Custom layer with different activation function for each output

Question

I have a simple neural network with two outputs and for each of them I need to use different activation function. I do basically what is written in this article - here, but it looks like my layer with different activation functions is not working:

See my code below:

X = filled_df.loc[:, "SOUTEZ_MEAN_HOME":"TOTAL_POINTS_AWAY"].values
y = filled_df.loc[:, "HOME_YELLOW_CARDS"].values

X= X.astype("float32")
y= y.astype("float32")

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.3)

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train= scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

def negative_binomial_layer(x):

    # Get the number of dimensions of the input
    num_dims = len(x.get_shape())
    
    # Separate the parameters
    n, p = tf.unstack(x, num=2, axis=-1)

    
    # Add one dimension to make the right shape
    n = tf.expand_dims(n, -1)
    p = tf.expand_dims(p, -1)
        
    # Apply a softplus to make positive
    n = tf.cast(n, tf.float32)
    p = tf.cast(p, tf.float32)
    n = tf.keras.activations.softplus(n)
    
    # Apply a sigmoid activation to bound between 0 and 1
    p = tf.keras.activations.sigmoid(p)

    # Join back together again
    out_tensor = tf.concat((n, p), axis=num_dims-1)

    return out_tensor 


input_shape = (212, )

# Define inputs with predefined shape
inputs = Input(shape=input_shape)

# Build network with some predefined architecture
Layer1 = Dense(16)
Layer2 = Dense(8)

output1 = Layer1(inputs)
output2 = Layer2(output1)

# Predict the parameters of a negative binomial distribution
outputs = Dense(2)(output2)
#outputs = tf.cast(outputs, tf.float32)
distribution_outputs = Lambda(negative_binomial_layer)(outputs)


# Construct model
model = Model(inputs=inputs, outputs=outputs)

num_epochs = 10
opt = Adam()
model.compile(loss = negative_binomial_loss, optimizer = opt)

history = model.fit(X_train, y_train, epochs = num_epochs,
                    validation_data = (X_test, y_test))

These are my predicted values if I print y_pred in custom loss function:

Epoch 1/10
y_pred =  [[2.19472528 3.14479065]
 [-1.16056371 1.69369149]
 [-1.12327099 2.06830978]
 ...
 [-1.23587477 4.82307]
 [0.235431105 3.86740351]
 [-2.75554061 1.10352468]] [[[2.19472528 3.14479065]
 [-1.16056371 1.69369149]
 [-1.12327099 2.06830978]
 ...
 [-1.23587477 4.82307]
 [0.235431105 3.86740351]
 [-2.75554061 1.10352468]]]

Second predicted value p should be between 0 and 1 and since it is out of this range I am getting nan during counting loss.

Any suggestions? Thanks

Answer 1

I can't give an exact programming explanation, but I can give a theoretical answer to this question which you should be able to use to build it.

From what I am assuming, you are asking how to use a different activation function for each output node in the outputs layer. I do not know much about any of the libraries or extensions you are using, but usually these kinds of libraries include some kind of method to create a customised network. From the code you have posted I can see that you are using a pre-defined structure for a network, this means that you may not be able to customise the output layer yourself, and you will have to create a custom network instead. I am assuming you are using Tensorflow due to some of the methods in the code you posted.

There is also something else to consider. Usually you have activated functions on the neurons (hidden layer) too, that is something that you might have to take in to consideration as well.

I am sorry I was not able to give a practical answer, but I hope this helps you see what you can do to get it to work - have a nice day!