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def get_model_keras(self):
"""构建多输入模型"""
input_x = Input(shape=(self.feature_dim,), name='inputs')
hidden = Dense(256, activation='relu', name='hidden1')(input_x)
output = Dropout(rate=0.5)(hidden)
output = Dense(16, activation='relu', name='hidden2')(output)
output = Dropout(0.5)(output)
output = Dense(1, activation='sigmoid', name='outputs')(output)
model = Model(inputs=input_x, outputs=output)
adam = optimizers.Adam(lr=self.learning_rate)
model.compile(optimizer=adam, loss=[self.loss], metrics=['accuracy'])
# model.summary()
return model

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def export(self):
train_model = load_model(self.h5_model_name, compile=False)
sess = tf.keras.backend.get_session()
tf.saved_model.simple_save(
sess,
self.saved_model_dir,
inputs={'inputs': train_model.inputs},
outputs={'outputs': train_model.outputs})

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# 开启会话运行结果
with tf.Session(config=tf.ConfigProto()) as sess:
sess.run(tf.local_variables_initializer())
# 只需要指定要恢复模型的session，模型的 tag，模型的保存路径即可,
meta_graph_def = tf.saved_model.loader.load(sess, ["serve"], saved_model_dir)
# 名字与保存模型时定义的一致
signature_key = 'serving_default'
signature = meta_graph_def.signature_def
input_name = signature[signature_key].inputs["inputs"].name
output_name = signature[signature_key].outputs["outputs"].name
print(input_name, output_name)
input_tensor = sess.graph.get_tensor_by_name(input_name)
output_tensor = sess.graph.get_tensor_by_name(output_name)
try:
while not coord.should_stop():
# batch_features_data 为输入的数据
pred_scores = [x[0] for x in sess.run(output_tensor, feed_dict={input_tensor: batch_features_data})]
except tf.errors.OutOfRangeError:
print("done")
finally:
# 结束子线程
coord.request_stop()
# 等待子线程结束
coord.join(threads)