我有这个网络模型：初学v3预训练。 https://storage.googleapis.com/openimages/2016_08/model_2016_08.tar.gz
我想用新图层扩展它。
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os.path
import tensorflow as tf
from tensorflow.contrib import layers
from tensorflow.contrib.slim.python.slim.nets import inception
from tensorflow.python.ops import array_ops
from tensorflow.python.training import saver as tf_saver
slim = tf.contrib.slim
FLAGS = None
def PreprocessImage(image, central_fraction=0.875):
"""Load and preprocess an image.
Args:
image: a tf.string tensor with an JPEG-encoded image.
central_fraction: do a central crop with the specified
fraction of image covered.
Returns:
An ops.Tensor that produces the preprocessed image.
"""
# Decode Jpeg data and convert to float.
image = tf.cast(tf.image.decode_jpeg(image, channels=3), tf.float32)
image = tf.image.central_crop(image, central_fraction=central_fraction)
# Make into a 4D tensor by setting a 'batch size' of 1.
image = tf.expand_dims(image, [0])
image = tf.image.resize_bilinear(image,
[FLAGS.image_size, FLAGS.image_size],
align_corners=False)
# Center the image about 128.0 (which is done during training) and normalize.
image = tf.multiply(image, 1.0 / 127.5)
return tf.subtract(image, 1.0)
def main(args):
if not os.path.exists(FLAGS.checkpoint):
tf.logging.fatal(
'Checkpoint %s does not exist. Have you download it? See tools/download_data.sh',
FLAGS.checkpoint)
g = tf.Graph()
with g.as_default():
input_image = tf.placeholder(tf.string)
processed_image = PreprocessImage(input_image)
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, end_points = inception.inception_v3(
processed_image, num_classes=FLAGS.num_classes, is_training=False)
predictions = end_points['multi_predictions'] = tf.nn.sigmoid(
logits, name='multi_predictions')
sess = tf.Session()
saver = tf_saver.Saver()
saver.restore(sess, FLAGS.checkpoint)
logits_2 = layers.conv2d(
end_points['PreLogits'],
FLAGS.num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='Conv2d_final_1x1')
logits_2 = array_ops.squeeze(logits_2, [1, 2], name='SpatialSqueeze_2')
predictions_2 = end_points['multi_predictions_2'] = tf.nn.sigmoid(logits_2, name='multi_predictions_2')
sess.run(tf.global_variables_initializer())
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint', type=str, default='2016_08/model.ckpt',
help='Checkpoint to run inference on.')
parser.add_argument('--dict', type=str, default='dict.csv',
help='Path to a dict.csv that translates from mid to a display name.')
parser.add_argument('--image_size', type=int, default=299,
help='Image size to run inference on.')
parser.add_argument('--num_classes', type=int, default=6012,
help='Number of output classes.')
parser.add_argument('--image_path', default='test_set/0a9ed4def08fe6d1')
FLAGS = parser.parse_args()
tf.app.run()
如何只恢复保存在model.ckpt文件中的初始v3预训练网络权重？ 如何初始化新的conv2d图层？
答案 0 :(得分：3)
我解决了这个问题!!
在使用saver.restore (sess, FLAGS.checkpoint)初始化网络后，您需要致电sess.run (tf.global_variables_initializer ())。
重要：在向图表添加新图层之前，必须先实例化saver = tf_saver.Saver ()。
这样，当执行saver.restore(sess, FLAGS.checkpoint)时，它只会在创建新图层之前知道计算图。
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os.path
import tensorflow as tf
from tensorflow.contrib import layers
from tensorflow.contrib.slim.python.slim.nets import inception
from tensorflow.python.ops import array_ops
from tensorflow.python.training import saver as tf_saver
slim = tf.contrib.slim
FLAGS = None
def PreprocessImage(image, central_fraction=0.875):
"""Load and preprocess an image.
Args:
image: a tf.string tensor with an JPEG-encoded image.
central_fraction: do a central crop with the specified
fraction of image covered.
Returns:
An ops.Tensor that produces the preprocessed image.
"""
# Decode Jpeg data and convert to float.
image = tf.cast(tf.image.decode_jpeg(image, channels=3), tf.float32)
image = tf.image.central_crop(image, central_fraction=central_fraction)
# Make into a 4D tensor by setting a 'batch size' of 1.
image = tf.expand_dims(image, [0])
image = tf.image.resize_bilinear(image,
[FLAGS.image_size, FLAGS.image_size],
align_corners=False)
# Center the image about 128.0 (which is done during training) and normalize.
image = tf.multiply(image, 1.0 / 127.5)
return tf.subtract(image, 1.0)
def main(args):
if not os.path.exists(FLAGS.checkpoint):
tf.logging.fatal(
'Checkpoint %s does not exist. Have you download it? See tools/download_data.sh',
FLAGS.checkpoint)
g = tf.Graph()
with g.as_default():
input_image = tf.placeholder(tf.string)
processed_image = PreprocessImage(input_image)
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, end_points = inception.inception_v3(
processed_image, num_classes=FLAGS.num_classes, is_training=False)
predictions = end_points['multi_predictions'] = tf.nn.sigmoid(
logits, name='multi_predictions')
sess = tf.Session()
saver = tf_saver.Saver()
logits_2 = layers.conv2d(
end_points['PreLogits'],
FLAGS.num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='Conv2d_final_1x1')
logits_2 = array_ops.squeeze(logits_2, [1, 2], name='SpatialSqueeze_2')
predictions_2 = end_points['multi_predictions_2'] = tf.nn.sigmoid(logits_2, name='multi_predictions_2')
sess.run(tf.global_variables_initializer())
saver.restore(sess, FLAGS.checkpoint)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint', type=str, default='2016_08/model.ckpt',
help='Checkpoint to run inference on.')
parser.add_argument('--dict', type=str, default='dict.csv',
help='Path to a dict.csv that translates from mid to a display name.')
parser.add_argument('--image_size', type=int, default=299,
help='Image size to run inference on.')
parser.add_argument('--num_classes', type=int, default=6012,
help='Number of output classes.')
parser.add_argument('--image_path', default='test_set/0a9ed4def08fe6d1')
FLAGS = parser.parse_args()
tf.app.run()