코딩으로 돈벌기

class myCallback(tf.keras.callbacks.Callback) : def on_epoch_end(self, epoch, logs={}) : if logs['val_accuracy'] > 0.88: print('\n내가 정한 정확도에 도달했으니, 학습을 멈춘다.') self.model.stop_training = True my_cb = myCallback() model = build_model() epoch_history = model.fit(X_train, y_train, epochs=30, validation_split=0.2, callbacks = [my_cb])

def build_model(): model = Sequential() model.add( Flatten() ) model.add( Dense(128, 'relu') ) model.add( Dense(64, 'relu') ) model.add( Dense(10, 'softmax')) model.compile('adam', 'sparse_categorical_crossentropy', ['accuracy']) return model model = build_model() epoch_history = model.fit(X_train, y_train, epochs= 30 , validation_split= 0.2 ) 모델링을 하면서 에포크의 숫자를 너무 과하게 할경우 epoch_history.history.k..