andersbll / nnet Goto Github PK

#!/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()