Visualization for simple attention and Google's multi-head attention.

• python
• jdk1.8

1. Python version (for simple attention only):

python exec/plot_heatmap.py --input xxx.attention

2. Java version (for both simple attention and Google's multi-head attention):

java -jar exec/plot_heatmap.jar

then select the attention file on the GUI.

The name of the attention file should end with ".attention" extension especially when using exec/plot_heatmap.jar and the file should be json format:

{ "0": {
    "source": " ",       # the source sentence (without <s> and </s> symbols)
    "translation": " ",      # the target sentence (without <s> and </s> symbols)
    "attentions": [         # various attention results
        {
            "name": " ",        # a unique name for this attention
            "type": " ",        # the type of this attention (simple or multihead)
            "value": [...]      # the attention weights, a json array
        },      # end of one attention result
        {...}, ...]     # end of various attention results
    },   # end of the first sample
  "1":{
        ...
    },       # end of the second sample
    ...
}       # end of file

Note that due to the hard coding, the name of each attention should contain "encoder_decoder_attention", "encoder_self_attention" or "decoder_self_attention" substring on the basis of its real meaning.

The value has shape [length_queries, length_keys] when type=simple and has shape [num_heads, length_queries, length_keys] when type=multihead.

For more details, see attention.py.

The toydata/toy.attention is generated by a NMT model with a self-attention encoder, Bahdanau's attention and a RNN decoder using NJUNMT-tf.

1. Execute the python version (for simple attention only):

python exec/plot_heatmap.py --input toydata/toy.attention

It will plot the traditional attention heatmap:

2. As for java version (for both simple attention and Google's multihead attention), execute

java -jar exec/plot_heatmap.jar

then select the toydata/toy.attention on the GUI.

The words on the left side are attention "queries" and attention "keys" are on the right. Click on the words on the left side to see the heatmap:

Here shows the traditional encoder_decoder_attention of word "obtained". The color depth of lines and squares indicate the degree of attention.

Next, select encoder_self_attention0 under the menu bar. Click on the "获得" on the left.

It shows the multi-head attention of the word "获得". Attention weights of head0 - head7 are displayed on the right.