import tensorflow as tf
from trainsetting import train_db, dev_db
import models
import setting
# 从models文件中导入模型
model = models.my_densenet()
model.summary()
# 配置优化器、损失函数、以及监控指标
model.compile(tf.keras.optimizers.Adam(setting.LEARNING_RATE),
loss=tf.keras.losses.categorical_crossentropy,
metrics=['accuracy'])
# 在每个epoch结束后尝试保存模型参数，只有当前参数的val_accuracy比之前保存的更优时，才会覆盖掉之前保存的参数
model_check_point = tf.keras.callbacks.ModelCheckpoint(
filepath=setting.MODEL_PATH, monitor='val_accuracy', save_best_only=True)
# 使用tf.keras的高级接口进行训练
model.fit_generator(train_db,
epochs=setting.TRAIN_EPOCHS,
validation_data=dev_db,
callbacks=[model_check_point])
# @File : app.py
# @Author : AaronJny
# @Time : 2019/12/18
# @Desc :
import tensorflow as tf
from flask import Flask
from flask import jsonify
from flask import request, render_template
import settings
from models import my_densenet
app = Flask(__name__)
# 导入模型
model = my_densenet()
# 加载训练好的参数
model.load_weights(settings.MODEL_PATH)
@app.route('/', methods=['GET'])
def index():
"""
首页，vue入口
"""
return render_template('index.html')
@app.route('/api/v1/pets_classify/', methods=['POST'])
def pets_classify():
"""