更新时间:2023-12-02 22:05:34
您必须导出模型.我有一个 PR 在再训练期间导出模型.其要点如下:
You have to export the model. I have a PR that exports the model during retraining. The gist of it is below:
import tensorflow as tf
def export_model(sess, architecture, saved_model_dir):
if architecture == 'inception_v3':
input_tensor = 'DecodeJpeg/contents:0'
elif architecture.startswith('mobilenet_'):
input_tensor = 'input:0'
else:
raise ValueError('Unknown architecture', architecture)
in_image = sess.graph.get_tensor_by_name(input_tensor)
inputs = {'image': tf.saved_model.utils.build_tensor_info(in_image)}
out_classes = sess.graph.get_tensor_by_name('final_result:0')
outputs = {'prediction': tf.saved_model.utils.build_tensor_info(out_classes)}
signature = tf.saved_model.signature_def_utils.build_signature_def(
inputs=inputs,
outputs=outputs,
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME
)
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
# Save out the SavedModel.
builder = tf.saved_model.builder.SavedModelBuilder(saved_model_dir)
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature
},
legacy_init_op=legacy_init_op)
builder.save()
上面会创建一个变量目录和saved_model.pb文件.如果你把它放在代表版本号的父目录下(例如 1/),那么你可以通过以下方式调用 tensorflow 服务:
Above will create a variables directory and saved_model.pb file. If you put it under a parent directory representing the version number (e.g. 1/) then you can call tensorflow serving via:
tensorflow_model_server --port=9000 --model_name=inception --model_base_path=/path/to/saved_models/