在不同长度的成批序列上训练LSTM网络的最佳方法是什么？

Question

所以我有一个序列到序列的问题，输入是许多不同长度的多变量序列，输出是一个与输入对应的长度相同的二进制向量序列。我把长度相同的序列归在一起，放在一个单独的文件夹里，然后像这样调用拟合函数。

for e in range(epochs):
    print('Epoch', e+1)
    for i in range(3,19):
        train_x_batch,train_y_batch,batch_size= get_data(i)
        history=model.fit_(train_x_batch,train_y_batch,
                    batch_size=batch_size,
                    validation_split=0.15,
                    callbacks=[tensorboard_cb])

def get_data(i):
    train_x = np.load(os.path.join(cwd, "lab_values","batches",f"f_{i}","train_x.npy"), allow_pickle=True)
    train_y = np.load(os.path.join(cwd, "lab_values","batches",f"f_{i}","train_y.npy"), allow_pickle=True)
    print(f"batch no {i} Train X size= ", train_x.shape)
    print(f"batch no {i} Train Y size= ", train_y.shape)
    batch_Size=train_x.shape[0]
    return train_x,train_y,batch_size

所以问题是有什么更好的方法吗？我听说我可以使用一个生成器来实现这个功能，可惜我无法实现这样的生成器。

Answer 1

你是想对整个数据进行训练 (npy file) 而不是分批训练模型。

我们可以写一个 Generator 并对模型进行训练 Batches.

我们使用代码从现有的Numpy文件中提取数据批次。

train_x = np.load(os.path.join(cwd, "lab_values","batches",f"f_{i}","train_x.npy"), mmap_mode='r', allow_pickle=True)

和

x_batch = train_x[start:end].copy().

完整的代码 Generator 和代码的 Training 如下图所示。

import numpy as np

for e in range(epochs):
    print('Epoch', e+1)
    for i in range(3,19):
        #train_x_batch,train_y_batch = get_data(i)
        batch_size = 32
        history=model.fit_(get_data(i),
                    batch_size=batch_size,
                    validation_split=0.15,
                    callbacks=[tensorboard_cb],epochs = 20
                          steps_per_epoch = 500, val_steps = 10)

def get_data(i):
    train_x = np.load(os.path.join(cwd, "lab_values","batches",f"f_{i}","train_x.npy"), 
                      mmap_mode='r', allow_pickle=True)
    train_y = np.load(os.path.join(cwd, "lab_values","batches",f"f_{i}","train_y.npy"),
                      mmap_mode='r', allow_pickle=True)
    print(f"batch no {i} Train X size= ", train_x.shape)
    print(f"batch no {i} Train Y size= ", train_y.shape)
    Number_Of_Rows = train_x.shape[0]
    batch_size = 32
    start = np.random.choice(Number_of_Rows - batch_size)
    end = start + batch_size
    x_batch = train_x[start:end].copy()
    y_batch = train_y[start:end].copy()        
    yield x_batch,y_batch

更多信息请参考以下内容 问题 而这 问题 也。