tensorflow中sparse_placeholder在saved_model中保存pb模型的使用方法

# coding:utf-8# @author: liu
# @file: sparse_tensor_pb.py
# @time: 2020/4/3 11:00
# @desc:# coding:utf-8import tensorflow as tf
import random
import numpy as np
import os
import shutilprint(tf.__version__)def create_sparse(batch_size, dtype=np.int32):'''创建稀疏张量,ctc_loss中labels要求是稀疏张量,随机生成序列长度在150～180之间的labels'''indices = []values = []for i in range(batch_size):length = random.randint(150, 180)for j in range(length):indices.append((i, j))value = random.randint(0, 779)values.append(value)indices = np.asarray(indices, dtype=np.int64)values = np.asarray(values, dtype=dtype)shape = np.asarray([batch_size, np.asarray(indices).max(0)[1] + 1], dtype=np.int64)  # [64,180]return [indices, values, shape]# 保存成pb模型def saved_model(sess: tf.Session, input: tf.sparse_placeholder , ss, cc, model_path):if os.path.exists(model_path):shutil.rmtree(model_path)builder = tf.saved_model.builder.SavedModelBuilder(model_path)# input_x = tf.saved_model.build_tensor_info(input)indices = tf.saved_model.build_tensor_info(input.indices)values = tf.saved_model.build_tensor_info(input.values)dense_shape = tf.saved_model.build_tensor_info(input.dense_shape)output_a = tf.saved_model.build_tensor_info(ss)output_b = tf.saved_model.build_tensor_info(cc)prediction_signature = tf.saved_model.signature_def_utils.build_signature_def(inputs={"indices": indices, "values": values, "dense_shapes": dense_shape},outputs={"ss": output_a, "cc": output_b},method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)builder.add_meta_graph_and_variables(sess, [tf.saved_model.tag_constants.SERVING], signature_def_map={"predict": prediction_signature})builder.save()def load_model(model_path=None):sess = tf.Session()meta_graph = tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], model_path)signature = meta_graph.signature_def# input_x = signature["predict"].inputs["input"].nameindices = signature["predict"].inputs["indices"].namevalues = signature["predict"].inputs["values"].namedense_shape = signature["predict"].inputs["dense_shapes"].nameoutput_a = signature["predict"].outputs["ss"].nameoutput_b = signature["predict"].outputs["cc"].name# return sess, input_x, output_a, output_breturn sess, indices, values, dense_shape, output_a, output_bdef train():a = tf.sparse_placeholder(tf.float32, name="in")values = a.valuesc = tf.sparse_to_dense(a.indices, a.dense_shape, a.values)s = tf.sparse.reduce_sum(a)indices_list = [[0, 1], [0, 4], [2, 3]]# print(indices_list)values_list = [1, 4, 5]dense_shape_list = [4, 5]with tf.Session() as sess:sess.run(tf.global_variables_initializer())ss, cc, va = sess.run([s, c, values], feed_dict={a: tf.SparseTensorValue(indices=indices_list, values=values_list, dense_shape=dense_shape_list)})print(ss)print("-----------")print(cc)# print("va", va)saved_model(sess, a, s, c, "model/1")def predict():indices_list = [[0, 1], [0, 4], [2, 3]]# print(indices_list)values_list = [1, 4, 5]dense_shape_list = [4, 5]# sess, input_x, output_a, output_b = load_model("model/1")sess, indices, values, dense_shape, output_a, output_b = load_model("model/1")with sess:# a, b = sess.run([output_a, output_b], {input_x: tf.SparseTensorValue(indices=indices_list, values=values_list, dense_shape=dense_shape_list)})a, b = sess.run([output_a, output_b], feed_dict={indices:indices_list, values:values_list, dense_shape:dense_shape_list})print("预测结果")print(a)print(b)train()
predict()

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tensorflow中sparse_placeholder在saved_model中保存pb模型的使用方法

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