How to save models?

[Minsol Jeong]

Hello, I am a student who just started to learn machine learning. Your codes were really helpful. I'd like save the codes and restore them later when there were different date sets. I am wondering how I can save the models and successfully restore them later?

Here is my code that I've worked on so far.

`
output_predict = minmax.inverse_transform(output_predict)
deep_future = self.anchor(output_predict[:, 0], 0.3)

    weights = tf.Variable(tf.random_normal([modelnn.X, modelnn.hidden_layer]), name='weights')
    biases = tf.Variable(tf.random_normal([moedlnn.X]), name='biases')
    X = tf.Variable(tf.random_normal([modelnn.X]))
    saver  = tf.train.Saver()
    sess = tf.Session()
    sess.run(tf.global_variables_initializer())
    saver.save(sess, 'my_model', global_step=1000)

    return deep_future[-test_size:]`

[Harini N]

If you have find the best solution to save the model, please let me know!

[Nil Andreu]

@jminsol @HariniNarasimhan. You can both save the model or save the weights. In the case we want to save the weights (less memory usage):

# Include the epoch in the file name (uses `str.format`)
checkpoint_path = "training/cp-{epoch:04d}.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)

# Create a callback that saves the model's weights every 5 epochs
cp_callback = tf.keras.callbacks.ModelCheckpoint(
    filepath=checkpoint_path, 
    verbose=1, 
    save_weights_only=True,
    save_freq=5*batch_size)

# Create a new model instance
model = create_model()

# Save the weights using the `checkpoint_path` format
model.save_weights(checkpoint_path.format(epoch=0))

# Train the model
model.fit(...)

# And we want to load the weights used
model = create_model()
model.load_weights(...)

For documentation here.