Question

我正在使用深度流处理人类活动识别问题。

每个样本都是一个大小为（20,3，no_of_frames）的Matlab文件，即每帧中有20行和3个cols，不同样本的帧数可能不同。

我用0填充所有样本，以便所有样本都包含相同的帧数（比如说100）。

所以现在所有的样本都是大小（20,3,100）。

同样，样本的总数不得超过400，而且没有类别为10。

如何安排我的数据集以便在Keras中使用LSTM。您是否还会为分类目的建议一些基本的LSTM模型？

Answer 1

    Here is my code to create dataset:

    clc;
    clear all;

    ctr=0;
    longest=326; % length of longest sequence
    gyroData = zeros(431,longest*3);
    gyroLabel=zeros(431,1);

    for a=1:27
        for s=1:2:7
            for t=1:4
                fname = strcat('a', int2str(a), '_s', int2str(s), '_t', int2str(t),'_inertial.mat');
                if exist(fname)
                    load(fname);
                    d_iner = d_iner(:,1:3);
                    r = size(d_iner,1);
                    d_iner = cat(1,d_iner,zeros(longest-r,3)); % do zero padding to longest sequence
                    ctr = ctr+1;
                    d_iner = d_iner';
                    gyroData(ctr,:,:) = d_iner(:);
                    gyroLabel(ctr,1)=a-1;
                 end
            end
        end
    end

    n1 = randperm(ctr);

    for i=1:ctr
        if i==1
            x1=gyroData(n1(i),:);
            y1=gyroLabel(n1(i),1);
        else
            x1=cat(1,x1,gyroData(n1(i),:));
            y1=cat(1,y1,gyroLabel(n1(i),1));
        end
    end

    %%
    ctr=0;
    gyroData = zeros(430,longest*3);
    gyroLabel=zeros(430,1);

    for a=1:27
        for s=2:2:8
            for t=1:4
                fname = strcat('a', int2str(a), '_s', int2str(s), '_t', int2str(t),'_inertial.mat');
                if exist(fname)
                    load(fname);
                    d_iner = d_iner(:,1:3);
                    r = size(d_iner,1);
                    d_iner = cat(1,d_iner,zeros(longest-r,3)); % do zero padding to longest sequence
                    ctr = ctr+1;
                    d_iner = d_iner';
                    gyroData(ctr,:,:) = d_iner(:);
                    gyroLabel(ctr,1)=a-1;
                end
            end
        end
    end

    n1 = randperm(ctr);

    for i=1:ctr
        if i==1
            x2=gyroData(n1(i),:);
            y2=gyroLabel(n1(i),1);
        else
            x2=cat(1,x2,gyroData(n1(i),:));
            y2=cat(1,y2,gyroLabel(n1(i),1));
        end
    end

    save('inertial_padded.mat', 'x1', 'y1', 'x2', 'y2');


**And this is my LSTM code in Keras**

import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
import scipy.io

# fix random seed for reproducibility
np.random.seed(7)

mat = scipy.io.loadmat('inertial_padded.mat')
x_train = mat['x1']
y_train = mat['y1']
x_test = mat['x2']
y_test = mat['y2']

data_dim = 3
timesteps = 326
num_classes = 27

x_train = x_train.reshape(x_train.shape[0], 326,3)
x_test = x_test.reshape(x_test.shape[0], 326, 3)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')

y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')


model = Sequential()
model.add(LSTM(32, input_shape=(timesteps, data_dim), activation='sigmoid'))  
#model.add(LSTM(32, activation='tanh')) # returns a sequence of vectors of dimension 32
#model.add(LSTM(32))  # return a single vector of dimension 32
model.add(Dense(27, activation='softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

print(model.summary())
model.fit(x_train, y_train,
          batch_size=16, epochs=25,
          validation_data=(x_test, y_test))
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))

如何使用LSTM进行序列分类？

1 个答案: