def sparse_filtering_loss(_, y_pred):
'''Defines the sparse filtering loss function.
Args:
y_true (tensor): The ground truth tensor (not used, since this is an
unsupervised learning algorithm).
y_pred (tensor): Tensor representing the feature vector at a
particular layer.
Returns:
scalar tensor: The sparse filtering loss.
'''
y = tf.reshape(y_pred, tf.stack([-1, tf.reduce_prod(y_pred.shape[1:])]))
l2_normed = tf.nn.l2_normalize(y, dim=1)
l1_norm = tf.norm(l2_normed, ord=1, axis=1)
return tf.reduce_sum(l1_norm)
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