIR/ | head -1) --test_data $DEV_PATH
+echo "\n\nEvaluate model on test data"
+python evaluate.py --checkpoint_path $EXPT_DIR/$(ls -t $EXPT_DIR/ | head -1) --test_data $TRAIN_PATH
 
-echo "Run in inference mode"
+echo "\n\nRun in inference mode"
 python infer.py --checkpoint_path $EXPT_DIR/$(ls -t $EXPT_DIR/ | head -1) 

infer.py

@@ -49,6 +49,8 @@
     exit()
 
 while True:
-        seq_str = raw_input("Type in a source sequence:")
+        seq_str = raw_input("\n\nType in a source sequence: ")
+        if seq_str == 'q':
+            exit()
         seq = seq_str.strip().split()
         print(predictor.predict(seq))