Recurrent Neural Networks (LSTM / RNN) Implementation with Keras – Python




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#RNN #LSTM #RecurrentNeuralNetworks #Keras #Python #DeepLearning

In this tutorial, we implement Recurrent Neural Networks with LSTM as example with keras and Tensorflow backend. The same procedure can be followed for a Simple RNN.

We implement Multi layer RNN, visualize the convergence and results. We then implement for variable sized inputs.

Recurrent Neural Networks RNN / LSTM / GRU are a very popular type of Neural Networks which captures features from time series or sequential data. It has amazing results with text and even Image Captioning.

In this example we try to predict the next digit given a sequence of digits. Same concept can be extended to text images and even music.

Find the codes here
GitHub : https://github.com/shreyans29/thesemicolon
Good Reads : http://karpathy.github.io/

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Comment List

  • The Semicolon
    January 13, 2021

    I want to classify anomaly detection using RNN keras.tf but I have a problem where the accuracy value increases but the val_accuracy value does not change and just remains constant at 50%. this is my complete code available on google colab https://colab.research.google.com/drive/1saoNuCxj08JCxZ_7taIjhp8sJEIV-T5U?usp=sharing //

  • The Semicolon
    January 13, 2021

    That small part in the end ,how to train for different lengths of input sequence,that small part that is happiness. God bless bro

  • The Semicolon
    January 13, 2021

    Good Introduction. Can you explain while your loss is decreasing with epochs, Why acc is still 0? Is this not relevant. Kindly care to explain,
    Thanks.

  • The Semicolon
    January 13, 2021

    Hey, thanks for this tutorial. Do you know how can I access the last hidden output of the LSTM ?

    Thanks for your answer,

    Robin

  • The Semicolon
    January 13, 2021

    Good video.
    If you add the activation it will make the learn fast without adding a new layer.
    Example:
    model.add(LSTM(1, activation='relu', batch_input_shape=(None, 5, 1),return_sequences=False))

  • The Semicolon
    January 13, 2021

    If you train the model on variable size inputs one in a non-random fashion (i.e. similar to your example – fitting the model on sequence of length 6 followed by length 7), then would the model not overfit to the most recent sequence? Is it possible to provide LSTM model with multiple variable length inputs in one fit call?

  • The Semicolon
    January 13, 2021

    How to pass a video as input for an RNN?

  • The Semicolon
    January 13, 2021

    Thanks bro, This is exactly what I was looking for.

  • The Semicolon
    January 13, 2021

    thank you Semicolon, this helped a lot!

  • The Semicolon
    January 13, 2021

    so helpful to built my fist lstm model..thanks a lot

  • The Semicolon
    January 13, 2021

    the prediction sucks

  • The Semicolon
    January 13, 2021

    In the Theory you told that, for obtaining output y, we will pass the hidden states through a dense layer and then through a softmax.

    Then in this code, why are you, pretending that the Hidden state output is output y ??

  • The Semicolon
    January 13, 2021

    Are we not supposed to do one-hot coding for y here?

  • The Semicolon
    January 13, 2021

    Keras to me to me to me to me. Keras to me to me to me to me. Laughter at Facebook at Facebook at Facebook.

  • The Semicolon
    January 13, 2021

    Can please make a video on video classification using RNN

  • The Semicolon
    January 13, 2021

    My laptop is hanging after trying lstm rnn. Please help

  • The Semicolon
    January 13, 2021

    Thank you. Can I reply LSTM with GRU?

  • The Semicolon
    January 13, 2021

    Thanks for the tutorial. Really helpful.

  • The Semicolon
    January 13, 2021

    Hi ! I have some trouble when I try my NN with a single vector. For example, when I send [[0.40], [0.41], [0.42], [0.43], [0.44]] I should obtain 0.45 but I receive something like 0.42 or 0.47. What should I do ?

  • The Semicolon
    January 13, 2021

    Thank you so much!

  • The Semicolon
    January 13, 2021

    I got an error
    TypeError: add() got an unexpected keyword argument 'batch_input_shape'

  • The Semicolon
    January 13, 2021

    Accuracy seems like an odd choice of metric. It seems like mean squared error would be better.

  • The Semicolon
    January 13, 2021

    Thank you for such a wonderful video

  • The Semicolon
    January 13, 2021

    Very good, informative content

  • The Semicolon
    January 13, 2021

    good job :)))

    please can you tell me what's difference batch_input_shape and input_shape ??

  • The Semicolon
    January 13, 2021

    Great video. Please make more video on keras.

  • The Semicolon
    January 13, 2021

    increase the prediction % using
    model.add(Dense(1))

    keras.activations.linear(x)

  • The Semicolon
    January 13, 2021

    I am sorry but where is the last number prediction .. The routine did not predicted it – Could you please clarify this ?

  • The Semicolon
    January 13, 2021

    U r typing very very speed good to see it😍

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