estimators_blog_plot_weights.py

# Load the tensor with the model weights
weights = classifier.get_variable_value('linear/linear_model/x/weights').flatten()
# Find biggest weights in absolute value
extremes = np.concatenate((sorted_indexes[-8:], sorted_indexes[:8]))
# word_inverted_index is a dictionary that maps from indexes back to tokens
extreme_weights = sorted(
    [(weights[i], word_inverted_index[i]) for i in extremes])
# Create plot
y_pos = np.arange(len(extreme_weights))
plt.bar(y_pos, [pair[0] for pair in extreme_weights], align='center', alpha=0.5)
plt.xticks(y_pos, [pair[1] for pair in extreme_weights], rotation=45, ha='right')
plt.ylabel('Weight')
plt.title('Most significant tokens') 
plt.show()