tf.keras.models.save_model(
model,
filepath,
overwrite=True,
include_optimizer=True,
save_format=None,
signatures=None,
options=None,
save_traces=True,
)new_model = tf.keras.models.load_model('my_model.h5')# Save the modelmodel.save('path_to_my_model.h5')# Recreate the exact same model purely from the filenew_model = keras.models.load_model('path_to_my_model.h5')
with tf.Session() as sess:
new_saver = tf.train.import_meta_graph('my_test_model-1000.meta')
new_saver.restore(sess, tf.train.latest_checkpoint('./'))
with tf.Session() as sess:
saver = tf.train.import_meta_graph('my-model-1000.meta')
saver.restore(sess,tf.train.latest_checkpoint('./'))
print(sess.run('w1:0'))
##Model has been restored. Above statement will print the saved value of w1.