Emilie  Okumu

Emilie Okumu

1658418240

Human Activity Recognition Example using TensorFlow with LSTM

LSTMs for Human Activity Recognition

Human Activity Recognition (HAR) using smartphones dataset and an LSTM RNN. Classifying the type of movement amongst six categories:

  • WALKING,
  • WALKING_UPSTAIRS,
  • WALKING_DOWNSTAIRS,
  • SITTING,
  • STANDING,
  • LAYING.

Compared to a classical approach, using a Recurrent Neural Networks (RNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering. Data can be fed directly into the neural network who acts like a black box, modeling the problem correctly. Other research on the activity recognition dataset can use a big amount of feature engineering, which is rather a signal processing approach combined with classical data science techniques. The approach here is rather very simple in terms of how much was the data preprocessed.

Let's use Google's neat Deep Learning library, TensorFlow, demonstrating the usage of an LSTM, a type of Artificial Neural Network that can process sequential data / time series.

Video dataset overview

Follow this link to see a video of the 6 activities recorded in the experiment with one of the participants:

Video of the experiment

[Watch video]

 

Details about the input data

I will be using an LSTM on the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this:

The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.

That said, I will use the almost raw data: only the gravity effect has been filtered out of the accelerometer as a preprocessing step for another 3D feature as an input to help learning. If you'd ever want to extract the gravity by yourself, you could fork my code on using a Butterworth Low-Pass Filter (LPF) in Python and edit it to have the right cutoff frequency of 0.3 Hz which is a good frequency for activity recognition from body sensors.

What is an RNN?

As explained in this article, an RNN takes many input vectors to process them and output other vectors. It can be roughly pictured like in the image below, imagining each rectangle has a vectorial depth and other special hidden quirks in the image below. In our case, the "many to one" architecture is used: we accept time series of feature vectors (one vector per time step) to convert them to a probability vector at the output for classification. Note that a "one to one" architecture would be a standard feedforward neural network.

RNN Architectures Learn more on RNNs

What is an LSTM?

An LSTM is an improved RNN. It is more complex, but easier to train, avoiding what is called the vanishing gradient problem. I recommend this course for you to learn more on LSTMs.

Learn more on LSTMs

Results

Scroll on! Nice visuals awaits.

# All Includes

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import tensorflow as tf  # Version 1.0.0 (some previous versions are used in past commits)
from sklearn import metrics

import os
# Useful Constants

# Those are separate normalised input features for the neural network
INPUT_SIGNAL_TYPES = [
    "body_acc_x_",
    "body_acc_y_",
    "body_acc_z_",
    "body_gyro_x_",
    "body_gyro_y_",
    "body_gyro_z_",
    "total_acc_x_",
    "total_acc_y_",
    "total_acc_z_"
]

# Output classes to learn how to classify
LABELS = [
    "WALKING",
    "WALKING_UPSTAIRS",
    "WALKING_DOWNSTAIRS",
    "SITTING",
    "STANDING",
    "LAYING"
]

Let's start by downloading the data:

# Note: Linux bash commands start with a "!" inside those "ipython notebook" cells

DATA_PATH = "data/"

!pwd && ls
os.chdir(DATA_PATH)
!pwd && ls

!python download_dataset.py

!pwd && ls
os.chdir("..")
!pwd && ls

DATASET_PATH = DATA_PATH + "UCI HAR Dataset/"
print("\n" + "Dataset is now located at: " + DATASET_PATH)
/home/ubuntu/pynb/LSTM-Human-Activity-Recognition
data     LSTM_files  LSTM_OLD.ipynb  README.md
LICENSE  LSTM.ipynb  lstm.py         screenlog.0
/home/ubuntu/pynb/LSTM-Human-Activity-Recognition/data
download_dataset.py  source.txt

Downloading...
--2017-05-24 01:49:53--  https://archive.ics.uci.edu/ml/machine-learning-databases/00240/UCI%20HAR%20Dataset.zip
Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.249
Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.249|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 60999314 (58M) [application/zip]
Saving to: ‘UCI HAR Dataset.zip’

100%[======================================>] 60,999,314  1.69MB/s   in 38s    

2017-05-24 01:50:31 (1.55 MB/s) - ‘UCI HAR Dataset.zip’ saved [60999314/60999314]

Downloading done.

Extracting...
Extracting successfully done to /home/ubuntu/pynb/LSTM-Human-Activity-Recognition/data/UCI HAR Dataset.
/home/ubuntu/pynb/LSTM-Human-Activity-Recognition/data
download_dataset.py  __MACOSX  source.txt  UCI HAR Dataset  UCI HAR Dataset.zip
/home/ubuntu/pynb/LSTM-Human-Activity-Recognition
data     LSTM_files  LSTM_OLD.ipynb  README.md
LICENSE  LSTM.ipynb  lstm.py         screenlog.0

Dataset is now located at: data/UCI HAR Dataset/

Preparing dataset:

TRAIN = "train/"
TEST = "test/"


# Load "X" (the neural network's training and testing inputs)

def load_X(X_signals_paths):
    X_signals = []

    for signal_type_path in X_signals_paths:
        file = open(signal_type_path, 'r')
        # Read dataset from disk, dealing with text files' syntax
        X_signals.append(
            [np.array(serie, dtype=np.float32) for serie in [
                row.replace('  ', ' ').strip().split(' ') for row in file
            ]]
        )
        file.close()

    return np.transpose(np.array(X_signals), (1, 2, 0))

X_train_signals_paths = [
    DATASET_PATH + TRAIN + "Inertial Signals/" + signal + "train.txt" for signal in INPUT_SIGNAL_TYPES
]
X_test_signals_paths = [
    DATASET_PATH + TEST + "Inertial Signals/" + signal + "test.txt" for signal in INPUT_SIGNAL_TYPES
]

X_train = load_X(X_train_signals_paths)
X_test = load_X(X_test_signals_paths)


# Load "y" (the neural network's training and testing outputs)

def load_y(y_path):
    file = open(y_path, 'r')
    # Read dataset from disk, dealing with text file's syntax
    y_ = np.array(
        [elem for elem in [
            row.replace('  ', ' ').strip().split(' ') for row in file
        ]],
        dtype=np.int32
    )
    file.close()

    # Substract 1 to each output class for friendly 0-based indexing
    return y_ - 1

y_train_path = DATASET_PATH + TRAIN + "y_train.txt"
y_test_path = DATASET_PATH + TEST + "y_test.txt"

y_train = load_y(y_train_path)
y_test = load_y(y_test_path)

Additionnal Parameters:

Here are some core parameter definitions for the training.

For example, the whole neural network's structure could be summarised by enumerating those parameters and the fact that two LSTM are used one on top of another (stacked) output-to-input as hidden layers through time steps.

# Input Data

training_data_count = len(X_train)  # 7352 training series (with 50% overlap between each serie)
test_data_count = len(X_test)  # 2947 testing series
n_steps = len(X_train[0])  # 128 timesteps per series
n_input = len(X_train[0][0])  # 9 input parameters per timestep


# LSTM Neural Network's internal structure

n_hidden = 32 # Hidden layer num of features
n_classes = 6 # Total classes (should go up, or should go down)


# Training

learning_rate = 0.0025
lambda_loss_amount = 0.0015
training_iters = training_data_count * 300  # Loop 300 times on the dataset
batch_size = 1500
display_iter = 30000  # To show test set accuracy during training


# Some debugging info

print("Some useful info to get an insight on dataset's shape and normalisation:")
print("(X shape, y shape, every X's mean, every X's standard deviation)")
print(X_test.shape, y_test.shape, np.mean(X_test), np.std(X_test))
print("The dataset is therefore properly normalised, as expected, but not yet one-hot encoded.")
Some useful info to get an insight on dataset's shape and normalisation:
(X shape, y shape, every X's mean, every X's standard deviation)
(2947, 128, 9) (2947, 1) 0.0991399 0.395671
The dataset is therefore properly normalised, as expected, but not yet one-hot encoded.

Utility functions for training:

def LSTM_RNN(_X, _weights, _biases):
    # Function returns a tensorflow LSTM (RNN) artificial neural network from given parameters.
    # Moreover, two LSTM cells are stacked which adds deepness to the neural network.
    # Note, some code of this notebook is inspired from an slightly different
    # RNN architecture used on another dataset, some of the credits goes to
    # "aymericdamien" under the MIT license.

    # (NOTE: This step could be greatly optimised by shaping the dataset once
    # input shape: (batch_size, n_steps, n_input)
    _X = tf.transpose(_X, [1, 0, 2])  # permute n_steps and batch_size
    # Reshape to prepare input to hidden activation
    _X = tf.reshape(_X, [-1, n_input])
    # new shape: (n_steps*batch_size, n_input)

    # ReLU activation, thanks to Yu Zhao for adding this improvement here:
    _X = tf.nn.relu(tf.matmul(_X, _weights['hidden']) + _biases['hidden'])
    # Split data because rnn cell needs a list of inputs for the RNN inner loop
    _X = tf.split(_X, n_steps, 0)
    # new shape: n_steps * (batch_size, n_hidden)

    # Define two stacked LSTM cells (two recurrent layers deep) with tensorflow
    lstm_cell_1 = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)
    lstm_cell_2 = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)
    lstm_cells = tf.contrib.rnn.MultiRNNCell([lstm_cell_1, lstm_cell_2], state_is_tuple=True)
    # Get LSTM cell output
    outputs, states = tf.contrib.rnn.static_rnn(lstm_cells, _X, dtype=tf.float32)

    # Get last time step's output feature for a "many-to-one" style classifier,
    # as in the image describing RNNs at the top of this page
    lstm_last_output = outputs[-1]

    # Linear activation
    return tf.matmul(lstm_last_output, _weights['out']) + _biases['out']


def extract_batch_size(_train, step, batch_size):
    # Function to fetch a "batch_size" amount of data from "(X|y)_train" data.

    shape = list(_train.shape)
    shape[0] = batch_size
    batch_s = np.empty(shape)

    for i in range(batch_size):
        # Loop index
        index = ((step-1)*batch_size + i) % len(_train)
        batch_s[i] = _train[index]

    return batch_s


def one_hot(y_, n_classes=n_classes):
    # Function to encode neural one-hot output labels from number indexes
    # e.g.:
    # one_hot(y_=[[5], [0], [3]], n_classes=6):
    #     return [[0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0]]

    y_ = y_.reshape(len(y_))
    return np.eye(n_classes)[np.array(y_, dtype=np.int32)]  # Returns FLOATS

Let's get serious and build the neural network:


# Graph input/output
x = tf.placeholder(tf.float32, [None, n_steps, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])

# Graph weights
weights = {
    'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])), # Hidden layer weights
    'out': tf.Variable(tf.random_normal([n_hidden, n_classes], mean=1.0))
}
biases = {
    'hidden': tf.Variable(tf.random_normal([n_hidden])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

pred = LSTM_RNN(x, weights, biases)

# Loss, optimizer and evaluation
l2 = lambda_loss_amount * sum(
    tf.nn.l2_loss(tf_var) for tf_var in tf.trainable_variables()
) # L2 loss prevents this overkill neural network to overfit the data
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=pred)) + l2 # Softmax loss
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer

correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

Hooray, now train the neural network:

# To keep track of training's performance
test_losses = []
test_accuracies = []
train_losses = []
train_accuracies = []

# Launch the graph
sess = tf.InteractiveSession(config=tf.ConfigProto(log_device_placement=True))
init = tf.global_variables_initializer()
sess.run(init)

# Perform Training steps with "batch_size" amount of example data at each loop
step = 1
while step * batch_size <= training_iters:
    batch_xs =         extract_batch_size(X_train, step, batch_size)
    batch_ys = one_hot(extract_batch_size(y_train, step, batch_size))

    # Fit training using batch data
    _, loss, acc = sess.run(
        [optimizer, cost, accuracy],
        feed_dict={
            x: batch_xs,
            y: batch_ys
        }
    )
    train_losses.append(loss)
    train_accuracies.append(acc)

    # Evaluate network only at some steps for faster training:
    if (step*batch_size % display_iter == 0) or (step == 1) or (step * batch_size > training_iters):

        # To not spam console, show training accuracy/loss in this "if"
        print("Training iter #" + str(step*batch_size) + \
              ":   Batch Loss = " + "{:.6f}".format(loss) + \
              ", Accuracy = {}".format(acc))

        # Evaluation on the test set (no learning made here - just evaluation for diagnosis)
        loss, acc = sess.run(
            [cost, accuracy],
            feed_dict={
                x: X_test,
                y: one_hot(y_test)
            }
        )
        test_losses.append(loss)
        test_accuracies.append(acc)
        print("PERFORMANCE ON TEST SET: " + \
              "Batch Loss = {}".format(loss) + \
              ", Accuracy = {}".format(acc))

    step += 1

print("Optimization Finished!")

# Accuracy for test data

one_hot_predictions, accuracy, final_loss = sess.run(
    [pred, accuracy, cost],
    feed_dict={
        x: X_test,
        y: one_hot(y_test)
    }
)

test_losses.append(final_loss)
test_accuracies.append(accuracy)

print("FINAL RESULT: " + \
      "Batch Loss = {}".format(final_loss) + \
      ", Accuracy = {}".format(accuracy))
WARNING:tensorflow:From <ipython-input-19-3339689e51f6>:9: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
Training iter #1500:   Batch Loss = 5.416760, Accuracy = 0.15266665816307068
PERFORMANCE ON TEST SET: Batch Loss = 4.880829811096191, Accuracy = 0.05632847175002098
Training iter #30000:   Batch Loss = 3.031930, Accuracy = 0.607333242893219
PERFORMANCE ON TEST SET: Batch Loss = 3.0515167713165283, Accuracy = 0.6067186594009399
Training iter #60000:   Batch Loss = 2.672764, Accuracy = 0.7386666536331177
PERFORMANCE ON TEST SET: Batch Loss = 2.780435085296631, Accuracy = 0.7027485370635986
Training iter #90000:   Batch Loss = 2.378301, Accuracy = 0.8366667032241821
PERFORMANCE ON TEST SET: Batch Loss = 2.6019773483276367, Accuracy = 0.7617915868759155
Training iter #120000:   Batch Loss = 2.127290, Accuracy = 0.9066667556762695
PERFORMANCE ON TEST SET: Batch Loss = 2.3625404834747314, Accuracy = 0.8116728663444519
Training iter #150000:   Batch Loss = 1.929805, Accuracy = 0.9380000233650208
PERFORMANCE ON TEST SET: Batch Loss = 2.306251049041748, Accuracy = 0.8276212215423584
Training iter #180000:   Batch Loss = 1.971904, Accuracy = 0.9153333902359009
PERFORMANCE ON TEST SET: Batch Loss = 2.0835530757904053, Accuracy = 0.8771631121635437
Training iter #210000:   Batch Loss = 1.860249, Accuracy = 0.8613333702087402
PERFORMANCE ON TEST SET: Batch Loss = 1.9994492530822754, Accuracy = 0.8788597583770752
Training iter #240000:   Batch Loss = 1.626292, Accuracy = 0.9380000233650208
PERFORMANCE ON TEST SET: Batch Loss = 1.879166603088379, Accuracy = 0.8944689035415649
Training iter #270000:   Batch Loss = 1.582758, Accuracy = 0.9386667013168335
PERFORMANCE ON TEST SET: Batch Loss = 2.0341007709503174, Accuracy = 0.8361043930053711
Training iter #300000:   Batch Loss = 1.620352, Accuracy = 0.9306666851043701
PERFORMANCE ON TEST SET: Batch Loss = 1.8185184001922607, Accuracy = 0.8639293313026428
Training iter #330000:   Batch Loss = 1.474394, Accuracy = 0.9693333506584167
PERFORMANCE ON TEST SET: Batch Loss = 1.7638503313064575, Accuracy = 0.8747878670692444
Training iter #360000:   Batch Loss = 1.406998, Accuracy = 0.9420000314712524
PERFORMANCE ON TEST SET: Batch Loss = 1.5946787595748901, Accuracy = 0.902273416519165
Training iter #390000:   Batch Loss = 1.362515, Accuracy = 0.940000057220459
PERFORMANCE ON TEST SET: Batch Loss = 1.5285792350769043, Accuracy = 0.9046487212181091
Training iter #420000:   Batch Loss = 1.252860, Accuracy = 0.9566667079925537
PERFORMANCE ON TEST SET: Batch Loss = 1.4635565280914307, Accuracy = 0.9107565879821777
Training iter #450000:   Batch Loss = 1.190078, Accuracy = 0.9553333520889282
...
PERFORMANCE ON TEST SET: Batch Loss = 0.42567864060401917, Accuracy = 0.9324736595153809
Training iter #2070000:   Batch Loss = 0.342763, Accuracy = 0.9326667189598083
PERFORMANCE ON TEST SET: Batch Loss = 0.4292983412742615, Accuracy = 0.9273836612701416
Training iter #2100000:   Batch Loss = 0.259442, Accuracy = 0.9873334169387817
PERFORMANCE ON TEST SET: Batch Loss = 0.44131210446357727, Accuracy = 0.9273836612701416
Training iter #2130000:   Batch Loss = 0.284630, Accuracy = 0.9593333601951599
PERFORMANCE ON TEST SET: Batch Loss = 0.46982717514038086, Accuracy = 0.9093992710113525
Training iter #2160000:   Batch Loss = 0.299012, Accuracy = 0.9686667323112488
PERFORMANCE ON TEST SET: Batch Loss = 0.48389002680778503, Accuracy = 0.9138105511665344
Training iter #2190000:   Batch Loss = 0.287106, Accuracy = 0.9700000286102295
PERFORMANCE ON TEST SET: Batch Loss = 0.4670214056968689, Accuracy = 0.9216151237487793
Optimization Finished!
FINAL RESULT: Batch Loss = 0.45611169934272766, Accuracy = 0.9165252447128296

Training is good, but having visual insight is even better:

Okay, let's plot this simply in the notebook for now.

# (Inline plots: )
%matplotlib inline

font = {
    'family' : 'Bitstream Vera Sans',
    'weight' : 'bold',
    'size'   : 18
}
matplotlib.rc('font', **font)

width = 12
height = 12
plt.figure(figsize=(width, height))

indep_train_axis = np.array(range(batch_size, (len(train_losses)+1)*batch_size, batch_size))
plt.plot(indep_train_axis, np.array(train_losses),     "b--", label="Train losses")
plt.plot(indep_train_axis, np.array(train_accuracies), "g--", label="Train accuracies")

indep_test_axis = np.append(
    np.array(range(batch_size, len(test_losses)*display_iter, display_iter)[:-1]),
    [training_iters]
)
plt.plot(indep_test_axis, np.array(test_losses),     "b-", label="Test losses")
plt.plot(indep_test_axis, np.array(test_accuracies), "g-", label="Test accuracies")

plt.title("Training session's progress over iterations")
plt.legend(loc='upper right', shadow=True)
plt.ylabel('Training Progress (Loss or Accuracy values)')
plt.xlabel('Training iteration')

plt.show()

LSTM Training Testing Comparison Curve

And finally, the multi-class confusion matrix and metrics!

# Results

predictions = one_hot_predictions.argmax(1)

print("Testing Accuracy: {}%".format(100*accuracy))

print("")
print("Precision: {}%".format(100*metrics.precision_score(y_test, predictions, average="weighted")))
print("Recall: {}%".format(100*metrics.recall_score(y_test, predictions, average="weighted")))
print("f1_score: {}%".format(100*metrics.f1_score(y_test, predictions, average="weighted")))

print("")
print("Confusion Matrix:")
confusion_matrix = metrics.confusion_matrix(y_test, predictions)
print(confusion_matrix)
normalised_confusion_matrix = np.array(confusion_matrix, dtype=np.float32)/np.sum(confusion_matrix)*100

print("")
print("Confusion matrix (normalised to % of total test data):")
print(normalised_confusion_matrix)
print("Note: training and testing data is not equally distributed amongst classes, ")
print("so it is normal that more than a 6th of the data is correctly classifier in the last category.")

# Plot Results:
width = 12
height = 12
plt.figure(figsize=(width, height))
plt.imshow(
    normalised_confusion_matrix,
    interpolation='nearest',
    cmap=plt.cm.rainbow
)
plt.title("Confusion matrix \n(normalised to % of total test data)")
plt.colorbar()
tick_marks = np.arange(n_classes)
plt.xticks(tick_marks, LABELS, rotation=90)
plt.yticks(tick_marks, LABELS)
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()
Testing Accuracy: 91.65252447128296%

Precision: 91.76286479743305%
Recall: 91.65252799457076%
f1_score: 91.6437546304815%

Confusion Matrix:
[[466   2  26   0   2   0]
 [  5 441  25   0   0   0]
 [  1   0 419   0   0   0]
 [  1   1   0 396  87   6]
 [  2   1   0  87 442   0]
 [  0   0   0   0   0 537]]

Confusion matrix (normalised to % of total test data):
[[ 15.81269073   0.06786563   0.88225317   0.           0.06786563   0.        ]
 [  0.16966406  14.96437073   0.84832031   0.           0.           0.        ]
 [  0.03393281   0.          14.21784878   0.           0.           0.        ]
 [  0.03393281   0.03393281   0.          13.43739319   2.95215464
    0.20359688]
 [  0.06786563   0.03393281   0.           2.95215464  14.99830341   0.        ]
 [  0.           0.           0.           0.           0.          18.22192001]]
Note: training and testing data is not equally distributed amongst classes,
so it is normal that more than a 6th of the data is correctly classifier in the last category.

Confusion Matrix

sess.close()

Conclusion

Outstandingly, the final accuracy is of 91%! And it can peak to values such as 93.25%, at some moments of luck during the training, depending on how the neural network's weights got initialized at the start of the training, randomly.

This means that the neural networks is almost always able to correctly identify the movement type! Remember, the phone is attached on the waist and each series to classify has just a 128 sample window of two internal sensors (a.k.a. 2.56 seconds at 50 FPS), so it amazes me how those predictions are extremely accurate given this small window of context and raw data. I've validated and re-validated that there is no important bug, and the community used and tried this code a lot. (Note: be sure to report something in the issue tab if you find bugs, otherwise Quora, StackOverflow, and other StackExchange sites are the places for asking questions.)

I specially did not expect such good results for guessing between the labels "SITTING" and "STANDING". Those are seemingly almost the same thing from the point of view of a device placed at waist level according to how the dataset was originally gathered. Thought, it is still possible to see a little cluster on the matrix between those classes, which drifts away just a bit from the identity. This is great.

It is also possible to see that there was a slight difficulty in doing the difference between "WALKING", "WALKING_UPSTAIRS" and "WALKING_DOWNSTAIRS". Obviously, those activities are quite similar in terms of movements.

I also tried my code without the gyroscope, using only the 3D accelerometer's 6 features (and not changing the training hyperparameters), and got an accuracy of 87%. In general, gyroscopes consumes more power than accelerometers, so it is preferable to turn them off.

Improvements

In another open-source repository of mine, the accuracy is pushed up to nearly 94% using a special deep LSTM architecture which combines the concepts of bidirectional RNNs, residual connections, and stacked cells. This architecture is also tested on another similar activity dataset. It resembles the nice architecture used in "Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation", without an attention mechanism, and with just the encoder part - as a "many to one" architecture instead of a "many to many" to be adapted to the Human Activity Recognition (HAR) problem. I also worked more on the problem and came up with the LARNN, however it's complicated for just a little gain. Thus the current, original activity recognition project is simply better to use for its simplicity. We've also coded a non-deep learning machine learning pipeline on the same datasets using classical featurization techniques and older machine learning algorithms.

If you want to learn more about deep learning, I have also built a list of the learning ressources for deep learning which have revealed to be the most useful to me here. You may also be interested in my online course on Deep Learning and Recurrent Neural Networks (DL&RNN).

I also have made even more improvements as seen just below with the few lines of code for easier usage and for reaching an even better score. Note this this is still an ongoing project, subscribe here to learn more.

More time series processing

Visit Neuraxio's Time Series Solution product page for more information.

References

The dataset can be found on the UCI Machine Learning Repository:

Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.

Citation

Copyright (c) 2016 Guillaume Chevalier. To cite my code, you can point to the URL of the GitHub repository, for example:

Guillaume Chevalier, LSTMs for Human Activity Recognition, 2016, https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition

My code is available for free and even for private usage for anyone under the MIT License, however I ask to cite for using the code.

Here is the BibTeX citation code:

@misc{chevalier2016lstms,
  title={LSTMs for human activity recognition},
  author={Chevalier, Guillaume},
  year={2016}
}

I've also published a second paper, with contributors, regarding a second iteration as an improvement of this work, with deeper neural networks. The paper is available on arXiv. Here is the BibTeX citation code for this newer piece of work based on this project:

@article{DBLP:journals/corr/abs-1708-08989,
  author    = {Yu Zhao and
               Rennong Yang and
               Guillaume Chevalier and
               Maoguo Gong},
  title     = {Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable
               Sensors},
  journal   = {CoRR},
  volume    = {abs/1708.08989},
  year      = {2017},
  url       = {http://arxiv.org/abs/1708.08989},
  archivePrefix = {arXiv},
  eprint    = {1708.08989},
  timestamp = {Mon, 13 Aug 2018 16:46:48 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1708-08989},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Extra links

Connect with me

Liked this project? Did it help you? Leave a star, fork and share the love!

This activity recognition project has been seen in:

Collaborate with us on similar projects!

Join our slack workspace for time series processing, where you can:

  • Collaborate with like-minded researchers in the #research channel;
  • Do business with us and other companies for services and products related to time series processing, in the #business channel;
  • Talk about how to do Clean Machine Learning using Neuraxle, in the #neuraxle channel;

Online Course: Learn Deep Learning and Recurrent Neural Networks (DL&RNN)

I have created a course on Deep Learning and Recurrent Neural Networks (DL&RNN). Watch a preview of the Deep Learning and Recurrent Neural Networks (DL&RNN) course here. It is the most richly dense and accelerated course out there on this precise topic to make you understand RNNs and other advanced neural networks techniques quickly.


# Let's convert this notebook to a README automatically for the GitHub project's title page:
!jupyter nbconvert --to markdown LSTM.ipynb
!mv LSTM.md README.md
[NbConvertApp] Converting notebook LSTM.ipynb to markdown
[NbConvertApp] Support files will be in LSTM_files/
[NbConvertApp] Making directory LSTM_files
[NbConvertApp] Making directory LSTM_files
[NbConvertApp] Writing 38654 bytes to LSTM.md

Author: guillaume-chevalier
Source code:  https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition
License: MIT license

#tensorflow #jupyter 

What is GEEK

Buddha Community

Human Activity Recognition Example using TensorFlow with LSTM
Chloe  Butler

Chloe Butler

1667425440

Pdf2gerb: Perl Script Converts PDF Files to Gerber format

pdf2gerb

Perl script converts PDF files to Gerber format

Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.

The general workflow is as follows:

  1. Design the PCB using your favorite CAD or drawing software.
  2. Print the top and bottom copper and top silk screen layers to a PDF file.
  3. Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
  4. Use a Gerber viewer to double-check the output against the original PCB design.
  5. Make adjustments as needed.
  6. Submit the files to a PCB manufacturer.

Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).

See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.


pdf2gerb_cfg.pm

#Pdf2Gerb config settings:
#Put this file in same folder/directory as pdf2gerb.pl itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;

use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)


##############################################################################################
#configurable settings:
#change values here instead of in main pfg2gerb.pl file

use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call

#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)}Pdf2Gerb.pl ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software.  \nGerber files MAY CONTAIN ERRORS.  Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG

use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC

use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)

#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1); 

#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
(
#round or square pads (> 0) and drills (< 0):
    .010, -.001,  #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
    .031, -.014,  #used for vias
    .041, -.020,  #smallest non-filled plated hole
    .051, -.025,
    .056, -.029,  #useful for IC pins
    .070, -.033,
    .075, -.040,  #heavier leads
#    .090, -.043,  #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
    .100, -.046,
    .115, -.052,
    .130, -.061,
    .140, -.067,
    .150, -.079,
    .175, -.088,
    .190, -.093,
    .200, -.100,
    .220, -.110,
    .160, -.125,  #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
    .090, -.040,  #want a .090 pad option, but use dummy hole size
    .065, -.040, #.065 x .065 rect pad
    .035, -.040, #.035 x .065 rect pad
#traces:
    .001,  #too thin for real traces; use only for board outlines
    .006,  #minimum real trace width; mainly used for text
    .008,  #mainly used for mid-sized text, not traces
    .010,  #minimum recommended trace width for low-current signals
    .012,
    .015,  #moderate low-voltage current
    .020,  #heavier trace for power, ground (even if a lighter one is adequate)
    .025,
    .030,  #heavy-current traces; be careful with these ones!
    .040,
    .050,
    .060,
    .080,
    .100,
    .120,
);
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);

#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size:   parsed PDF diameter:      error:
#  .014                .016                +.002
#  .020                .02267              +.00267
#  .025                .026                +.001
#  .029                .03167              +.00267
#  .033                .036                +.003
#  .040                .04267              +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
{
    HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
    RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
    SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
    RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
    TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
    REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects
};

#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
{
    CIRCLE_ADJUST_MINX => 0,
    CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
    CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
    CIRCLE_ADJUST_MAXY => 0,
    SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
    WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
    RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found
};

#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches

#line join/cap styles:
use constant
{
    CAP_NONE => 0, #butt (none); line is exact length
    CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
    CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
    CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
};
    
#number of elements in each shape type:
use constant
{
    RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
    LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
    CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
    CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
};
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
our %SHAPELEN =
(
    rect => RECT_SHAPELEN,
    line => LINE_SHAPELEN,
    curve => CURVE_SHAPELEN,
    circle => CIRCLE_SHAPELEN,
);

#panelization:
#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions

# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?

#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes. 
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes

#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches

# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)

# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time

# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const

use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool

my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time

print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load


#############################################################################################
#junk/experiment:

#use Package::Constants;
#use Exporter qw(import); #https://perldoc.perl.org/Exporter.html

#my $caller = "pdf2gerb::";

#sub cfg
#{
#    my $proto = shift;
#    my $class = ref($proto) || $proto;
#    my $settings =
#    {
#        $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
#    };
#    bless($settings, $class);
#    return $settings;
#}

#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;

#print STDERR "read cfg file\n";

#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #https://www.perlmonks.org/?node_id=1072691; NOTE: "_OK" skips short/common names

#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }

Download Details:

Author: swannman
Source Code: https://github.com/swannman/pdf2gerb

License: GPL-3.0 license

#perl 

Emilie  Okumu

Emilie Okumu

1658418240

Human Activity Recognition Example using TensorFlow with LSTM

LSTMs for Human Activity Recognition

Human Activity Recognition (HAR) using smartphones dataset and an LSTM RNN. Classifying the type of movement amongst six categories:

  • WALKING,
  • WALKING_UPSTAIRS,
  • WALKING_DOWNSTAIRS,
  • SITTING,
  • STANDING,
  • LAYING.

Compared to a classical approach, using a Recurrent Neural Networks (RNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering. Data can be fed directly into the neural network who acts like a black box, modeling the problem correctly. Other research on the activity recognition dataset can use a big amount of feature engineering, which is rather a signal processing approach combined with classical data science techniques. The approach here is rather very simple in terms of how much was the data preprocessed.

Let's use Google's neat Deep Learning library, TensorFlow, demonstrating the usage of an LSTM, a type of Artificial Neural Network that can process sequential data / time series.

Video dataset overview

Follow this link to see a video of the 6 activities recorded in the experiment with one of the participants:

Video of the experiment

[Watch video]

 

Details about the input data

I will be using an LSTM on the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this:

The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.

That said, I will use the almost raw data: only the gravity effect has been filtered out of the accelerometer as a preprocessing step for another 3D feature as an input to help learning. If you'd ever want to extract the gravity by yourself, you could fork my code on using a Butterworth Low-Pass Filter (LPF) in Python and edit it to have the right cutoff frequency of 0.3 Hz which is a good frequency for activity recognition from body sensors.

What is an RNN?

As explained in this article, an RNN takes many input vectors to process them and output other vectors. It can be roughly pictured like in the image below, imagining each rectangle has a vectorial depth and other special hidden quirks in the image below. In our case, the "many to one" architecture is used: we accept time series of feature vectors (one vector per time step) to convert them to a probability vector at the output for classification. Note that a "one to one" architecture would be a standard feedforward neural network.

RNN Architectures Learn more on RNNs

What is an LSTM?

An LSTM is an improved RNN. It is more complex, but easier to train, avoiding what is called the vanishing gradient problem. I recommend this course for you to learn more on LSTMs.

Learn more on LSTMs

Results

Scroll on! Nice visuals awaits.

# All Includes

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import tensorflow as tf  # Version 1.0.0 (some previous versions are used in past commits)
from sklearn import metrics

import os
# Useful Constants

# Those are separate normalised input features for the neural network
INPUT_SIGNAL_TYPES = [
    "body_acc_x_",
    "body_acc_y_",
    "body_acc_z_",
    "body_gyro_x_",
    "body_gyro_y_",
    "body_gyro_z_",
    "total_acc_x_",
    "total_acc_y_",
    "total_acc_z_"
]

# Output classes to learn how to classify
LABELS = [
    "WALKING",
    "WALKING_UPSTAIRS",
    "WALKING_DOWNSTAIRS",
    "SITTING",
    "STANDING",
    "LAYING"
]

Let's start by downloading the data:

# Note: Linux bash commands start with a "!" inside those "ipython notebook" cells

DATA_PATH = "data/"

!pwd && ls
os.chdir(DATA_PATH)
!pwd && ls

!python download_dataset.py

!pwd && ls
os.chdir("..")
!pwd && ls

DATASET_PATH = DATA_PATH + "UCI HAR Dataset/"
print("\n" + "Dataset is now located at: " + DATASET_PATH)
/home/ubuntu/pynb/LSTM-Human-Activity-Recognition
data     LSTM_files  LSTM_OLD.ipynb  README.md
LICENSE  LSTM.ipynb  lstm.py         screenlog.0
/home/ubuntu/pynb/LSTM-Human-Activity-Recognition/data
download_dataset.py  source.txt

Downloading...
--2017-05-24 01:49:53--  https://archive.ics.uci.edu/ml/machine-learning-databases/00240/UCI%20HAR%20Dataset.zip
Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.249
Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.249|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 60999314 (58M) [application/zip]
Saving to: ‘UCI HAR Dataset.zip’

100%[======================================>] 60,999,314  1.69MB/s   in 38s    

2017-05-24 01:50:31 (1.55 MB/s) - ‘UCI HAR Dataset.zip’ saved [60999314/60999314]

Downloading done.

Extracting...
Extracting successfully done to /home/ubuntu/pynb/LSTM-Human-Activity-Recognition/data/UCI HAR Dataset.
/home/ubuntu/pynb/LSTM-Human-Activity-Recognition/data
download_dataset.py  __MACOSX  source.txt  UCI HAR Dataset  UCI HAR Dataset.zip
/home/ubuntu/pynb/LSTM-Human-Activity-Recognition
data     LSTM_files  LSTM_OLD.ipynb  README.md
LICENSE  LSTM.ipynb  lstm.py         screenlog.0

Dataset is now located at: data/UCI HAR Dataset/

Preparing dataset:

TRAIN = "train/"
TEST = "test/"


# Load "X" (the neural network's training and testing inputs)

def load_X(X_signals_paths):
    X_signals = []

    for signal_type_path in X_signals_paths:
        file = open(signal_type_path, 'r')
        # Read dataset from disk, dealing with text files' syntax
        X_signals.append(
            [np.array(serie, dtype=np.float32) for serie in [
                row.replace('  ', ' ').strip().split(' ') for row in file
            ]]
        )
        file.close()

    return np.transpose(np.array(X_signals), (1, 2, 0))

X_train_signals_paths = [
    DATASET_PATH + TRAIN + "Inertial Signals/" + signal + "train.txt" for signal in INPUT_SIGNAL_TYPES
]
X_test_signals_paths = [
    DATASET_PATH + TEST + "Inertial Signals/" + signal + "test.txt" for signal in INPUT_SIGNAL_TYPES
]

X_train = load_X(X_train_signals_paths)
X_test = load_X(X_test_signals_paths)


# Load "y" (the neural network's training and testing outputs)

def load_y(y_path):
    file = open(y_path, 'r')
    # Read dataset from disk, dealing with text file's syntax
    y_ = np.array(
        [elem for elem in [
            row.replace('  ', ' ').strip().split(' ') for row in file
        ]],
        dtype=np.int32
    )
    file.close()

    # Substract 1 to each output class for friendly 0-based indexing
    return y_ - 1

y_train_path = DATASET_PATH + TRAIN + "y_train.txt"
y_test_path = DATASET_PATH + TEST + "y_test.txt"

y_train = load_y(y_train_path)
y_test = load_y(y_test_path)

Additionnal Parameters:

Here are some core parameter definitions for the training.

For example, the whole neural network's structure could be summarised by enumerating those parameters and the fact that two LSTM are used one on top of another (stacked) output-to-input as hidden layers through time steps.

# Input Data

training_data_count = len(X_train)  # 7352 training series (with 50% overlap between each serie)
test_data_count = len(X_test)  # 2947 testing series
n_steps = len(X_train[0])  # 128 timesteps per series
n_input = len(X_train[0][0])  # 9 input parameters per timestep


# LSTM Neural Network's internal structure

n_hidden = 32 # Hidden layer num of features
n_classes = 6 # Total classes (should go up, or should go down)


# Training

learning_rate = 0.0025
lambda_loss_amount = 0.0015
training_iters = training_data_count * 300  # Loop 300 times on the dataset
batch_size = 1500
display_iter = 30000  # To show test set accuracy during training


# Some debugging info

print("Some useful info to get an insight on dataset's shape and normalisation:")
print("(X shape, y shape, every X's mean, every X's standard deviation)")
print(X_test.shape, y_test.shape, np.mean(X_test), np.std(X_test))
print("The dataset is therefore properly normalised, as expected, but not yet one-hot encoded.")
Some useful info to get an insight on dataset's shape and normalisation:
(X shape, y shape, every X's mean, every X's standard deviation)
(2947, 128, 9) (2947, 1) 0.0991399 0.395671
The dataset is therefore properly normalised, as expected, but not yet one-hot encoded.

Utility functions for training:

def LSTM_RNN(_X, _weights, _biases):
    # Function returns a tensorflow LSTM (RNN) artificial neural network from given parameters.
    # Moreover, two LSTM cells are stacked which adds deepness to the neural network.
    # Note, some code of this notebook is inspired from an slightly different
    # RNN architecture used on another dataset, some of the credits goes to
    # "aymericdamien" under the MIT license.

    # (NOTE: This step could be greatly optimised by shaping the dataset once
    # input shape: (batch_size, n_steps, n_input)
    _X = tf.transpose(_X, [1, 0, 2])  # permute n_steps and batch_size
    # Reshape to prepare input to hidden activation
    _X = tf.reshape(_X, [-1, n_input])
    # new shape: (n_steps*batch_size, n_input)

    # ReLU activation, thanks to Yu Zhao for adding this improvement here:
    _X = tf.nn.relu(tf.matmul(_X, _weights['hidden']) + _biases['hidden'])
    # Split data because rnn cell needs a list of inputs for the RNN inner loop
    _X = tf.split(_X, n_steps, 0)
    # new shape: n_steps * (batch_size, n_hidden)

    # Define two stacked LSTM cells (two recurrent layers deep) with tensorflow
    lstm_cell_1 = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)
    lstm_cell_2 = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)
    lstm_cells = tf.contrib.rnn.MultiRNNCell([lstm_cell_1, lstm_cell_2], state_is_tuple=True)
    # Get LSTM cell output
    outputs, states = tf.contrib.rnn.static_rnn(lstm_cells, _X, dtype=tf.float32)

    # Get last time step's output feature for a "many-to-one" style classifier,
    # as in the image describing RNNs at the top of this page
    lstm_last_output = outputs[-1]

    # Linear activation
    return tf.matmul(lstm_last_output, _weights['out']) + _biases['out']


def extract_batch_size(_train, step, batch_size):
    # Function to fetch a "batch_size" amount of data from "(X|y)_train" data.

    shape = list(_train.shape)
    shape[0] = batch_size
    batch_s = np.empty(shape)

    for i in range(batch_size):
        # Loop index
        index = ((step-1)*batch_size + i) % len(_train)
        batch_s[i] = _train[index]

    return batch_s


def one_hot(y_, n_classes=n_classes):
    # Function to encode neural one-hot output labels from number indexes
    # e.g.:
    # one_hot(y_=[[5], [0], [3]], n_classes=6):
    #     return [[0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0]]

    y_ = y_.reshape(len(y_))
    return np.eye(n_classes)[np.array(y_, dtype=np.int32)]  # Returns FLOATS

Let's get serious and build the neural network:


# Graph input/output
x = tf.placeholder(tf.float32, [None, n_steps, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])

# Graph weights
weights = {
    'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])), # Hidden layer weights
    'out': tf.Variable(tf.random_normal([n_hidden, n_classes], mean=1.0))
}
biases = {
    'hidden': tf.Variable(tf.random_normal([n_hidden])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

pred = LSTM_RNN(x, weights, biases)

# Loss, optimizer and evaluation
l2 = lambda_loss_amount * sum(
    tf.nn.l2_loss(tf_var) for tf_var in tf.trainable_variables()
) # L2 loss prevents this overkill neural network to overfit the data
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=pred)) + l2 # Softmax loss
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer

correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

Hooray, now train the neural network:

# To keep track of training's performance
test_losses = []
test_accuracies = []
train_losses = []
train_accuracies = []

# Launch the graph
sess = tf.InteractiveSession(config=tf.ConfigProto(log_device_placement=True))
init = tf.global_variables_initializer()
sess.run(init)

# Perform Training steps with "batch_size" amount of example data at each loop
step = 1
while step * batch_size <= training_iters:
    batch_xs =         extract_batch_size(X_train, step, batch_size)
    batch_ys = one_hot(extract_batch_size(y_train, step, batch_size))

    # Fit training using batch data
    _, loss, acc = sess.run(
        [optimizer, cost, accuracy],
        feed_dict={
            x: batch_xs,
            y: batch_ys
        }
    )
    train_losses.append(loss)
    train_accuracies.append(acc)

    # Evaluate network only at some steps for faster training:
    if (step*batch_size % display_iter == 0) or (step == 1) or (step * batch_size > training_iters):

        # To not spam console, show training accuracy/loss in this "if"
        print("Training iter #" + str(step*batch_size) + \
              ":   Batch Loss = " + "{:.6f}".format(loss) + \
              ", Accuracy = {}".format(acc))

        # Evaluation on the test set (no learning made here - just evaluation for diagnosis)
        loss, acc = sess.run(
            [cost, accuracy],
            feed_dict={
                x: X_test,
                y: one_hot(y_test)
            }
        )
        test_losses.append(loss)
        test_accuracies.append(acc)
        print("PERFORMANCE ON TEST SET: " + \
              "Batch Loss = {}".format(loss) + \
              ", Accuracy = {}".format(acc))

    step += 1

print("Optimization Finished!")

# Accuracy for test data

one_hot_predictions, accuracy, final_loss = sess.run(
    [pred, accuracy, cost],
    feed_dict={
        x: X_test,
        y: one_hot(y_test)
    }
)

test_losses.append(final_loss)
test_accuracies.append(accuracy)

print("FINAL RESULT: " + \
      "Batch Loss = {}".format(final_loss) + \
      ", Accuracy = {}".format(accuracy))
WARNING:tensorflow:From <ipython-input-19-3339689e51f6>:9: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
Training iter #1500:   Batch Loss = 5.416760, Accuracy = 0.15266665816307068
PERFORMANCE ON TEST SET: Batch Loss = 4.880829811096191, Accuracy = 0.05632847175002098
Training iter #30000:   Batch Loss = 3.031930, Accuracy = 0.607333242893219
PERFORMANCE ON TEST SET: Batch Loss = 3.0515167713165283, Accuracy = 0.6067186594009399
Training iter #60000:   Batch Loss = 2.672764, Accuracy = 0.7386666536331177
PERFORMANCE ON TEST SET: Batch Loss = 2.780435085296631, Accuracy = 0.7027485370635986
Training iter #90000:   Batch Loss = 2.378301, Accuracy = 0.8366667032241821
PERFORMANCE ON TEST SET: Batch Loss = 2.6019773483276367, Accuracy = 0.7617915868759155
Training iter #120000:   Batch Loss = 2.127290, Accuracy = 0.9066667556762695
PERFORMANCE ON TEST SET: Batch Loss = 2.3625404834747314, Accuracy = 0.8116728663444519
Training iter #150000:   Batch Loss = 1.929805, Accuracy = 0.9380000233650208
PERFORMANCE ON TEST SET: Batch Loss = 2.306251049041748, Accuracy = 0.8276212215423584
Training iter #180000:   Batch Loss = 1.971904, Accuracy = 0.9153333902359009
PERFORMANCE ON TEST SET: Batch Loss = 2.0835530757904053, Accuracy = 0.8771631121635437
Training iter #210000:   Batch Loss = 1.860249, Accuracy = 0.8613333702087402
PERFORMANCE ON TEST SET: Batch Loss = 1.9994492530822754, Accuracy = 0.8788597583770752
Training iter #240000:   Batch Loss = 1.626292, Accuracy = 0.9380000233650208
PERFORMANCE ON TEST SET: Batch Loss = 1.879166603088379, Accuracy = 0.8944689035415649
Training iter #270000:   Batch Loss = 1.582758, Accuracy = 0.9386667013168335
PERFORMANCE ON TEST SET: Batch Loss = 2.0341007709503174, Accuracy = 0.8361043930053711
Training iter #300000:   Batch Loss = 1.620352, Accuracy = 0.9306666851043701
PERFORMANCE ON TEST SET: Batch Loss = 1.8185184001922607, Accuracy = 0.8639293313026428
Training iter #330000:   Batch Loss = 1.474394, Accuracy = 0.9693333506584167
PERFORMANCE ON TEST SET: Batch Loss = 1.7638503313064575, Accuracy = 0.8747878670692444
Training iter #360000:   Batch Loss = 1.406998, Accuracy = 0.9420000314712524
PERFORMANCE ON TEST SET: Batch Loss = 1.5946787595748901, Accuracy = 0.902273416519165
Training iter #390000:   Batch Loss = 1.362515, Accuracy = 0.940000057220459
PERFORMANCE ON TEST SET: Batch Loss = 1.5285792350769043, Accuracy = 0.9046487212181091
Training iter #420000:   Batch Loss = 1.252860, Accuracy = 0.9566667079925537
PERFORMANCE ON TEST SET: Batch Loss = 1.4635565280914307, Accuracy = 0.9107565879821777
Training iter #450000:   Batch Loss = 1.190078, Accuracy = 0.9553333520889282
...
PERFORMANCE ON TEST SET: Batch Loss = 0.42567864060401917, Accuracy = 0.9324736595153809
Training iter #2070000:   Batch Loss = 0.342763, Accuracy = 0.9326667189598083
PERFORMANCE ON TEST SET: Batch Loss = 0.4292983412742615, Accuracy = 0.9273836612701416
Training iter #2100000:   Batch Loss = 0.259442, Accuracy = 0.9873334169387817
PERFORMANCE ON TEST SET: Batch Loss = 0.44131210446357727, Accuracy = 0.9273836612701416
Training iter #2130000:   Batch Loss = 0.284630, Accuracy = 0.9593333601951599
PERFORMANCE ON TEST SET: Batch Loss = 0.46982717514038086, Accuracy = 0.9093992710113525
Training iter #2160000:   Batch Loss = 0.299012, Accuracy = 0.9686667323112488
PERFORMANCE ON TEST SET: Batch Loss = 0.48389002680778503, Accuracy = 0.9138105511665344
Training iter #2190000:   Batch Loss = 0.287106, Accuracy = 0.9700000286102295
PERFORMANCE ON TEST SET: Batch Loss = 0.4670214056968689, Accuracy = 0.9216151237487793
Optimization Finished!
FINAL RESULT: Batch Loss = 0.45611169934272766, Accuracy = 0.9165252447128296

Training is good, but having visual insight is even better:

Okay, let's plot this simply in the notebook for now.

# (Inline plots: )
%matplotlib inline

font = {
    'family' : 'Bitstream Vera Sans',
    'weight' : 'bold',
    'size'   : 18
}
matplotlib.rc('font', **font)

width = 12
height = 12
plt.figure(figsize=(width, height))

indep_train_axis = np.array(range(batch_size, (len(train_losses)+1)*batch_size, batch_size))
plt.plot(indep_train_axis, np.array(train_losses),     "b--", label="Train losses")
plt.plot(indep_train_axis, np.array(train_accuracies), "g--", label="Train accuracies")

indep_test_axis = np.append(
    np.array(range(batch_size, len(test_losses)*display_iter, display_iter)[:-1]),
    [training_iters]
)
plt.plot(indep_test_axis, np.array(test_losses),     "b-", label="Test losses")
plt.plot(indep_test_axis, np.array(test_accuracies), "g-", label="Test accuracies")

plt.title("Training session's progress over iterations")
plt.legend(loc='upper right', shadow=True)
plt.ylabel('Training Progress (Loss or Accuracy values)')
plt.xlabel('Training iteration')

plt.show()

LSTM Training Testing Comparison Curve

And finally, the multi-class confusion matrix and metrics!

# Results

predictions = one_hot_predictions.argmax(1)

print("Testing Accuracy: {}%".format(100*accuracy))

print("")
print("Precision: {}%".format(100*metrics.precision_score(y_test, predictions, average="weighted")))
print("Recall: {}%".format(100*metrics.recall_score(y_test, predictions, average="weighted")))
print("f1_score: {}%".format(100*metrics.f1_score(y_test, predictions, average="weighted")))

print("")
print("Confusion Matrix:")
confusion_matrix = metrics.confusion_matrix(y_test, predictions)
print(confusion_matrix)
normalised_confusion_matrix = np.array(confusion_matrix, dtype=np.float32)/np.sum(confusion_matrix)*100

print("")
print("Confusion matrix (normalised to % of total test data):")
print(normalised_confusion_matrix)
print("Note: training and testing data is not equally distributed amongst classes, ")
print("so it is normal that more than a 6th of the data is correctly classifier in the last category.")

# Plot Results:
width = 12
height = 12
plt.figure(figsize=(width, height))
plt.imshow(
    normalised_confusion_matrix,
    interpolation='nearest',
    cmap=plt.cm.rainbow
)
plt.title("Confusion matrix \n(normalised to % of total test data)")
plt.colorbar()
tick_marks = np.arange(n_classes)
plt.xticks(tick_marks, LABELS, rotation=90)
plt.yticks(tick_marks, LABELS)
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()
Testing Accuracy: 91.65252447128296%

Precision: 91.76286479743305%
Recall: 91.65252799457076%
f1_score: 91.6437546304815%

Confusion Matrix:
[[466   2  26   0   2   0]
 [  5 441  25   0   0   0]
 [  1   0 419   0   0   0]
 [  1   1   0 396  87   6]
 [  2   1   0  87 442   0]
 [  0   0   0   0   0 537]]

Confusion matrix (normalised to % of total test data):
[[ 15.81269073   0.06786563   0.88225317   0.           0.06786563   0.        ]
 [  0.16966406  14.96437073   0.84832031   0.           0.           0.        ]
 [  0.03393281   0.          14.21784878   0.           0.           0.        ]
 [  0.03393281   0.03393281   0.          13.43739319   2.95215464
    0.20359688]
 [  0.06786563   0.03393281   0.           2.95215464  14.99830341   0.        ]
 [  0.           0.           0.           0.           0.          18.22192001]]
Note: training and testing data is not equally distributed amongst classes,
so it is normal that more than a 6th of the data is correctly classifier in the last category.

Confusion Matrix

sess.close()

Conclusion

Outstandingly, the final accuracy is of 91%! And it can peak to values such as 93.25%, at some moments of luck during the training, depending on how the neural network's weights got initialized at the start of the training, randomly.

This means that the neural networks is almost always able to correctly identify the movement type! Remember, the phone is attached on the waist and each series to classify has just a 128 sample window of two internal sensors (a.k.a. 2.56 seconds at 50 FPS), so it amazes me how those predictions are extremely accurate given this small window of context and raw data. I've validated and re-validated that there is no important bug, and the community used and tried this code a lot. (Note: be sure to report something in the issue tab if you find bugs, otherwise Quora, StackOverflow, and other StackExchange sites are the places for asking questions.)

I specially did not expect such good results for guessing between the labels "SITTING" and "STANDING". Those are seemingly almost the same thing from the point of view of a device placed at waist level according to how the dataset was originally gathered. Thought, it is still possible to see a little cluster on the matrix between those classes, which drifts away just a bit from the identity. This is great.

It is also possible to see that there was a slight difficulty in doing the difference between "WALKING", "WALKING_UPSTAIRS" and "WALKING_DOWNSTAIRS". Obviously, those activities are quite similar in terms of movements.

I also tried my code without the gyroscope, using only the 3D accelerometer's 6 features (and not changing the training hyperparameters), and got an accuracy of 87%. In general, gyroscopes consumes more power than accelerometers, so it is preferable to turn them off.

Improvements

In another open-source repository of mine, the accuracy is pushed up to nearly 94% using a special deep LSTM architecture which combines the concepts of bidirectional RNNs, residual connections, and stacked cells. This architecture is also tested on another similar activity dataset. It resembles the nice architecture used in "Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation", without an attention mechanism, and with just the encoder part - as a "many to one" architecture instead of a "many to many" to be adapted to the Human Activity Recognition (HAR) problem. I also worked more on the problem and came up with the LARNN, however it's complicated for just a little gain. Thus the current, original activity recognition project is simply better to use for its simplicity. We've also coded a non-deep learning machine learning pipeline on the same datasets using classical featurization techniques and older machine learning algorithms.

If you want to learn more about deep learning, I have also built a list of the learning ressources for deep learning which have revealed to be the most useful to me here. You may also be interested in my online course on Deep Learning and Recurrent Neural Networks (DL&RNN).

I also have made even more improvements as seen just below with the few lines of code for easier usage and for reaching an even better score. Note this this is still an ongoing project, subscribe here to learn more.

More time series processing

Visit Neuraxio's Time Series Solution product page for more information.

References

The dataset can be found on the UCI Machine Learning Repository:

Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.

Citation

Copyright (c) 2016 Guillaume Chevalier. To cite my code, you can point to the URL of the GitHub repository, for example:

Guillaume Chevalier, LSTMs for Human Activity Recognition, 2016, https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition

My code is available for free and even for private usage for anyone under the MIT License, however I ask to cite for using the code.

Here is the BibTeX citation code:

@misc{chevalier2016lstms,
  title={LSTMs for human activity recognition},
  author={Chevalier, Guillaume},
  year={2016}
}

I've also published a second paper, with contributors, regarding a second iteration as an improvement of this work, with deeper neural networks. The paper is available on arXiv. Here is the BibTeX citation code for this newer piece of work based on this project:

@article{DBLP:journals/corr/abs-1708-08989,
  author    = {Yu Zhao and
               Rennong Yang and
               Guillaume Chevalier and
               Maoguo Gong},
  title     = {Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable
               Sensors},
  journal   = {CoRR},
  volume    = {abs/1708.08989},
  year      = {2017},
  url       = {http://arxiv.org/abs/1708.08989},
  archivePrefix = {arXiv},
  eprint    = {1708.08989},
  timestamp = {Mon, 13 Aug 2018 16:46:48 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1708-08989},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Extra links

Connect with me

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  • Collaborate with like-minded researchers in the #research channel;
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Online Course: Learn Deep Learning and Recurrent Neural Networks (DL&RNN)

I have created a course on Deep Learning and Recurrent Neural Networks (DL&RNN). Watch a preview of the Deep Learning and Recurrent Neural Networks (DL&RNN) course here. It is the most richly dense and accelerated course out there on this precise topic to make you understand RNNs and other advanced neural networks techniques quickly.


# Let's convert this notebook to a README automatically for the GitHub project's title page:
!jupyter nbconvert --to markdown LSTM.ipynb
!mv LSTM.md README.md
[NbConvertApp] Converting notebook LSTM.ipynb to markdown
[NbConvertApp] Support files will be in LSTM_files/
[NbConvertApp] Making directory LSTM_files
[NbConvertApp] Making directory LSTM_files
[NbConvertApp] Writing 38654 bytes to LSTM.md

Author: guillaume-chevalier
Source code:  https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition
License: MIT license

#tensorflow #jupyter 

Why Use WordPress? What Can You Do With WordPress?

Can you use WordPress for anything other than blogging? To your surprise, yes. WordPress is more than just a blogging tool, and it has helped thousands of websites and web applications to thrive. The use of WordPress powers around 40% of online projects, and today in our blog, we would visit some amazing uses of WordPress other than blogging.
What Is The Use Of WordPress?

WordPress is the most popular website platform in the world. It is the first choice of businesses that want to set a feature-rich and dynamic Content Management System. So, if you ask what WordPress is used for, the answer is – everything. It is a super-flexible, feature-rich and secure platform that offers everything to build unique websites and applications. Let’s start knowing them:

1. Multiple Websites Under A Single Installation
WordPress Multisite allows you to develop multiple sites from a single WordPress installation. You can download WordPress and start building websites you want to launch under a single server. Literally speaking, you can handle hundreds of sites from one single dashboard, which now needs applause.
It is a highly efficient platform that allows you to easily run several websites under the same login credentials. One of the best things about WordPress is the themes it has to offer. You can simply download them and plugin for various sites and save space on sites without losing their speed.

2. WordPress Social Network
WordPress can be used for high-end projects such as Social Media Network. If you don’t have the money and patience to hire a coder and invest months in building a feature-rich social media site, go for WordPress. It is one of the most amazing uses of WordPress. Its stunning CMS is unbeatable. And you can build sites as good as Facebook or Reddit etc. It can just make the process a lot easier.
To set up a social media network, you would have to download a WordPress Plugin called BuddyPress. It would allow you to connect a community page with ease and would provide all the necessary features of a community or social media. It has direct messaging, activity stream, user groups, extended profiles, and so much more. You just have to download and configure it.
If BuddyPress doesn’t meet all your needs, don’t give up on your dreams. You can try out WP Symposium or PeepSo. There are also several themes you can use to build a social network.

3. Create A Forum For Your Brand’s Community
Communities are very important for your business. They help you stay in constant connection with your users and consumers. And allow you to turn them into a loyal customer base. Meanwhile, there are many good technologies that can be used for building a community page – the good old WordPress is still the best.
It is the best community development technology. If you want to build your online community, you need to consider all the amazing features you get with WordPress. Plugins such as BB Press is an open-source, template-driven PHP/ MySQL forum software. It is very simple and doesn’t hamper the experience of the website.
Other tools such as wpFoRo and Asgaros Forum are equally good for creating a community blog. They are lightweight tools that are easy to manage and integrate with your WordPress site easily. However, there is only one tiny problem; you need to have some technical knowledge to build a WordPress Community blog page.

4. Shortcodes
Since we gave you a problem in the previous section, we would also give you a perfect solution for it. You might not know to code, but you have shortcodes. Shortcodes help you execute functions without having to code. It is an easy way to build an amazing website, add new features, customize plugins easily. They are short lines of code, and rather than memorizing multiple lines; you can have zero technical knowledge and start building a feature-rich website or application.
There are also plugins like Shortcoder, Shortcodes Ultimate, and the Basics available on WordPress that can be used, and you would not even have to remember the shortcodes.

5. Build Online Stores
If you still think about why to use WordPress, use it to build an online store. You can start selling your goods online and start selling. It is an affordable technology that helps you build a feature-rich eCommerce store with WordPress.
WooCommerce is an extension of WordPress and is one of the most used eCommerce solutions. WooCommerce holds a 28% share of the global market and is one of the best ways to set up an online store. It allows you to build user-friendly and professional online stores and has thousands of free and paid extensions. Moreover as an open-source platform, and you don’t have to pay for the license.
Apart from WooCommerce, there are Easy Digital Downloads, iThemes Exchange, Shopify eCommerce plugin, and so much more available.

6. Security Features
WordPress takes security very seriously. It offers tons of external solutions that help you in safeguarding your WordPress site. While there is no way to ensure 100% security, it provides regular updates with security patches and provides several plugins to help with backups, two-factor authorization, and more.
By choosing hosting providers like WP Engine, you can improve the security of the website. It helps in threat detection, manage patching and updates, and internal security audits for the customers, and so much more.

Read More

#use of wordpress #use wordpress for business website #use wordpress for website #what is use of wordpress #why use wordpress #why use wordpress to build a website

A Demo Code Of Training and Testing using Tensorflow

ProbFace, arxiv

This is a demo code of training and testing [ProbFace] using Tensorflow. ProbFace is a reliable Probabilistic Face Embeddging (PFE) method. The representation of each face will be an Guassian distribution parametrized by (mu, sigma), where mu is the original embedding and sigma is the learned uncertainty. Experiments show that ProbFace could

  • improve the robustness of PFE.
  • simplify the calculation of the multal likelihood score (MLS).
  • improve the recognition performance on the risk-controlled scenarios.

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How To Activate Cash App Card - Online Activation

Cash App is a digital payment that permits users to send, receive or request cash online. Of course, you may first ought to install the Cash app on your mobile device, using Google Play or maybe the iTunes Store. Considering that the Cash App needs the use of a bank account, you may need to be over 18 years old to register. Cash App offers customers a free debit card, which matches like some other VISA card. If you’ve got a Cash App card, you don’t want to apply the mobile application, again and again, now swipe your card at pay at retail shops everywhere in the USA.

When you receive your cash app debit card, it’d be a defaulted card and you’ll now no longer be capable of using it immediately. As a result, you need to comply with a few steps to How To Activate Cash App Card. However, to make use of this card, it’s far obligatory to complete the Cash App card activation method. The card activation technique isn’t always very complicated. It may be executed with the assist of some easy steps. There are too many simple methods to activate a Cash App card—the cardboard activation with QR code and without it. Users also can do it via means of calling the Cash App phone number.

In case you are still facing cash app card activation issues, then you definitely have to touch the Cash App customer support. To activate Cash App card, you want to take those steps:

  • Open the Cash App on your device.
  • Click at the Cash card icon.
  • Then pick out the choice of activating a Cash App card.
  • Use your mobile camera for scanning the QR code.
  • Once you do it efficiently your cash card is prepared to apply

Also Read: Cash App Login || Cash App Customer Service || Cash App Direct Deposit

What are the limitations of a Cash Card?

The cash app has withdrawal limits much like all different ATM cards. Customers have the freedom to withdraw up to $250 in step with transaction and $1250 withinside the time-frame of 30 days. Besides this, you are not allowed to withdraw over $1,000 in 24 hours and $1,000 in a week. Cash App Direct Deposit, if you get in trouble at the same time as using a Cash card and want help associated with the same, you could always communicate to the executives who’re usually there to guide you. Feel free to touch the team whenever via the Cash App Phone Number that is always practical to guide you about How to activate cash app card. Whenever you’re in hassle, you could always method them to fix all of your problems withinside the nick of time.inheritor username.

Cash App introduced a new Cash App card for its users and if you want to activate it, a person desires to follow some steps on their devices both Mac or android. The great perk of activating a Cash app card is you don’t need to preserve cash with you. All features are just like popular bank accounts, such as debit cards, access to transactions, and many different features. The most crucial part about a Cash App is that you want to pay high-priced fees as service charges; the expenses charged through them are so low and inexpensive. Activate Cash App card and you could withdraw your desired amount directly out of your Cash App account through going to a standard ATM.

If you’re new to Cash App and want guidance on how to use it, you could usually talk to the team members through contacting them on the Cash App Toll Free Number that is active round the clock. Connect with the team every time to talk about your issue and clear up all queries in no time. The team tells each and each answer in detail in order that the user does now no longer face any error at the same time as executing them. Talk to the team and let them recognize your errors.

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