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Tensorflow 2.3.0代码规范

基于Tensorflow2.3.0框架的MNIST图像分类，训练数据集tf_train_data2.zip点击这里下载。
如下所示是其超参搜索任务中一个超参数组合的训练代码，代码会通过argparse模块接受在平台中填写的信息，请保持一致。

tensorflow2.3_autosearch.py示例代码

# -*- coding:utf-8 -*- """ tensorflow2 train demo """ import tensorflow as t

f import os import numpy as np import time import argparse from rudder_autosearch.

sdk.amaas_tools import AMaasTools def parse_arg(): """parse arguments""" parser = argparse.

ArgumentParser(description='tensorflow2.3 mnist Example') parser.add_argument('--train_dir', 

type=str, default='./train_data', help='input data dir for training (default: ./train_data)'

) parser.add_argument('--test_dir', type=str, default='./test_data', help='input data dir for

 test (default: ./test_data)') parser.add_argument('--output_dir', type=str, default='./output',

 help='output dir for auto_search job (default: ./output)') parser.add_argument('--job_id',

 type=str, default="job-1234", help='auto_search job id (default: "job-1234")') parser

.add_argument('--trial_id', type=str, default="0-0", help='auto_search id of a single trial

 (default: "0-0")') parser.add_argument('--metric', type=str, default="acc", help='evaluation 

metric of the model') parser.add_argument('--data_sampling_scale', type=float, default=1.0, 

help='sampling ratio of the data (default: 1.0)') parser.add_argument('--batch_size', type=int, 

default=100, help='number of images input in an iteration (default: 100)') parser.add_argument('--lr', 

type=float, default=0.001, help='learning rate of the training (default: 0.001)') parser.add_argument

('--epoch', type=int, default=5, help='number of epochs to train (default: 5)') 

args = parser.parse_args() args.output_dir = os.path.join(args.output_dir, args.job_id, 

args.trial_id) if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) print

("job_id: {}, trial_id: {}".format(args.job_id, args.trial_id)) return args def load_data

(data_sampling_scale): """ load data """ mnist = tf.keras.datasets.mnist
    work_path = os.getcwd() (x_train, y_train), (x_test, y_test) 

= mnist.load_data('%s/train_data/mnist.npz' % work_path) # 

sample training data np.random.seed(0) sample_data_num = int(data_sampling_scale 

* len(x_train)) idx = np.arange(len(x_train)) np.random.shuffle(idx) x_train, y_train =

 x_train[0:sample_data_num], y_train[0:sample_data_num] x_train, x_test = x_train / 

255.0, x_test / 255.0 return (x_train, x_test), (y_train, y_test) def Model(learning_rate):

"""Model""" model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), 

tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10,

 activation='softmax') ]) model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate), loss=

'sparse_categorical_crossentropy', metrics=['accuracy']) return model def evaluate(model, 

x_test, y_test): """evaluate""" loss, acc = model.evaluate(x_test, y_test, verbose=2) print(

"accuracy: %f" % acc) return acc def report_final(args, metric): """report_final_result""" 

# 结果上报sdk amaas_tools = AMaasTools(args.job_id, args.trial_id) metric_dict = {args.metric: 

metric} for i in range(3): flag, ret_msg = amaas_tools.report_final_result(metric=metric_dict, 

export_model_path=args.output_dir, checkpoint_path="") print("End Report, metric:{}, ret_msg:

{}".format(metric, ret_msg)) if flag: break time.sleep(1) assert flag, "Report final result

 to manager failed! Please check whether manager'address or manager'status " \ "is ok! 

" def main(): """main""" # 获取参数 args = parse_arg() # 加载数据集 (x_train, x_test), 

(y_train, y_test) = load_data(args.data_sampling_scale) # 模型定义 model = Model(args.lr) 

# 模型训练 model.fit(x_train, y_train, epochs=args.epoch, batch_size=args.batch_size)

 # 模型保存 model.save(args.output_dir) # 模型评估 acc = evaluate(model, x_test, y_test) 

# 上报结果 report_final(args, metric=acc) if __name__ == '__main__': main()

示例代码对应的yaml配置如下，请保持格式一致

pwo_search_demo.yml示例内容

#搜索算法参数
search_strategy:
  algo: PARTICLE_SEARCH #搜索策略：粒子群算法
  params:
    population_num: 8 #种群个体数量 ｜ [1,10] int类型
    round: 10 #迭代轮数  ｜[5,50] int类型
    inertia_weight: 0.5 # 惯性权重  ｜(0，1] float类型
    global_acceleration: 1.5 #全局加速度 ｜(0,4] float类型
    local_acceleration: 1.5 #个体加速度  ｜(0,4] float类型

#单次训练时数据的采样比例，单位%
data_sampling_scale: 100 #｜（0，100] int类型

#评价指标参数
metrics:
  name: acc #评价指标  ｜ 任意字符串 str类型
  goal: MAXIMIZE #最大值/最小值 ｜ str类型   MAXIMIZE or MINIMIZE   必须为这两个之一（也即支持大写）
  expected_value: 100 #早停标准值，评价指标超过该值则结束整个超参搜索，单位%  ｜无限制 int类型

#搜索参数空间
search_space:
  batch_size:
    htype: choice
    value: [100, 200, 300, 400, 500, 600]
  lr:
    htype: loguniform
    value: [0.0001, 0.9]
  epoch:
    htype: choice
    value: [5, 10, 12]