Question: I'm interested in creating a callback while fitting my keras model. More in detail I'd like to receive a message from a bot telegram with val\_acc each time an epoch is over. I know you can add a callback\_list as a parameter in `classifier.fit()` but many callbacks are prebuilt by keras and I don't know how to add a custom one.

Thank you!

Answer: 
As example, I provide my custom callback with F1 metric. It calculates F1 at the end of each epoch not batch-wise but for ALL the train data passed (and optionally also validation). It can be easily customized with every other metric

```
class F1History(tf.keras.callbacks.Callback):

    def __init__(self, train, validation=None):
        super(F1History, self).__init__()
        self.validation = validation
        self.train = train

    def on_epoch_end(self, epoch, logs={}):

        logs['F1_score_train'] = float('-inf')
        X_train, y_train = self.train[0], self.train[1]
        y_pred = (self.model.predict(X_train).ravel()>0.5)+0
        score = f1_score(y_train, y_pred)       

        if (self.validation):
            logs['F1_score_val'] = float('-inf')
            X_valid, y_valid = self.validation[0], self.validation[1]
            y_val_pred = (self.model.predict(X_valid).ravel()>0.5)+0
            val_score = f1_score(y_valid, y_val_pred)
            logs['F1_score_train'] = np.round(score, 5)
            logs['F1_score_val'] = np.round(val_score, 5)
        else:
            logs['F1_score_train'] = np.round(score, 5)

```

for fitting:

```
es = EarlyStopping(patience=3, verbose=1, min_delta=0.001, monitor='F1_score_val', mode='max', restore_best_weights=True)
model.fit(x_train,y_train, epochs=10, 
          callbacks=[F1History(train=(x_train,y_train),validation=(x_val,y_val)),es])

```
You can perform any action while the model is training. for this purpose keras has provided some methods as following:

```
on_train_begin, on_train_end, on_epoch_begin, on_epoch_end, on_test_begin,
on_test_end, on_predict_begin, on_predict_end, on_train_batch_begin, on_train_batch_end,
on_test_batch_begin, on_test_batch_end, on_predict_batch_begin,on_predict_batch_end

```

**Example:**

If you want to make predictions on the test data at the end of every epoch using the model which is being trained you can do it using the following code

```
class CustomCallback(keras.callbacks.Callback):
    def __init__(self, model, x_test, y_test):
        self.model = model
        self.x_test = x_test
        self.y_test = y_test

    def on_epoch_end(self, epoch, logs={}):
        y_pred = self.model.predict(self.x_test, self.y_test)
        print('y predicted: ', y_pred)

```

You need mention the callback during the `model.fit`

```
model.sequence()
# your model architecture
model.fit(x_train, y_train, epochs=10, 
          callbacks=[CustomCallback(model, x_test, y_test)])

```