train half model in one time and other in another time
You can save a keras model during the training and then load it up again. To save a model you can do so using a json format, and then to save the weights you can do so using a callback as follows:

Let's make some random model,

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])


Then we can save the model that we created itself

# Save the model
model_json = model.to_json()
with open("weights/model.json", "w") as json_file:
    json_file.write(model_json)


And we can use a callback to save the weights at some given checkpoint. In this example they are saved each time we get better model performance.

# Save the weights using a checkpoint.
filepath="weights/weights-improvement-{epoch:02d}-{val_acc:.2f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]

epochs = 4
batch_size = 128
# Fit the model weights.
model.fit(x_train_reshaped, y_train_binary,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          callbacks=callbacks_list,
          validation_data=(x_test_reshaped, y_test_binary))


Then we can load the model and weights in the future using

# load model saved as a json and create model
json_file = open('weights/model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)

# load weights into new model
loaded_model.load_weights("weights/weights-improvement-04-0.99.hdf5")
print("Loaded model from disk")

# evaluate loaded model on test data
#loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
predictions = loaded_model.predict_classes(x_test_reshaped[0:10], verbose=0)
show_imgs(x_test[0:10])
print('Predicted classes: ', predictions)


Once you have the loaded weights, then you can resume training by once again calling the fit function

epochs = 4
batch_size = 128
# Fit the model weights.
loaded_model.fit(x_train_reshaped, y_train_binary,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          callbacks=callbacks_list,
          validation_data=(x_test_reshaped, y_test_binary))


This will perform 4 more epochs of training.