item_size = h5py.special_dtype(dtype).itemsize

这行代码是用来获取给定数据类型的字节大小。`dtype` 是一个数据类型对象，而 `h5py.special_dtype` 是 h5py 库中的一个函数，用于创建 HDF5 的特殊数据类型。`.itemsize` 是 numpy 数组对象的一个属性，用于返回数组中每个元素的字节大小。所以，`item_size` 变量将保存 `dtype` 对应数据类型的字节大小。

相关问题

class StockTradingEnv(gym.Env): metadata = {'render.modes': ['human']} def __init__(self, data, window_size): super(StockTradingEnv, self).__init__() self.data = data self.window_size = window_size self.action_space = spaces.Discrete(3) # 买入，卖出，持有 self.observation_space = spaces.Box(low=0, high=1, shape=(6, self.window_size + 1), dtype=np.float32) self.profit = 0 self.total_reward = 0 self.current_step = self.window_size self.done = False

这段代码是一个基于 Gym 库实现的股票交易环境 StockTradingEnv，其中包括了环境的初始化、动作空间、状态空间、当前状态等信息。具体来说，这个环境中的动作空间为三个离散值，分别代表买入、卖出和持有；状态空间为一个 6x(window_size+1) 的矩阵，其中包括了当前股票的开、高、低、收、成交量以及当前持有的股票数量；当前状态为当前时间步的窗口大小加一。

def model(self): num_classes = self.config.get("CNN_training_rule", "num_classes") seq_length = self.config.get("CNN_training_rule", "seq_length") conv1_num_filters = self.config.get("CNN_training_rule", "conv1_num_filters") conv1_kernel_size = self.config.get("CNN_training_rule", "conv1_kernel_size") conv2_num_filters = self.config.get("CNN_training_rule", "conv2_num_filters") conv2_kernel_size = self.config.get("CNN_training_rule", "conv2_kernel_size") hidden_dim = self.config.get("CNN_training_rule", "hidden_dim") dropout_keep_prob = self.config.get("CNN_training_rule", "dropout_keep_prob") model_input = keras.layers.Input((seq_length,1), dtype='float64') # conv1形状[batch_size, seq_length, conv1_num_filters] conv_1 = keras.layers.Conv1D(conv1_num_filters, conv1_kernel_size, padding="SAME")(model_input) conv_2 = keras.layers.Conv1D(conv2_num_filters, conv2_kernel_size, padding="SAME")(conv_1) max_poolinged = keras.layers.GlobalMaxPool1D()(conv_2) full_connect = keras.layers.Dense(hidden_dim)(max_poolinged) droped = keras.layers.Dropout(dropout_keep_prob)(full_connect) relued = keras.layers.ReLU()(droped) model_output = keras.layers.Dense(num_classes, activation="softmax")(relued) model = keras.models.Model(inputs=model_input, outputs=model_output) # model.compile(loss="categorical_crossentropy", # optimizer="adam", # metrics=["accuracy"]) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) print(model.summary()) return model给这段代码每行加上注释

def model(self):
    # 获取配置文件中的参数
    num_classes = self.config.get("CNN_training_rule", "num_classes") #分类数
    seq_length = self.config.get("CNN_training_rule", "seq_length") #序列长度
    conv1_num_filters = self.config.get("CNN_training_rule", "conv1_num_filters") #第一层卷积核数量
    conv1_kernel_size = self.config.get("CNN_training_rule", "conv1_kernel_size") #第一层卷积核大小
    conv2_num_filters = self.config.get("CNN_training_rule", "conv2_num_filters") #第二层卷积核数量
    conv2_kernel_size = self.config.get("CNN_training_rule", "conv2_kernel_size") #第二层卷积核大小
    hidden_dim = self.config.get("CNN_training_rule", "hidden_dim") #全连接层隐藏层大小
    dropout_keep_prob = self.config.get("CNN_training_rule", "dropout_keep_prob") #dropout保留率

    # 定义模型输入
    model_input = keras.layers.Input((seq_length,1), dtype='float64')

    # 第一层卷积
    conv_1 = keras.layers.Conv1D(conv1_num_filters, conv1_kernel_size, padding="SAME")(model_input)

    # 第二层卷积
    conv_2 = keras.layers.Conv1D(conv2_num_filters, conv2_kernel_size, padding="SAME")(conv_1)

    # 全局最大池化
    max_poolinged = keras.layers.GlobalMaxPool1D()(conv_2)

    # 全连接层
    full_connect = keras.layers.Dense(hidden_dim)(max_poolinged)

    # dropout层
    droped = keras.layers.Dropout(dropout_keep_prob)(full_connect)

    # relu激活层
    relued = keras.layers.ReLU()(droped)

    # 输出层
    model_output = keras.layers.Dense(num_classes, activation="softmax")(relued)

    # 定义模型
    model = keras.models.Model(inputs=model_input, outputs=model_output)

    # 编译模型
    model.compile(loss='binary_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])

    # 打印模型结构
    print(model.summary())

    return model