Python TensorFlow将学习迁移到TensorFlow服务问题
任何帮助都将不胜感激 我遵循了这一点,然后我使用这个简单的脚本来验证我的模型是否正常工作:Python TensorFlow将学习迁移到TensorFlow服务问题,python,machine-learning,tensorflow,tensorflow-serving,Python,Machine Learning,Tensorflow,Tensorflow Serving,任何帮助都将不胜感激 我遵循了这一点,然后我使用这个简单的脚本来验证我的模型是否正常工作: import tensorflow as tf from nets import inception_v3 from preprocessing import inception_preprocessing from matplotlib.pyplot import imshow, imread slim = tf.contrib.slim batch_size = 5 image_size = 2
import tensorflow as tf
from nets import inception_v3
from preprocessing import inception_preprocessing
from matplotlib.pyplot import imshow, imread
slim = tf.contrib.slim
batch_size = 5
image_size = 299
with tf.Graph().as_default():
with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
imgPath = 'dandelion.jpg'
#imgPath = '/tmp/rose.jpg'
testImage_string = tf.gfile.FastGFile(imgPath, 'rb').read()
testImage = tf.image.decode_jpeg(testImage_string, channels=3)
processed_image = inception_preprocessing.preprocess_image(testImage, image_size, image_size, is_training=False)
processed_images = tf.expand_dims(processed_image, 0)
logits, _ = inception_v3.inception_v3(processed_images, num_classes=5, is_training=False)
probabilities = tf.nn.softmax(logits)
checkpoint_path = tf.train.latest_checkpoint('/tmp/flowers-models/inception_v3')
init_fn = slim.assign_from_checkpoint_fn(
checkpoint_path, slim.get_model_variables('InceptionV3'))
with tf.Session() as sess:
init_fn(sess)
np_image, probabilities = sess.run([processed_images, probabilities])
probabilities = probabilities[0, 0:]
sorted_inds = [i[0] for i in sorted(enumerate(-probabilities), key=lambda x: x[1])]
names = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']
for i in range(5):
index = sorted_inds[i]
print((probabilities[index], names[index]))
然而,我的最终目标是在tensorflow服务中加载此模型,我不确定下一步如何将我的模型转换为服务理解的格式?tensorflow Slim使用了不推荐的“SessionBundle”。我目前正在将tensorflow/slim(tf.train.saver)生成的检查点转换为SavedModel格式(tf.saved\u model) “tensorflow/contrib/session\u bundle/session\u bundle.py”中的“session\u bundle.load\u session\u bundle\u from\u path”返回会话和元图定义。其目的是手动构建savedModel(),但无法从meta_graph_def获取输入 目前使用Tensorflow 1.7,降级以查看是否有任何区别