Tensorflow: how to save/restore a model?

Question

After you train a model in Tensorflow:

1. How do you save the trained model?
2. How do you later restore this saved model?

Answer 1

Following @Vishnuvardhan Janapati 's answer, here is another way to save and reload model with custom layer/metric/loss under TensorFlow 2.0.0

import tensorflow as tf
from tensorflow.keras.layers import Layer
from tensorflow.keras.utils.generic_utils import get_custom_objects

# custom loss (for example)  
def custom_loss(y_true,y_pred):
  return tf.reduce_mean(y_true - y_pred)
get_custom_objects().update({'custom_loss': custom_loss}) 

# custom loss (for example) 
class CustomLayer(Layer):
  def __init__(self, ...):
      ...
  # define custom layer and all necessary custom operations inside custom layer

get_custom_objects().update({'CustomLayer': CustomLayer})  

In this way, once you have executed such codes, and saved your model with tf.keras.models.save_model or model.save or ModelCheckpoint callback, you can re-load your model without the need of precise custom objects, as simple as

new_model = tf.keras.models.load_model("./model.h5"})