calculate score calculation within callback

[pure-rgb]

import keras

def get_model():
    model = keras.Sequential()
    model.add(keras.layers.Dense(1))
    model.compile(
        optimizer=keras.optimizers.RMSprop(learning_rate=0.1),
        loss="mean_squared_error",
        metrics=["mean_absolute_error"],
    )
    return model

(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train = x_train.reshape(-1, 784).astype("float32") / 255.0
x_test = x_test.reshape(-1, 784).astype("float32") / 255.0
x_train = x_train[:1000]
y_train = y_train[:1000]
x_test = x_test[:1000]
y_test = y_test[:1000]

class CustomCallback(keras.callbacks.Callback):
    def __init__(self, x, y):
        super().__init__()
        self.x = x
        self.y = y
        
    def on_epoch_end(self, epoch, logs=None):
        y_pred = self.model.predict(self.x, verbose=0)
        score = self.model.compute_metrics(self.x, self.y, y_pred, sample_weight=None)
        print()
        print(score)

model = get_model()
model.fit(
    x_train,
    y_train,
    batch_size=256,
    epochs=5,
    verbose=1,
    callbacks=[CustomCallback(x_train, y_train)],
)

Epoch 1/5
1/4 ━━━━━━━━━━━━━━━━━━━━ 1s 526ms/step - loss: 25.3436 - mean_absolute_error: 4.2441
{'loss': 242.523193359375, 'mean_absolute_error': 6.016280174255371}
4/4 ━━━━━━━━━━━━━━━━━━━━ 1s 57ms/step - loss: 256.4755 - mean_absolute_error: 10.3880
Epoch 2/5
1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step - loss: 6.6658 - mean_absolute_error: 2.1646
{'loss': 6.03378438949585, 'mean_absolute_error': 1.8999731540679932}
4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 6.2043 - mean_absolute_error: 2.0708
Epoch 3/5
1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step - loss: 4.1691 - mean_absolute_error: 1.6324
{'loss': 4.564587593078613, 'mean_absolute_error': 1.7218464612960815}
4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step - loss: 4.4746 - mean_absolute_error: 1.7039
Epoch 4/5
1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step - loss: 4.4333 - mean_absolute_error: 1.7299
{'loss': 4.227972030639648, 'mean_absolute_error': 1.6317805051803589}
4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 43ms/step - loss: 4.2346 - mean_absolute_error: 1.6612
Epoch 5/5
1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step - loss: 3.7971 - mean_absolute_error: 1.5549
{'loss': 5.39981746673584, 'mean_absolute_error': 2.682666063308716}
4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step - loss: 4.6834 - mean_absolute_error: 1.7220
<keras.src.callbacks.history.History at 0x7fa5642bba60>

1. About printing log, why printing caused in the middle of epoch, when it should be after finishing the epoch.

Epoch 1/5
1/4 ━━━━━━━━━━━━━━━━━━━━ 1s 526ms/step - loss: 25.3436 - mean_absolute_error: 4.2441
{'loss': 242.523193359375, 'mean_absolute_error': 6.016280174255371}
4/4 ━━━━━━━━━━━━━━━━━━━━ 1s 57ms/step - loss: 256.4755 - mean_absolute_error: 10.3880

2. About score, it should match but callback gives loss 242 and logs gives 256 - callback gives mae 6.01 and log gives 10.3.

[pctablet505]

The loss and metrics displayed in progress bar is for each batch or mini-batch, where as the output in next line is for each epoch.

[pure-rgb]

ok.

Now, if we do this, then I should able to get them matched.

x_train = x_train[:256]
y_train = y_train[:256]
x_test = x_test[:256]
y_test = y_test[:256]

model = get_model()
model.fit(
    x_train,
    y_train,
    batch_size=256,
    epochs=5,
    verbose=1,
    callbacks=[CustomCallback(x_train, y_train)],
)

Epoch 1/5
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 405ms/step - loss: 25.9250 - mean_absolute_error: 4.1543
{'loss': 25.925048828125, 'mean_absolute_error': 16.05439567565918}
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 490ms/step - loss: 25.9250 - mean_absolute_error: 4.1543

The loss get matched, but why not metrics?