High error on time-series data #1
Description
Thank you so much for making such readable code for beginners in convolutional neural networks like me.
I am trying to get some results with this code using time-series, xyz acceleration data.
As opposed to images which are 2D, 3-channel data, time-series are 1D, 3-channel data. I have run the cnn_mnist examples included in this code, and have achieved satisfactory results. However, with the scaled time-series data that I have, I couldn't get any decent results with the same parameters and layer configurations. I suppose convolution computation for 1D and 2D is entirely different, and that I need to apply internal modifications if I want this to work. Please see below for the code I used.
Do I need to make modifications on the matrix operations level (convolution)? Where is this part in the code?
Note: I am also a python beginner, but I can understand the basic hierarchy of the code.
Code:
#!/usr/bin/env python
# coding: utf-8
import time
import numpy as np
import sklearn.datasets
import nnet
def run():
# Data (https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones)
# imgs has shape (n_imgs, n_channels_in, img_h, img_w)
# filters has shape (n_channels_in, n_channels_out, img_h, img_w)
# convout has shape (n_imgs, n_channels_out, img_h, img_w)
xtrain = np.genfromtxt('/home/sclab/UCI_HAR_Dataset/train/Inertial Signals/body_acc_x_trainScaled.csv', delimiter=',')
ytrain = np.genfromtxt('/home/sclab/UCI_HAR_Dataset/train/Inertial Signals/body_acc_y_trainScaled.csv', delimiter=',')
ztrain = np.genfromtxt('/home/sclab/UCI_HAR_Dataset/train/Inertial Signals/body_acc_z_trainScaled.csv', delimiter=',')
traindata = np.concatenate((xtrain, ytrain, ztrain), axis = 1)
X_train = np.reshape(traindata, (-1, 3, 1, 64))
y_train = np.genfromtxt('/home/sclab/UCI_HAR_Dataset/train/Inertial Signals/y_train.txt', dtype=int)
xtest = np.genfromtxt('/home/sclab/UCI_HAR_Dataset/test/Inertial Signals/body_acc_x_testScaled.csv', delimiter=',')
ytest = np.genfromtxt('/home/sclab/UCI_HAR_Dataset/test/Inertial Signals/body_acc_y_testScaled.csv', delimiter=',')
ztest = np.genfromtxt('/home/sclab/UCI_HAR_Dataset/test/Inertial Signals/body_acc_z_testScaled.csv', delimiter=',')
testdata = np.concatenate((xtest, ytest, ztest), axis = 1)
X_test = np.reshape(testdata, (-1, 3, 1, 64))
y_test = np.genfromtxt('/home/sclab/UCI_HAR_Dataset/test/Inertial Signals/y_test.txt', dtype=int)
n_classes = np.unique(y_train).size
# Setup convolutional neural network
nn = nnet.NeuralNetwork(
layers=[
nnet.Conv(
n_feats=12,
filter_shape=(3, 3),
strides=(1, 1),
weight_scale=0.1,
weight_decay=0.001,
),
nnet.Activation('relu'),
nnet.Pool(
pool_shape=(2, 2),
strides=(2, 2),
mode='max',
),
nnet.Conv(
n_feats=600,
filter_shape=(3, 3),
strides=(1, 1),
weight_scale=0.1,
weight_decay=0.001,
),
nnet.Activation('relu'),
nnet.Flatten(),
nnet.Linear(
n_out=n_classes,
weight_scale=0.1,
weight_decay=0.02,
),
nnet.LogRegression(),
],
)
# Train neural network
t0 = time.time()
nn.fit(X_train, y_train, learning_rate=0.05, max_iter=3, batch_size=32)
t1 = time.time()
print('Duration: %.1fs' % (t1-t0))
# Evaluate on test data
error = nn.error(X_test, y_test)
print('Test error rate: %.4f' % error)
if __name__ == '__main__':
run()