Keras Functional API and loss function with multiple inputs

Question

I am trying to use a custom Keras loss function that apart from the usual signature (y_true, y_pred) takes another parameter sigma (which is also produced by the last layer of the network). The training works fine, but then I am not sure how to perform forward propagation and return sigma (while muis the output of the model.predict method). This is the code I am using, which features a custom layer GaussianLayer that returns the list [mu, sigma].

import tensorflow as tf
from keras import backend as K
from keras.layers import Input, Dense, Layer, Dropout
from keras.models import Model
from keras.initializers import glorot_normal
import numpy as np

def custom_loss(sigma):
    def gaussian_loss(y_true, y_pred):
        return tf.reduce_mean(0.5*tf.log(sigma) + 0.5*tf.div(tf.square(y_true - y_pred), sigma)) + 10
    return gaussian_loss

class GaussianLayer(Layer):

    def __init__(self, output_dim, **kwargs):
        self.output_dim = output_dim
        super(GaussianLayer, self).__init__(**kwargs)

    def build(self, input_shape):
        self.kernel_1 = self.add_weight(name='kernel_1', 
                                      shape=(30, self.output_dim),
                                      initializer=glorot_normal(),
                                      trainable=True)
        self.kernel_2 = self.add_weight(name='kernel_2', 
                                      shape=(30, self.output_dim),
                                      initializer=glorot_normal(),
                                      trainable=True)
        self.bias_1 = self.add_weight(name='bias_1',
                                    shape=(self.output_dim, ),
                                    initializer=glorot_normal(),
                                    trainable=True)
        self.bias_2 = self.add_weight(name='bias_2',
                                    shape=(self.output_dim, ),
                                    initializer=glorot_normal(),
                                    trainable=True)
        super(GaussianLayer, self).build(input_shape) 

    def call(self, x):
        output_mu  = K.dot(x, self.kernel_1) + self.bias_1
        output_sig = K.dot(x, self.kernel_2) + self.bias_2
        output_sig_pos = K.log(1 + K.exp(output_sig)) + 1e-06  
        return [output_mu, output_sig_pos]

    def compute_output_shape(self, input_shape):
        return [(input_shape[0], self.output_dim), (input_shape[0], self.output_dim)]

# This returns a tensor
inputs = Input(shape=(1,))
x = Dense(30, activation='relu')(inputs)
x = Dropout(0.3)(x)
x = Dense(30, activation='relu')(x)
x = Dense(40, activation='relu')(x)
x = Dropout(0.3)(x)
x = Dense(30, activation='relu')(x)
mu, sigma = GaussianLayer(1)(x)

model = Model(inputs, mu)
model.compile(loss=custom_loss(sigma), optimizer='adam')
model.fit(train_x, train_y, epochs=150)

user_2099607 · Answer

Since your model returns two tensors as output, you also need to pass a list of two arrays as the output when calling fit() method. That's essentially what the error is trying to convey:

Error when checking model target:

So the error is in targets (i.e. labels). What is wrong?

the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 2 array(s), but instead got the following list of 1 arrays:

user_1958843 · Answer

I may have found the answer among Keras FAQs. I found out that it is possible to retrieve intermediate steps' output using the code snippet below:

layer_name = 'main_output'
intermediate_layer_model = Model(inputs=model.input,
                                 outputs=model.get_layer(layer_name).output)
intermediate_output = intermediate_layer_model.predict(train_x[0])
intermediate_output

In this case intermediate_output is a list of two values [mu, sigma] (just needed to name the output layer main_output and retrieve it later)

Keras Functional API and loss function with multiple inputs

2 Answers