Save Keras model at specific epochs

Edit
In most cases it's enough to use name formatting suggested by @Toan Tran in his answer.

But if you need some sophisticated logic, you can use a callback, for example

import keras

class CustomSaver(keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs={}):
        if epoch == 2:  # or save after some epoch, each k-th epoch etc.
            self.model.save("model_{}.hd5".format(epoch))

on_epoch_end is called at the end of each epoch; epoch is a number of epoch, latter argument is a logs (you can read about other callback methods in docs). Put the logic into this method (in example it's as simple as possible).

Create saver object and put it into fit method:

import keras
import numpy as np

inp = keras.layers.Input(shape=(10,))
dense = keras.layers.Dense(10, activation='relu')(inp)
out = keras.layers.Dense(1, activation='sigmoid')(dense)
model = keras.models.Model(inp, out)
model.compile(optimizer="adam", loss="binary_crossentropy",)

# Just a noise data for fast working example
X = np.random.normal(0, 1, (1000, 10))
y = np.random.randint(0, 2, 1000)

# create and use callback:
saver = CustomSaver()
model.fit(X, y, callbacks=[saver], epochs=5)

In the bash:

!ls
Out:
model_2.hd5                     

So, it works.

checkpoint = keras.callbacks.ModelCheckpoint('model{epoch:08d}.h5', period=5) 
model.fit(X_train, Y_train, callbacks=[checkpoint])

Did you try checkpoint? period=5 means model is saved after 5 epoch

More details here

Hope this help :)