Multiple outputs in Keras

Question

I have a problem which deals with predicting two outputs when given a vector of predictors. Assume that a predictor vector looks like x1, y1, att1, att2, ..., attn, which says x1, y1 are coordinates and att's are the other attributes attached to the occurrence of x1, y1 coordinates. Based on this predictor set I want to predict x2, y2. This is a time series problem, which I am trying to solve using multiple regresssion. My question is how do I setup keras, which can give me 2 outputs in the final layer.

Answer 1

from keras.models import Model from keras.layers import *      #inp is a "tensor", that can be passed when calling other layers to produce an output  inp = Input((10,)) #supposing you have ten numeric values as input    #here, SomeLayer() is defining a layer,  #and calling it with (inp) produces the output tensor x x = SomeLayer(blablabla)(inp)  x = SomeOtherLayer(blablabla)(x) #here, I just replace x, because this intermediate output is not interesting to keep   #here, I want to keep the two different outputs for defining the model #notice that both left and right are called with the same input x, creating a fork out1 = LeftSideLastLayer(balbalba)(x)     out2 = RightSideLastLayer(banblabala)(x)   #here, you define which path you will follow in the graph you've drawn with layers #notice the two outputs passed in a list, telling the model I want it to have two outputs. model = Model(inp, [out1,out2]) model.compile(optimizer = ...., loss = ....) #loss can be one for both sides or a list with different loss functions for out1 and out2      model.fit(inputData,[outputYLeft, outputYRight], epochs=..., batch_size=...) 

Answer 2

You can make a model with multiple output with

1. the Functional API

2. by subclassing tf.keras.Model.

Here's an example of dual outputs (regression and classification) on the Iris Dataset, using the Functional API:

from sklearn.datasets import load_iris from tensorflow.keras.layers import Dense from tensorflow.keras import Input, Model import tensorflow as tf  data, target = load_iris(return_X_y=True) X = data[:, (0, 1, 2)] Y = data[:, 3] Z = target  inputs = Input(shape=(3,), name='input') x = Dense(16, activation='relu', name='16')(inputs) x = Dense(32, activation='relu', name='32')(x) output1 = Dense(1, name='cont_out')(x) output2 = Dense(3, activation='softmax', name='cat_out')(x)  model = Model(inputs=inputs, outputs=[output1, output2])  model.compile(loss={'cont_out': 'mean_absolute_error',                      'cat_out': 'sparse_categorical_crossentropy'},               optimizer='adam',               metrics={'cat_out': tf.metrics.SparseCategoricalAccuracy(name='acc')})  history = model.fit(X, {'cont_out': Y, 'cat_out': Z}, epochs=10, batch_size=8) 

Here's a simplified version:

from sklearn.datasets import load_iris from tensorflow.keras.layers import Dense from tensorflow.keras import Input, Model  data, target = load_iris(return_X_y=True) X = data[:, (0, 1, 2)] Y = data[:, 3] Z = target  inputs = Input(shape=(3,)) x = Dense(16, activation='relu')(inputs) x = Dense(32, activation='relu')(x) output1 = Dense(1)(x) output2 = Dense(3, activation='softmax')(x)  model = Model(inputs=inputs, outputs=[output1, output2])  model.compile(loss=['mae', 'sparse_categorical_crossentropy'], optimizer='adam')  history = model.fit(X, [Y, Z], epochs=10, batch_size=8) 

Here's the same example, subclassing tf.keras.Model and with a custom training loop:

import tensorflow as tf from tensorflow.keras.layers import Dense from tensorflow.keras import Model from sklearn.datasets import load_iris tf.keras.backend.set_floatx('float64') iris, target = load_iris(return_X_y=True)  X = iris[:, :3] y = iris[:, 3] z = target  ds = tf.data.Dataset.from_tensor_slices((X, y, z)).shuffle(150).batch(8)  class MyModel(Model):     def __init__(self):         super(MyModel, self).__init__()         self.d0 = Dense(16, activation='relu')         self.d1 = Dense(32, activation='relu')         self.d2 = Dense(1)         self.d3 = Dense(3, activation='softmax')      def call(self, x, training=None, **kwargs):         x = self.d0(x)         x = self.d1(x)         a = self.d2(x)         b = self.d3(x)         return a, b  model = MyModel()  loss_obj_reg = tf.keras.losses.MeanAbsoluteError() loss_obj_cat = tf.keras.losses.SparseCategoricalCrossentropy()  optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3)  loss_reg = tf.keras.metrics.Mean(name='regression loss') loss_cat = tf.keras.metrics.Mean(name='categorical loss')  error_reg = tf.keras.metrics.MeanAbsoluteError() error_cat = tf.keras.metrics.SparseCategoricalAccuracy()  @tf.function def train_step(inputs, y_reg, y_cat):     with tf.GradientTape() as tape:         pred_reg, pred_cat = model(inputs)         reg_loss = loss_obj_reg(y_reg, pred_reg)         cat_loss = loss_obj_cat(y_cat, pred_cat)      gradients = tape.gradient([reg_loss, cat_loss], model.trainable_variables)     optimizer.apply_gradients(zip(gradients, model.trainable_variables))     loss_reg(reg_loss)     loss_cat(cat_loss)      error_reg(y_reg, pred_reg)     error_cat(y_cat, pred_cat)   for epoch in range(50):     for xx, yy, zz in ds:         train_step(xx, yy, zz)      template = 'Epoch {:>2}, SCCE: {:>5.2f},' \                ' MAE: {:>4.2f}, SAcc: {:>5.1%}'     print(template.format(epoch+1,                         loss_cat.result(),                         error_reg.result(),                         error_cat.result()))      loss_reg.reset_states()     loss_cat.reset_states()      error_reg.reset_states()     error_cat.reset_states()