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正如我所说的，我最终意识到模型中的模式是通过 TF 工程化的东西。这一点引领着我我设计了一个非常简单的类（class），其可以由我未来的模型所扩展。 我并不是继承类别（class inheritance）的热衷者，但我也不是永远清晰复写一段相同代码的热衷者。当你在进行机器学习项目时，模型通过你使用的框架共享了许多相似之处。 所以我试图找到一个避免继承的（inheritance）已知香蕉问题（banana problem）的实现，这是通过让一个继承尽可能地深而达到。 要完全清楚，我们需要将这一类别作为以后模型的顶部父级类别（top parent），令你模型的构建在一行使用一个变元（one argument）：配置（the configuration）。 为了更进一步理解，我们将为你直接展示注释文件（commented file）： import os, copyimport tensorflow as tfclass BasicAgent(object): # To build your model, you only to pass a "configuration" which is a dictionary def __init__(self, config): # I like to keep the best HP found so far inside the model itself # This is a mechanism to load the best HP and override the configuration if config['best']: config.update(self.get_best_config(config['env_name'])) # I make a `deepcopy` of the configuration before using it # to avoid any potential mutation when I iterate asynchronously over configurations self.config = copy.deepcopy(config) if config['debug']: # This is a personal check i like to do print('config', self.config) # When working with NN, one usually initialize randomly # and you want to be able to reproduce your initialization so make sure # you store the random seed and actually use it in your TF graph (tf.set_random_seed() for example) self.random_seed = self.config['random_seed'] # All models share some basics hyper parameters, this is the section where we # copy them into the model self.result_dir = self.config['result_dir'] self.max_iter = self.config['max_iter'] self.lr = self.config['lr'] self.nb_units = self.config['nb_units'] # etc. # Now the child Model needs some custom parameters, to avoid any # inheritance hell with the __init__ function, the model # will override this function completely self.set_agent_props() # Again, child Model should provide its own build_grap function self.graph = self.build_graph(tf.Graph()) # Any operations that should be in the graph but are common to all models # can be added this way, here with self.graph.as_default(): self.saver = tf.train.Saver( max_to_keep=50, ) # Add all the other common code for the initialization here gpu_options = tf.GPUOptions(allow_growth=True) sessConfig = tf.ConfigProto(gpu_options=gpu_options) self.sess = tf.Session(config=sessConfig, graph=self.graph) self.sw = tf.summary.FileWriter(self.result_dir, self.sess.graph) # This function is not always common to all models, that's why it's again # separated from the __init__ one self.init() # At the end of this function, you want your model to be ready! def set_agent_props(self): # This function is here to be overriden completely. # When you look at your model, you want to know exactly which custom options it needs. pass def get_best_config(self): # This function is here to be overriden completely. # It returns a dictionary used to update the initial configuration (see __init__) return {} @staticmethod def get_random_config(fixed_params={}): # Why static? Because you want to be able to pass this function to other processes # so they can independently generate random configuration of the current model raise Exception('The get_random_config function must be overriden by the agent') def build_graph(self, graph): raise Exception('The build_graph function must be overriden by the agent') def infer(self): raise Exception('The infer function must be overriden by the agent') def learn_from_epoch(self): # I like to separate the function to train per epoch and the function to train globally raise Exception('The learn_from_epoch function must be overriden by the agent') def train(self, save_every=1): # This function is usually common to all your models, Here is an example: for epoch_id in range(0, self.max_iter): self.learn_from_epoch() # If you don't want to save during training, you can just pass a negative number if save_every > 0 and epoch_id % save_every == 0: self.save() def save(self): # This function is usually common to all your models, Here is an example: global_step_t = tf.train.get_global_step(self.graph) global_step, episode_id = self.sess.run([global_step_t, self.episode_id]) if self.config['debug']: print('Saving to %s with global_step %d' % (self.result_dir, global_step)) self.saver.save(self.sess, self.result_dir + '/agent-ep_' + str(episode_id), global_step) # I always keep the configuration that if not os.path.isfile(self.result_dir + '/config.json'): config = self.config if 'phi' in config: del config['phi'] with open(self.result_dir + '/config.json', 'w') as f: json.dump(self.config, f) def init(self): # This function is usually common to all your models # but making separate than the __init__ function allows it to be overidden cleanly # this is an example of such a function checkpoint = tf.train.get_checkpoint_state(self.result_dir) if checkpoint is None: self.sess.run(self.init_op) else: if self.config['debug']: print('Loading the model from folder: %s' % self.result_dir) self.saver.restore(self.sess, checkpoint.model_checkpoint_path) def infer(self): # This function is usually common to all your models pass基本模型文件 一些注释： 关于我配置的「最佳」属性：人们通常在没有模型最优超参数的情况下传送代码，有人知道为什么吗？ (责任编辑：本港台直播) |