1. 实验结果纪录

纪录一下调整参数带来的结果.不过语音识别这块完全不熟.

1.1 Simple Baseline

acc>0.45797

直接上传助教代码

1.2 Middle Baseline

acc>0.69747
助教给与了提示需要我们将frame拼接,可以达到更好的训练效果.进行了几轮尝试
(1) 拼接11维frame,将39维改为39 * 11 维
实际还是没过baseline,从后面的实验结果来看需要增加模型结构的复杂程度.

(2) 拼接11维frame,将模型结构的结点层数定义为3层,增加dropout(0.5)
修改后欠拟合了,需要将dropout调小.

def __init__(self, input_dim, output_dim):
        super(BasicBlock, self).__init__()

        self.block = nn.Sequential(
            nn.Linear(input_dim, output_dim),
            nn.ReLU(),
            nn.Dropout(0.5),
        )

(3) 训练轮数调整到25,学习率不变,隐藏层增加到6,隐藏层维数调整到1024,dropout调整到0.1,其余与上一致
擦线过Middle Baseline,在训练的时候可以发现train set的acc过了0.8,而test set的acc只能保持在0.69.

1.3 Strong Baseline

acc>0.75028

(1) dropout 调整到 0.25
因为之前的训练可以观察到明显的过拟合,因此将dropout调大至0.25,提高了一点.此处的train set的acc和test set的acc基本一致,保持在大约0.7的位置.

(2) batch_size调到2048,lr调至0.001
这里按李老师提示,实验判断模型宽浅以及窄深对训练哪个更好

超参数设置

# data prarameters
concat_nframes = 11              # the number of frames to concat with, n must be odd (total 2k+1 = n frames)
train_ratio = 0.8               # the ratio of data used for training, the rest will be used for validation

# training parameters
seed = 0                        # random seed
batch_size = 2048                # batch size
num_epoch = 25                   # the number of training epoch
learning_rate = 0.001          # learning rate
model_path = './model.ckpt'     # the path where the checkpoint will be saved

# model parameters
input_dim = 39 * concat_nframes # the input dim of the model, you should not change the value
hidden_layers = 6               # the number of hidden layers
hidden_dim = 1024               # the hidden dim

使用torchsummary看模型参数:

只有一个过了middle baseline.

观察模型变深对训练的影响,将隐藏层设置为8:

观察模型变窄对训练的影响,将隐藏维度设置为2048,比加深效果好点,但都不如原来: