11.1 Saving and Restoring TensorFlow Models

import os

import numpy as np
np.random.seed(123)
print("NumPy:{}".format(np.__version__))

import tensorflow as tf
tf.set_random_seed(123)
print("TensorFlow:{}".format(tf.__version__))

NumPy:1.13.3
Pandas:0.21.0
Matplotlib:2.1.0
TensorFlow:1.4.0


Using TensorFlow backend.


Keras:2.0.9

DATASETSLIB_HOME = os.path.expanduser('~/dl-ts/datasetslib')
import sys
if not DATASETSLIB_HOME in sys.path:
    sys.path.append(DATASETSLIB_HOME)
%reload_ext autoreload
%autoreload 2
import datasetslib

from datasetslib import util as dsu
datasetslib.datasets_root = os.path.join(os.path.expanduser('~'),'datasets')
models_root = os.path.join(os.path.expanduser('~'),'models')

Saving / Restoring Model in TensorFlow

Saving all variables in a graph

# Saving all variables in a graph

tf.reset_default_graph()

# Assume Linear Model y = w * x + b
# Define model parameters
w = tf.Variable([.3], tf.float32)
b = tf.Variable([-.3], tf.float32)
# Define model input and output
x = tf.placeholder(tf.float32)
y = w * x + b
output = 0

# create saver object
saver = tf.train.Saver()

with tf.Session() as tfs:
    # initialize and print the variable y
    tfs.run(tf.global_variables_initializer())
    output = tfs.run(y,{x:[1,2,3,4]})
    saved_model_file = saver.save(tfs,'saved-models/full-graph-save-example.ckpt')
    print('Model saved in {}'.format(saved_model_file))
    print('Values of variables w,b: {}{}'.format(w.eval(),b.eval()))
    print('output={}'.format(output))

Model saved in saved-models/full-graph-save-example.ckpt
Values of variables w,b: [ 0.30000001][-0.30000001]
output=[ 0.          0.30000001  0.60000002  0.90000004]

Restoring all variables from a graph

tf.reset_default_graph()

# Assume Linear Model y = w * x + b
# Define model parameters
w = tf.Variable([0], dtype=tf.float32)
b = tf.Variable([0], dtype=tf.float32)
# Define model input and output
x = tf.placeholder(dtype=tf.float32)
y = w * x + b
output = 0

# create saver object
saver = tf.train.Saver()

with tf.Session() as tfs:
    saved_model_file = saver.restore(tfs,'saved-models/full-graph-save-example.ckpt')
    print('Values of variables w,b: {}{}'.format(w.eval(),b.eval()))
    output = tfs.run(y,{x:[1,2,3,4]})
    print('output={}'.format(output))

INFO:tensorflow:Restoring parameters from saved-models/full-graph-save-example.ckpt
Values of variables w,b: [ 0.30000001][-0.30000001]
output=[ 0.          0.30000001  0.60000002  0.90000004]

Saving selected variables in a graph

# Saving selected variables in a graph in TensorFlow

tf.reset_default_graph()

# Assume Linear Model y = w * x + b
# Define model parameters
w = tf.Variable([.3], tf.float32)
b = tf.Variable([-.3], tf.float32)
# Define model input and output
x = tf.placeholder(tf.float32)
y = w * x + b
output = 0

# create saver object
saver = tf.train.Saver({'weights': w})

with tf.Session() as tfs:
    # initialize and print the variable y
    tfs.run(tf.global_variables_initializer())
    output = tfs.run(y,{x:[1,2,3,4]})
    saved_model_file = saver.save(tfs,'saved-models/weights-save-example.ckpt')
    print('Model saved in {}'.format(saved_model_file))
    print('Values of variables w,b: {}{}'.format(w.eval(),b.eval()))
    print('output={}'.format(output))

Model saved in saved-models/weights-save-example.ckpt
Values of variables w,b: [ 0.30000001][-0.30000001]
output=[ 0.          0.30000001  0.60000002  0.90000004]

Restoring selected variables in a graph

# Restoring selected variables in a graph in TensorFlow

tf.reset_default_graph()

# Assume Linear Model y = w * x + b
# Define model parameters
w = tf.Variable([0], dtype=tf.float32)
b = tf.Variable([0], dtype=tf.float32)
# Define model input and output
x = tf.placeholder(dtype=tf.float32)
y = w * x + b
output = 0

# create saver object
saver = tf.train.Saver({'weights': w})

with tf.Session() as tfs:
    b.initializer.run()
    saved_model_file = saver.restore(tfs,'saved-models/weights-save-example.ckpt')
    print('Values of variables w,b: {}{}'.format(w.eval(),b.eval()))
    output = tfs.run(y,{x:[1,2,3,4]})
    print('output={}'.format(output))

INFO:tensorflow:Restoring parameters from saved-models/weights-save-example.ckpt
Values of variables w,b: [ 0.30000001][ 0.]
output=[ 0.30000001  0.60000002  0.90000004  1.20000005]