Keras version: 3

TensorFlow version

2.16.1

Current behavior?

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step - accuracy: 0.5000 - loss: 1.1560
[1.1560312509536743, 0.5]
Epoch 1/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 596ms/step - accuracy: 0.5000 - loss: 1.1560
Epoch 2/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step - accuracy: 0.5000 - loss: 14.5018
Epoch 3/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step - accuracy: 0.5000 - loss: 9.9714
Epoch 4/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step - accuracy: 0.7500 - loss: 1.3363
Epoch 5/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 26ms/step - accuracy: 1.0000 - loss: 8.9407e-08
Epoch 6/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 1.0000 - loss: 4.7684e-07
Epoch 7/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step - accuracy: 0.7500 - loss: 0.2545
Epoch 8/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step - accuracy: 0.7500 - loss: 0.8729
Epoch 9/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step - accuracy: 1.0000 - loss: 9.1682e-04
Epoch 10/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step - accuracy: 1.0000 - loss: 2.6822e-07
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step - accuracy: 1.0000 - loss: 0.0000e+00
[0.0, 1.0]
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step - accuracy: 0.2500 - loss: 0.8475 # this should be acc 1.0 loss 0
[0.847506046295166, 0.25]

Standalone code to reproduce the issue

import tensorflow as tf

class CusModel(tf.keras.Model):
    def __init__(self):
        super().__init__()
        self.dense = tf.keras.layers.Dense(units=2, activation='softmax', name='output')

    def call(self, x):
        return self.dense(x)

dummy_data_x = tf.convert_to_tensor([[0, 0],
                [1, 0],
                [0, 1],
                [1, 1]])
dummy_data_y = tf.convert_to_tensor([0, 1, 0, 1])

model = CusModel()
model.compile(optimizer=tf.keras.optimizers.Adam(10.0),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
print(model.evaluate(x=dummy_data_x, y=dummy_data_y))
model.fit(x=dummy_data_x, y=dummy_data_y, epochs=10)
print(model.evaluate(x=dummy_data_x, y=dummy_data_y))
model.save_weights('test_model.weights.h5')

model = CusModel()
model.load_weights('test_model.weights.h5')
model.compile(optimizer=tf.keras.optimizers.Adam(10.0),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
print(model.evaluate(x=dummy_data_x, y=dummy_data_y))