Touche <variable_name> pas trouvé de point de contrôle Tensorflow

Je suis en utilisant Tensorflow v1.1 et j'ai été à essayer de comprendre comment utiliser mon EMA ed poids pour l'inférence, mais peu importe ce que je fais, je reçois l'erreur

Pas trouvé: Touche W/ExponentialMovingAverage pas trouvé de point de contrôle

même si, quand j'parcourir et d'imprimer tous les tf.global_variables la clé existe

Ici est reproductible script fortement adapté à partir de Facenet de l' de test de l'unité:

import tensorflow as tf
import numpy as np
tf.reset_default_graph()
# Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data * 0.1 + 0.3
# Try to find values for W and b that compute y_data = W * x_data + b
# (We know that W should be 0.1 and b 0.3, but TensorFlow will
# figure that out for us.)
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name='W')
b = tf.Variable(tf.zeros([1]), name='b')
y = W * x_data + b
# Minimize the mean squared errors.
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
opt_op = optimizer.minimize(loss)
# Track the moving averages of all trainable variables.
ema = tf.train.ExponentialMovingAverage(decay=0.9999)
variables = tf.trainable_variables()
print(variables)
averages_op = ema.apply(tf.trainable_variables())
with tf.control_dependencies([opt_op]):
train_op = tf.group(averages_op)
# Before starting, initialize the variables.  We will 'run' this first.
init = tf.global_variables_initializer()
saver = tf.train.Saver(tf.trainable_variables())
# Launch the graph.
sess = tf.Session()
sess.run(init)
# Fit the line.
for _ in range(201):
sess.run(train_op)
w_reference = sess.run('W/ExponentialMovingAverage:0')
b_reference = sess.run('b/ExponentialMovingAverage:0')
saver.save(sess, os.path.join("model_ex1"))
tf.reset_default_graph()
tf.train.import_meta_graph("model_ex1.meta")
sess = tf.Session()
print('------------------------------------------------------')
for var in tf.global_variables():
print('all variables: ' + var.op.name)
for var in tf.trainable_variables():
print('normal variable: ' + var.op.name)
for var in tf.moving_average_variables():
print('ema variable: ' + var.op.name)
print('------------------------------------------------------')
mode = 1
restore_vars = {}
if mode == 0:
ema = tf.train.ExponentialMovingAverage(1.0)
for var in tf.trainable_variables():
print('%s: %s' % (ema.average_name(var), var.op.name))
restore_vars[ema.average_name(var)] = var
elif mode == 1:
for var in tf.trainable_variables():
ema_name = var.op.name + '/ExponentialMovingAverage'
print('%s: %s' % (ema_name, var.op.name))
restore_vars[ema_name] = var
saver = tf.train.Saver(restore_vars, name='ema_restore')
saver.restore(sess, os.path.join("model_ex1")) # error happens here!
w_restored = sess.run('W:0')
b_restored = sess.run('b:0')
print(w_reference)
print(w_restored)
print(b_reference)
print(b_restored)

OriginalL'auteur YellowPillow | 2017-07-19