How can I implement this custom loss function in Keras?

I'm trying to implement a custom loss function on my neural network, which would look like this, if tensors were, instead, numpy arrays:

def custom_loss(y_true, y_pred):
    activated = y_pred[y_true > 1]
    return np.abs(activated.mean() - activated.std()) / activated.std()

The y's have a shape of (batch_size, 1); that's to say, it's a scalar output for each input row.

obs: this post (Converting Tensor to np.array using K.eval() in Keras returns InvalidArgumentError) gave me an initial direction for which to walk on.

Edit:

This is a reproducible setup for which I'm trying to apply the custom loss function:

import numpy as np

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers


X = np.random.normal(0, 1, (256, 5))
Y = np.random.normal(0, 1, (256, 1))

model = keras.Sequential([
    layers.Dense(1),
])

model.compile(optimizer='adam', loss=custom_loss)

model.fit(X, Y)

The .fit() on the last line throws the error AttributeError: 'Tensor' object has no attribute 'mean', if I define custom_loss as stated above on my question.

Solution

It's a simple catch. You can use your custom loss as follows

def custom_loss(y_true, y_pred):
    activated = y_pred[y_true > 1]
    return tf.math.abs(tf.reduce_mean(activated) - 
                       tf.math.reduce_std(activated)) / tf.math.reduce_std(activated)

or if you want to use tf.boolean_mask(tensor, mask, ..) then you need to ensure that the mask condition is in the shape of (None,) or 1D. And if we apply tf.where(y_true>1) it will produce a 2D tensor that needs to be reshaped in your case.

def custom_loss(y_true, y_pred):
    activated = tf.boolean_mask(y_pred, tf.reshape(tf.where(y_true>1),[-1]) )
    return tf.math.abs(tf.reduce_mean(activated) - 
                       tf.math.reduce_std(activated)) / tf.math.reduce_std(activated)