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Description
This behaviour happens in Keras 2 but works in Keras 3.
I tried to train a multi-output model. But it target looks like something as follows
y1_dummy = [1, 2, 0, -1, 0, -1, -1, -1, 3, -1]
y2_dummy = [-1, -1, -1, 2, -1, 0, 3, 1, -1, 2]Between this two target array, -1 is paced to y2_dummy[0] but some value in y1_dummy[0] and continues. In training time, I set ignore_class = -1, please see below.
def custom_loss(y_true, y_pred):
loss = sparse_categorical_crossentropy(
y_true, y_pred, ignore_class=-1
)
return lossThe code works in Keras 3 but in Keras 2, the loss becomes nan. Below is the full code.
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.losses import sparse_categorical_crossentropy
from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt
num_samples = 10
num_classes = 4
input_shape = (224, 224, 3)
x_dummy = np.random.rand(num_samples, *input_shape).astype('float32')
y1_dummy = [1, 2, 0, -1, 0, -1, -1, -1, 3, -1]
y2_dummy = [-1, -1, -1, 2, -1, 0, 3, 1, -1, 2]
_sample = tf.data.Dataset.from_tensor_slices(x_dummy)
_labels = tf.data.Dataset.from_tensor_slices(
(
y1_dummy,
y2_dummy
)
)
_data = tf.data.Dataset.zip((_sample, _labels))
_data = _data.batch(batch_size=3, drop_remainder=True)
def custom_loss(y_true, y_pred):
loss = sparse_categorical_crossentropy(
y_true, y_pred, ignore_class=-1
)
return loss
input_layer = keras.Input(shape=input_shape)
flatten_layer = layers.Flatten()(input_layer)
output_layer1 = layers.Dense(
num_classes, activation='softmax', name='out1'
)(flatten_layer)
output_layer2 = layers.Dense(
num_classes, activation='softmax', name='out2'
)(flatten_layer)
A = keras.Model(
inputs=input_layer,
outputs=[output_layer1, output_layer2]
)
A.compile(
optimizer=Adam(),
loss={
'out1': custom_loss,
'out2': custom_loss
}
)
A.fit(
_data,
epochs=2,
)