Cnn lstm github keras

In this blog, I will present an image captioning model, which generates a realistic caption for an input image. To help understand this topic, here are examples:. These two images are random images downloaded from internet, but our model can still generate realistic caption for the image. Our model is trying to understand the objects in the scene and generate a human readable caption. Our code with a writeup are available on Github. Also, we have a short video on YouTube. To train an image captioning model, we used the Flickr30K dataset, which contains 30k images along with five captions for each image.

And we extracted features from the images and save these them as numpy arrays. Then we fed the features into captioning model and got the model trained. Given a new image, we first do a feature extraction, then we feed the features into trained model and get prediction.

Quite straightforward, right? For our baseline, we used GIST to present the images as their features which was arrays with length Then we fed these features into KNN model use ball tree in sklearn :. The training part is quite simple! Then we are going to do prediction.

And we use BallTree to find the K nearest neighbors of A' from which we get all the candidate captions. The last step is deciding which caption we are going to use. Here, we make the decision according to consensus. We use BLEU score in nltk to measure the similarity between two captions, then we choose the caption which maximize the following formula:. Then we get our prediction! Simple enough! Let's look at something more complicated.

For our final, we built our model using Keras, which is a simple wrapper for implementing the building blocks of advanced machine learning algorithms. To achieve higher performance, we also use GPU.

During training, we use VGG for feature extraction, then fed features, captions, mask record previous words and position position of current in the caption into LSTM.

This script demonstrates the use of a convolutional LSTM network.

The ground truth Y is the next word in the caption. Finally, use a dictionary to interpret the output y into words. There are two versions of VGG network, 16 layers and 19 layers.

We mainly focus on VGG16 which is the 16 layers version. VGG16 network take image with size xx3 3 channel for RGB as input, and return a array as output, indicating which class the object in the image belongs to.

Therefore, we need to first resize the image:. VGG network consists of convolutional layers, pooling layers and full-connected layers. The last three layers are full-connected layers. The last layer is a softmax layer which only tell us which category the image belongs to. However, the second last layer, fc-2 layer, contains the features of a image as a array. Therefore, we get our output from the fc-2 layer.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. In your CNN-related layers you build up your model to use for single images, not for sequences. I have the same problem as llandolfi. Your latent space vector would be the output of the LSTM layer. Based on that higher deconvolution layers build the next frame for you.

Alternatively to recurrent structures you can take a look at 3d convolutions in order to incorporate video data as input.

cnn lstm github keras

How to handle the timesteps dimension? I'll really appreciate your answers. Also how should I do the same thing for the feature maps from other images too Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. New issue. Jump to bottom. Copy link Quote reply. This comment has been minimized. Sign in to view. Hello, as I understood your code, you want to provide to your model sequences of images. I removed some CNN-layers in order to compile your model on my machine. MahdiKhodayar closed this Apr 18, Hello, thank you very much for the example, I have a problem on model. I have the same problem as llandolfi Any solution? Sign up for free to join this conversation on GitHub.

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cnn lstm github keras

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Each image is 28x28 pixels. One of the small sub-images has the dimension 14 x The four sequences are stacked together along the width shouldn't matter whether width or height. Now this should be given to a Keras model.

I searched online and found this question: Python keras how to change the size of input after convolution layer into lstm layer. Apparently the input to the Reshape layer is incorrect. As an alternative, I tried to pass the timesteps to the Reshape layer, too:.

There are rather complex approaches to this problem. Why are there so complicated at least they seem complicated to me solutions, if a simple Reshape does the trick?

Embarrassingly, I forgot that the dimensions will be changed by the pooling and for lack of padding the convolutions, too. The output of the layer before the Reshape layer is None, 32, 26, 5I changed the reshape to: model. It seems like I need to pass the timestep dimension through the entire network. How can I do that? According to Convolution2D definition your input must be 4-dimensional with dimensions samples, channels, rows, cols.

This is the direct reason why are you getting an error. To resolve that you must use TimeDistributed wrapper. This allows you to use static not recurrent layers across the time. Learn more. Keras: reshape to connect lstm and conv Ask Question.

Asked 3 years, 5 months ago. Active 3 years, 5 months ago. Viewed 7k times. This creates a vector with the shape [, 4, 1, 56, 14] where: is the number of samples 4 is the number of elements in a sequence of timesteps 1 is the depth of colors greyscale 56 and 14 are width and height Now this should be given to a Keras model.

As an alternative, I tried to pass the timesteps to the Reshape layer, too: model. Am I doing this the right way? Just for curiosity, can you please tell me the loss function you have used and why?

Active Oldest Votes.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Already on GitHub? Sign in to your account. I was wondering if there was a straightforward way in Keras or would I have to write my own layer? I'm aware of ; however, in this case, I believe the original poster wanted it so that the convolutional layer does not accept new inputs across timesteps so something like Figure 3, pg.

That should give you everything you need. Don't flatten the CNN outputs, use reshape instead. I would be appreciated if you could help. I have sequence of frames and I am going to map them to a sequence of predefined labels. My problem is how to define permute and reshape to connect the output layer of convolution layer to LSTM. Am I using the package wrong?

Get Started with Using CNN+LSTM for Forecasting

Or is there something I need to implement somewhere? Thanks for taking the time to read this. OnlySang afsanehghasemi At the moment, these layers don't work with the current version of Keras which uses multiple backends. If you use a version of Keras from September or October before the new updatethen the layers as they currently are should work.

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On the other hand, if you don't want to use a slightly earlier Keras version, I am planning on releasing an updated version of the layers soon that should work with the newest Keras version.

I will update this thread when I do release the update. Yes, I did realise that, I changed the input to 30, 1, Thanks for your prompt support here. I will be waiting for your update. It's somewhat impractical to keep up with the changes in Keras as a separate repo as it is constantly changing.

I know fchollet had plans for a general time distributed layer, though I think it's been on the backlog for a while. However, if making a general time distributed layer is too much work or is taking too much time, and if TimeDistributedConvolution2D, TimeDistributedPooling2D, and TimeDistributedFlatten seem to be something that could be useful to Keras users especially those training CNN-RNN netsthen they or a subset thereof may be worth considering for inclusion in fact, TimeDistributedDense and TimeDistributedMerge are already specific time distributed layers.

cnn lstm github keras

It could be best to put all the time distributed layers in one place, to be used in conjunction with RNNs. Or, even better yet, for Flatten, Convolution2D, and Pooling2D, we can have a flag say, td such that if td is set to True, the layers do the appropriate operations to be TimeDistributed. But I'll leave the API decisions to others. On Tuesday, December 22,afsanehghasemi notifications github. But when I run the code this error occurs "TypeError: rnn got an unexpected keyword argument 'mask' ".

Would you please help me on this issue?

16. Video Frame Prediction using CNNs and LSTMs (2019)

Thank you very much in advance. Shadi94 Could you build a github gist with your code?GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.

I have taken 5 classes from sports 1M dataset like unicycling, marshal arts, dog agility, jetsprint and clay pigeon shooting. First I have captured the frames per sec from the video and stored the images.

I gave the labels to those images and trained them on VGG16 pretrained model. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. To classify video into various classes using keras library with tensorflow as back-end. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.

Latest commit Fetching latest commit…. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Jul 5, Set theme jekyll-theme-cayman. Jun 10, Whether it is stock price in financial market, power or energy consumption, or sales projection for corporate planning, a series of time-based data points can be the representation of how the world is thinking in any given moment, which has always fascinated me.

The capability to see and react ahead of time is an important factor to succeed in many aspects of life. Time Series model is very effective when there are clearly trend, seasonality or autocorrelation in the data. These factors are manifest especially when forecasting reaches to the granular level such as hours, or minutes.

LSTM long short-term memory is a recurrent neural network architecture that has been adopted for time series forecasting. I have been using stateful LSTM for my automated real-time prediction, as I need the model to transfer states between batches. Recently, I found adding convolutional layers to capture local, temporal pattern on top of LSTM layers can be immensely helpful in certain scenarios.

In this post, I will use a simple example to demonstrate this architecture. I personally benefit a lot from this series. For this demonstration, I used the individual household electric power consumption data from UCI machine learning repository.

I resampled the data over hours. In this post, I focus on the global active power attribute and disregard other variables. The data looks as below:. Problem Statement. So essentially this is a sequence to sequence prediction problem.

Model Architecture. I used a 1D convolutional layer followed by a max pooling layer, the output is then flattened to feed into LSTM layers. The model has two hidden LSTM layers followed by a dense layer to provide the output. The data is first reshaped and rescaled to fit the three-dimensional input requirements of Keras sequential model.

The input shape would be 24 time steps with 1 feature for a simple univariate model. And I chose the kernel size to be 3. The dense layer has 24 neurons to produce 24 output number. Below is a detailed model summary. As you can see, I used a batch input size of This is because, in this problem, which is also the case of many real-world situations, I want to predict on a daily basis.

The number of batch size needs to be divisible by In the end, I fit the model for 20 epochs and output the loss. I used mean squared error loss function and Adam optimization Adaptive Moment Estimation. Final Thoughts. Whether you should use RNN or CNN or hybrid models for time series forecasting really depends on the data and the problem you try to solve. I would go with a simple model if it serves the purpose and does not risk to overfit. I believe this particular data can be fit better with a multivariate LSTM model.

Sign in. A method you should consider when you have data of low granularity with recurring local pattern. Yitong Ren Follow.

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The model looks like the following taken from the paper. I dont know whether I am setting up the Text feature vector right in the above diagram right! I tried and I get the error as. I did follow the section on Note on specifying the initial state of RNNs in keras documentation and code. An LSTM has 2 hidden states, but you are providing only 1 initial state. You could do one of the following:. Learn more. Asked 2 years, 7 months ago. Active 2 years, 6 months ago. Viewed 3k times.

I will fix it. Active Oldest Votes. After reading thru philipperemy. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog. The Overflow How many jobs can be done at home? Featured on Meta. Community and Moderator guidelines for escalating issues via new response…. Feedback on Q2 Community Roadmap. Technical site integration observational experiment live on Stack Overflow.

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