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guillaume-chevalier / LSTM-Human-Activity-Recognition

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier
MIT License
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Different Performance Using the Current Version of lstm.py with TensorFlow r1.0 #8

Closed zhaowenyi94 closed 7 years ago

zhaowenyi94 commented 7 years ago

To fit the current code in to the new released TensorFlow r1.0, I made several modification on the code

In the Loading Function

#line 25:
file = open(signal_type_path, 'rb')    ===>>>     file = open(signal_type_path, 'r')

#line 40:
file = open(y_path, 'rb')     ===>>>    file = open(y_path, 'r')

In the LSTM_NETWORK() Function

#line 110:     
hidden = tf.split(0, config.n_steps, hidden)     ===>>>    hidden = tf.split(hidden, config.n_steps, 0)

#line 114    
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(config.n_hidden, forget_bias=1.0)    ===>>>    lstm_cell = tf.contrib.rnn.BasicLSTMCell(config.n_hidden, forget_bias=1.0)

#line 117 
lsmt_layers = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * 2)    ===>>>    lsmt_layers = tf.contrib.rnn.MultiRNNCell([lstm_cell] * 2)

#line 120
outputs, _ = tf.nn.rnn(lsmt_layers, hidden, dtype=tf.float32)    ===>>>    outputs, _ = tf.contrib.rnn.static_rnn(lsmt_layers, hidden, dtype=tf.float32)

In main()

#line 216         
tf.nn.softmax_cross_entropy_with_logits(pred_Y, Y)) + l2    ===>>>    tf.nn.softmax_cross_entropy_with_logits(labels=pred_Y,logits= Y)) + l2

#line 228
tf.initialize_all_variables().run()    ===>>>    tf.global_variables_initializer().run()

However, I ran the code and the performance does not seem good as it is shown in the readme file, I want to know whether the modification of the code have some mistakes. The results shown as below:

traing iter: 0, test accuracy : 0.34781134128570557, loss : 1.3058252334594727
traing iter: 1, test accuracy : 0.3338988721370697, loss : 1.5186371803283691
traing iter: 2, test accuracy : 0.287750244140625, loss : 1.7945531606674194
traing iter: 3, test accuracy : 0.2789277136325836, loss : 2.190826416015625
traing iter: 4, test accuracy : 0.36274176836013794, loss : 2.607555866241455
traing iter: 5, test accuracy : 0.3366135060787201, loss : 2.898186206817627
traing iter: 6, test accuracy : 0.235154390335083, loss : 3.007314443588257
traing iter: 7, test accuracy : 0.18154054880142212, loss : 3.0111827850341797
traing iter: 8, test accuracy : 0.18052256107330322, loss : 2.9800398349761963
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final test accuracy: 0.18052256107330322
best epoch's test accuracy: 0.36274176836013794
sitmo commented 7 years ago

Instead of reading data in binary mode (rb) you are now reading in text mode (r). On my machine the files are in binary format (I ran the download script in the data directory) and reading them in text mode would fail. I bet that's what's wrong: you read corrupt data.

btw I'm on an OSX machine.

I get the same performance ar the README:

traing iter: 598, test accuracy : 0.897862255573, loss : 0.568764030933
traing iter: 599, test accuracy : 0.899219572544, loss : 0.574430882931

final test accuracy: 0.899219572544
best epoch's test accuracy: 0.91177469492
zhaowenyi94 commented 7 years ago

I also use OSX, but I got an error if I use 'rb', did you went into this kind of error about and byte and string type?

  File "/Users/zhaowenichi/Downloads/LSTM-Human-Activity-Recognition-master/lstm.py", line 205, in <module>
    X_train = load_X(X_train_signals_paths)
  File "/Users/zhaowenichi/Downloads/LSTM-Human-Activity-Recognition-master/lstm.py", line 41, in load_X
    row.replace('  ', ' ').strip().split(' ') for row in file
  File "/Users/zhaowenichi/Downloads/LSTM-Human-Activity-Recognition-master/lstm.py", line 41, in <listcomp>
    row.replace('  ', ' ').strip().split(' ') for row in file
TypeError: a bytes-like object is required, not 'str'
sitmo commented 7 years ago

Turns out the data files are in text format and not binary so your "r" solution should work.

I'm using python 2.7,.. are you using python 3.5/3.6?

zhaowenyi94 commented 7 years ago

I use the Python 3.5 :( Do not know how to fix it On Thu, Mar 2, 2017 at 9:51 PM Thijs van den Berg notifications@github.com wrote:

Turns out the data files are in text format and not binary so your "r" solution should work.

I'm using python 2.7,.. are you using python 3.5/3.6?

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition/issues/8#issuecomment-283776161, or mute the thread https://github.com/notifications/unsubscribe-auth/AGr84vvo3rVlKJrQwlDJKyqGNX360g_Fks5rhyu6gaJpZM4MRMH9 .

sitmo commented 7 years ago

strange, I have no problem with _file = open(signal_typepath, 'r') and python 3.5.2

guillaume-chevalier commented 7 years ago

The loss should not be negative for the cross entropy function since behind that is normally the label multiplying a log function of the prediction. That is a big hint.

The bug sits where you redefined the loss: 

tf.nn.softmax_cross_entropy_with_logits(pred_Y, Y)) + l2    ===>>>    tf.nn.softmax_cross_entropy_with_logits(labels=pred_Y,logits= Y)) + l2

Indeed the API changed. The order of the arguments is wrong compared to your call in the new version. You inverted things: logits are predictions, not the other way around as in the old TensorFlow r0.11 API: https://www.tensorflow.org/versions/r0.11/api_docs/python/nn/classification#softmax_cross_entropy_with_logits

Inverting those arguments seems to be a common mistake: https://github.com/carpedm20/NTM-tensorflow/issues/17

You still might have other problems hidden somewhere, I only took a quick look. I would not have expected the loss function to yield negative numbers either, but for that I am not sure because I have not re-validated the math of inverting the arguments.

zhaowenyi94 commented 7 years ago

Yes, modified and works! Thanks!

AnSharypov commented 7 years ago

@zhaowenyi94 Hi! How did you modify?