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- def attention_map(self, text):
- """
- Text to visualze attention map for.
- """
- # encode the string
- d = self.input_vocab.string_to_int(text)
- # get the output sequence
- predicted_text = run_example(
- self.pred_model, self.input_vocab, self.output_vocab, text)
- text_ = list(text) + ['<eot>'] + ['<unk>'] * self.input_vocab.padding
- # get the lengths of the string
- input_length = len(text)+1
- output_length = predicted_text.index('<eot>')+1
- # get the activation map
- activation_map = np.squeeze(self.proba_model.predict(np.array([d])))[
- 0:output_length, 0:input_length]
- # import seaborn as sns
- plt.clf()
- f = plt.figure(figsize=(8, 8.5))
- ax = f.add_subplot(1, 1, 1)
- # add image
- i = ax.imshow(activation_map, interpolation='nearest', cmap='gray')
-
- # add colorbar
- cbaxes = f.add_axes([0.2, 0, 0.6, 0.03])
- cbar = f.colorbar(i, cax=cbaxes, orientation='horizontal')
- cbar.ax.set_xlabel('Probability', labelpad=2)
- # add labels
- ax.set_yticks(range(output_length))
- ax.set_yticklabels(predicted_text[:output_length])
-
- ax.set_xticks(range(input_length))
- ax.set_xticklabels(text_[:input_length], rotation=45)
-
- ax.set_xlabel('Input Sequence')
- ax.set_ylabel('Output Sequence')
- # add grid and legend
- ax.grid()
- # ax.legend(loc='best')
- f.savefig(os.path.join(HERE, 'attention_maps', text.replace('/', '')+'.pdf'), bbox_inches='tight')
- f.show()
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