num_tags = 12 # Number of unique issue tags num_words = 10000 # Size of vocabulary obtained when preprocessing text data num_departments = 4 # Number of departments for predictions title_input = keras.Input( shape=(None,), name="title" ) # Variable-length sequence of ints body_input = keras.Input(shape=(None,), name="body") # Variable-length sequence of ints tags_input = keras.Input( shape=(num_tags,), name="tags" ) # Binary vectors of size `num_tags` # Embed each word in the title into a 64-dimensional vector title_features = layers.Embedding(num_words, 64)(title_input) # Embed each word in the text into a 64-dimensional vector body_features = layers.Embedding(num_words, 64)(body_input) # Reduce sequence of embedded words in the title into a single 128-dimensional vector title_features = layers.LSTM(128)(title_features) # Reduce sequence of embedded words in the body into a single 32-dimensional vector body_features = layers.LSTM(32)(body_features) # Merge all available features into a single large vector via concatenation x = layers.concatenate([title_features, body_features, tags_input]) # Stick a logistic regression for priority prediction on top of the features priority_pred = layers.Dense(1, name="priority")(x) # Stick a department classifier on top of the features department_pred = layers.Dense(num_departments, name="department")(x) # Instantiate an end-to-end model predicting both priority and department model = keras.Model( inputs=[title_input, body_input, tags_input], outputs=[priority_pred, department_pred], )
现在绘制模型： keras.utils.plot_model(model, "multi_input_and_output_model.png", show_shapes=True)