When the training starts, in the run window only loss and acc are displayed, the val_loss and val_acc are missing. Only at the end, these values are showed.

model.add(Flatten())
model.add(Dense(512, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(10, activation="softmax"))model.compile(loss='categorical_crossentropy',optimizer="adam",metrics=['accuracy']
)model.fit(x_train,y_train,batch_size=32, epochs=1, validation_data=(x_test, y_test),shuffle=True
)

this is how the training starts:

Train on 50000 samples, validate on 10000 samples
Epoch 1/132/50000 [..............................] - ETA: 34:53 - loss: 2.3528 - acc: 0.093864/50000 [..............................] - ETA: 18:56 - loss: 2.3131 - acc: 0.093896/50000 [..............................] - ETA: 13:45 - loss: 2.3398 - acc: 0.1146

and this is when it finishes

49984/50000 [============================>.] - ETA: 0s - loss: 1.5317 - acc: 0.4377
50000/50000 [==============================] - 231s 5ms/step - loss: 1.5317 - acc: 0.4378 - val_loss: 1.1503 - val_acc: 0.5951

I want to see the val_acc and val_loss in each line

Validation loss and accuracy are computed on epoch end, not on batch end. If you want to compute those values after each batch, you would have to implement your own callback with an on_batch_end() method and call self.model.evaluate() on the validation set. See https://keras.io/callbacks/.

But computing the validation loss and accuracy after each epoch is going to slow down your training a lot and doesn't bring much in terms of evaluation of the network performance.