Resuming neural network training after a certain epoch in Keras

Question

I am training a neural network with a constant learning rate and epoch = 45. I observed that the accuracy is highest at epoch no 35 and then it wiggles around and decreases. I think I need to reduce the learning rate at epoch 35. Is there any chance that I can train the model again from epoch no 35 after the completion of all the epochs? My code is shown below-

model_nn = keras.Sequential()
model_nn.add(Dense(352, input_dim=28, activation='relu', kernel_regularizer=l2(0.001)))
model_nn.add(Dense(384, activation='relu', kernel_regularizer=l2(0.001)))
model_nn.add(Dense(288, activation='relu', kernel_regularizer=l2(0.001)))
model_nn.add(Dense(448, activation='relu', kernel_regularizer=l2(0.001)))
model_nn.add(Dense(320, activation='relu', kernel_regularizer=l2(0.001)))
model_nn.add(Dense(1, activation='sigmoid'))


auc_score = tf.keras.metrics.AUC()

model_nn.compile(loss='binary_crossentropy', 
                 optimizer=keras.optimizers.Adam(learning_rate=0.0001), 
                                                 metrics=['accuracy',auc_score])

history = model_nn.fit(X_train1, y_train1,
                       validation_data=(X_test, y_test),
                       epochs=45, 
                       batch_size=250, 
                       verbose=1)

_, accuracy = model_nn.evaluate(X_test, y_test)
# Saving model weights
model_nn.save('mymodel.h5')