I’m trying to create custom loss function which works as follow:
- I have a classification problem (50 classes)
- Each class belong to one fam class (3 fam classes)
- I have a dictionary which link between class and fam class.
- I want to create a model which predict one of the 50 classes.
- When calculating the loss, if the model prediction class is wrong it will punish more if the fam-class of the prediction is different from the true fam-class
simple example code:
import tensorflow as tf
import numpy as np
import random
# Generate synthetic dataset
num_samples = 1000
num_classes = 50
num_fam_classes = 3
# Create random data
X_train = np.random.rand(num_samples, 10)
y_train_class = np.random.randint(0, num_classes, size=num_samples)
X_val = np.random.rand(num_samples, 10)
y_val_class = np.random.randint(0, num_classes, size=num_samples)
# convert to categoy
y_train_one_hot = tf.keras.utils.to_categorical(y_train_class, num_classes=50)
y_val_one_hot = tf.keras.utils.to_categorical(y_val_class, num_classes=50)
# map between class and fam class
class_to_fam_dict = {}
for i in range(num_classes):
class_to_fam_dict[i] = random.randint(0, 2)
def map_class_to_fam(class_index):
return tf.cast(class_to_fam_dict[class_index], dtype=tf.int64)
# define loss:
def custom_loss(y_true, y_pred):
#
# --- step 1 : loss between classes
#
# loss between classes
ce_loss_class = tf.keras.losses.categorical_crossentropy(y_true, y_pred, from_logits=False)
#
# --- step 2 : loss between fam class
#
# Extract predicted class indices
predicted_classes = tf.argmax(y_pred, axis=1)
predicted_fam_classes = tf.map_fn(map_class_to_fam, predicted_classes, dtype=tf.int64)
true_fam_classes = tf.map_fn(map_class_to_fam, tf.argmax(y_true, axis=1), dtype=tf.int64)
true_fam_classes_one_hot = tf.one_hot(true_fam_classes, depth=num_fam_classes)
# Compute penalty term for different family classes
penalty = tf.where(tf.not_equal(predicted_fam_classes, true_fam_classes),
tf.keras.losses.categorical_crossentropy(true_fam_classes_one_hot,
tf.one_hot(predicted_fam_classes, depth=num_fam_classes),
from_logits=False),
0.0)
# Sum the penalty along with the class prediction loss
total_loss = ce_loss_class + penalty
return total_loss
# model
model = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation='relu', input_shape=(10,)),
tf.keras.layers.Dense(50, activation='softmax')
])
Give error:
TypeError: Tensor is unhashable. Instead, use tensor.ref() as the key.
I have tried to add:
tf.compat.v1.disable_v2_behavior()
but it gives different error:
KeyError: <tf.Tensor 'loss/dense_3_loss/map/while/TensorArrayReadV3:0' shape=() dtype=int64>
Versions:
tensorflow==2.10.0
python 3.10.0
How can I fix the code ?