def main(argv):
model_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if model_file is None:
print('No model found')
sys.exit()
set_log_level(logging.DEBUG)
sess = tf.Session()
with sess.as_default():
model = make_wresnet()
saver = tf.train.Saver()
# Restore the checkpoint
saver.restore(sess, model_file)
SCOPE = "cifar10_challenge"
model2 = make_wresnet(scope=SCOPE)
assert len(model.get_vars()) == len(model2.get_vars())
found = [False] * len(model2.get_vars())
for var1 in model.get_vars():
var1_found = False
var2_name = SCOPE + "/" + var1.name
for idx, var2 in enumerate(model2.get_vars()):
if var2.name == var2_name:
var1_found = True
found[idx] = True
sess.run(tf.assign(var2, var1))
break
assert var1_found, var1.name
assert all(found)
model2.dataset_factory = Factory(CIFAR, {"max_val": 255})
serial.save("model.joblib", model2)
def __init__(self, n_classes=10):
self.W_conv1 = self._weight_variable([5, 5, 1, 32])
self.b_conv1 = self._bias_variable([32])
self.W_conv2 = self._weight_variable([5, 5, 32, 64])
self.b_conv2 = self._bias_variable([64])
self.W_fc1 = self._weight_variable([7 * 7 * 64, 1024])
self.b_fc1 = self._bias_variable([1024])
self.W_fc2 = self._weight_variable([1024, n_classes])
self.b_fc2 = self._bias_variable([n_classes])
super().__init__(self, '', n_classes, {})
self.dataset_factory = Factory(MNIST, {"center": False})
def __init__(self, nb_classes=10):
# NOTE: for compatibility with Madry Lab downloadable checkpoints,
# we cannot use scopes, give these variables names, etc.
self.W_conv1 = self._weight_variable([5, 5, 1, 32])
self.b_conv1 = self._bias_variable([32])
self.W_conv2 = self._weight_variable([5, 5, 32, 64])
self.b_conv2 = self._bias_variable([64])
self.W_fc1 = self._weight_variable([7 * 7 * 64, 1024])
self.b_fc1 = self._bias_variable([1024])
self.W_fc2 = self._weight_variable([1024, nb_classes])
self.b_fc2 = self._bias_variable([nb_classes])
Model.__init__(self, "", nb_classes, {})
self.dataset_factory = Factory(MNIST, {"center": False})
def __init__(self, nb_classes=10):
# NOTE: for compatibility with Madry Lab downloadable checkpoints,
# we cannot use scopes, give these variables names, etc.
"""
self.conv1 = tf.layers.Conv2D(32, (5, 5), activation='relu', padding='same', name='conv1')
self.pool1 = tf.layers.MaxPooling2D((2, 2), (2, 2), padding='same')
self.conv2 = tf.layers.Conv2D(64, (5, 5), activation='relu', padding='same', name='conv2')
self.pool2 = tf.layers.MaxPooling2D((2, 2), (2, 2), padding='same')
self.fc1 = tf.layers.Dense(1024, activation='relu', name='fc1')
self.fc2 = tf.layers.Dense(10, name='fc2')
"""
keras_model = tf.keras.Sequential()
keras_model.add(
tf.keras.layers.Conv2D(32, (5, 5),
activation='relu',
padding='same',
name='conv1',
input_shape=(28, 28, 1)))
keras_model.add(
tf.keras.layers.MaxPooling2D((2, 2), (2, 2), padding='same'))
keras_model.add(
tf.keras.layers.Conv2D(64, (5, 5),
activation='relu',
padding='same',
name='conv2'))
keras_model.add(
tf.keras.layers.MaxPooling2D((2, 2), (2, 2), padding='same'))
keras_model.add(tf.keras.layers.Flatten())
keras_model.add(
tf.keras.layers.Dense(1024, activation='relu', name='fc1'))
keras_model.add(tf.keras.layers.Dense(10, name='fc2'))
self.keras_model = keras_model
Model.__init__(self, '', nb_classes, {})
self.dataset_factory = Factory(MNIST, {"center": False})