How can you save a keras model in 64bit format?

Question

how can you save a keras model in 64bit format? This is able to 'put tensorflow' in 64bit 'mode' for the current runtime. But I've found that even just saving the model & reloading it is sufficient to truncate the 64bit precision & change model outputs.

In particular I would like to save the model is such a way that it would load automatically in '64 bit mode', and obviously would never lose its precision.

Answer 1

What is the purpose of the requirements when you are using an optimizer larger than e-07?

[ Sample ]:

import os
from os.path import exists

import tensorflow as tf
import h5py

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
database_buffer = "F:\\models\\buffer\\" + os.path.basename(__file__).split('.')[0] + "\\TF_DataSets_01.h5"
database_buffer_dir = os.path.dirname(database_buffer)

checkpoint_path = "F:\\models\\checkpoint\\" + os.path.basename(__file__).split('.')[0] + "\\TF_DataSets_01.h5"
checkpoint_dir = os.path.dirname(checkpoint_path)
loggings = "F:\\models\\checkpoint\\" + os.path.basename(__file__).split('.')[0] + "\\loggings.log"

if not exists(checkpoint_dir) : 
    os.mkdir(checkpoint_dir)
    print("Create directory: " + checkpoint_dir)
    
if not exists(database_buffer_dir) : 
    os.mkdir(database_buffer_dir)
    print("Create directory: " + database_buffer_dir)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
DataSet
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.cifar10.load_data()
# Create hdf5 file
hdf5_file = h5py.File(database_buffer, mode='w')

# Train images
hdf5_file['x_train'] = train_images
hdf5_file['y_train'] = train_labels

# Test images
hdf5_file['x_test'] = test_images
hdf5_file['y_test'] = test_labels

hdf5_file.close()

# Visualize dataset train sample
hdf5_file = h5py.File(database_buffer,  mode='r')

x_train = hdf5_file['x_train'][0: 10000]
x_test = hdf5_file['x_test'][0: 100]
y_train = hdf5_file['y_train'][0: 10000]
y_test = hdf5_file['y_test'][0: 100]

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model = tf.keras.models.Sequential([
    tf.keras.layers.InputLayer(input_shape=( 32, 32, 3 )),
    tf.keras.layers.Normalization(mean=3., variance=2.),
    tf.keras.layers.Normalization(mean=4., variance=6.),
    tf.keras.layers.Conv2DTranspose(2, 3, activation='relu', padding="same", name="Conv2DTranspose_01"),
    tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(1, 1), padding='valid'),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(4 * 256), 
    tf.keras.layers.Reshape((4 * 256, 1)),

    tf.keras.layers.LSTM(128, return_sequences=True, return_state=False),
    tf.keras.layers.LSTM(128, name='LSTM256'),
    tf.keras.layers.Dropout(0.2),
])

model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(64, activation='relu', name='dense64'))
model.add(tf.keras.layers.Dense(7))
model.summary()

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Optimizer
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
optimizer = tf.keras.optimizers.Nadam( learning_rate=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, name='Nadam' )

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Loss Fn
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""                               
lossfn = tf.keras.losses.MeanSquaredLogarithmicError(reduction=tf.keras.losses.Reduction.AUTO, name='mean_squared_logarithmic_error')

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Summary
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model.compile(optimizer=optimizer, loss=lossfn, metrics=['accuracy'])

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: FileWriter
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
if exists(checkpoint_path) :
    model.load_weights(checkpoint_path)
    print("model load: " + checkpoint_path)
    input("Press Any Key!")
    
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Training
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
history = model.fit(x_train, y_train, epochs=1 ,validation_data=(x_train, y_train))
model.save_weights(checkpoint_path)

[ Output ]: