lrcn

by jeffdonahue CSS Version: Current License: No License

X-Ray Key Features Code Snippets(2)Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | lrcn Summary

lrcn is a CSS library. lrcn has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

lrcn

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lrcn has a low active ecosystem.

It has 7 star(s) with 1 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of lrcn is current.

Quality

lrcn has no bugs reported.

Security

lrcn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

lrcn does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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lrcn releases are not available. You will need to build from source code and install.

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lrcn Key Features

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lrcn Examples and Code Snippets

Initialize the model .

python

Lines of Code : 60

License : Permissive (MIT License)

Copy

def __init__(self, nb_classes, model, seq_length,
                 saved_model=None, features_length=2048):
        """
        `model` = one of:
            lstm
            lrcn
            mlp
            conv_3d
            c3d
        `nb_classe

Create LRCN model .

python

Lines of Code : 57

License : Permissive (MIT License)

Copy

def lrcn(self):
        """Build a CNN into RNN.
        Starting version from:
            https://github.com/udacity/self-driving-car/blob/master/
                steering-models/community-models/chauffeur/models.py

        Heavily influenced by V

Community Discussions

Trending Discussions on lrcn

Using XLOOKUP in two different Sheets and having a variable array extent (VBA Coding)

Training acc decreasing, validation - increasing. Training loss, validation loss decreasing

Loading a video dataset (Keras)

Message type "caffe.LayerParameter" has no field named "lstm_param"

QUESTION

Using XLOOKUP in two different Sheets and having a variable array extent (VBA Coding)

Asked 2021-Apr-08 at 11:16

Hello there I am very new to VBA coding and coding in general so I hope you can come up with a quick answer to my problem.

I am trying to get a XLookup-Formula into my vba-Code. The code is referencing to another Sheet ("Chart Plan" and is supposed to take the values in column "D" and "E" (starting from row 2) as fixed arrays down to the last row for the "Lookup array" and "return array". I want this to be variable as the "Chart Plan" is updated with different row numbers according to what I am working on. The formula then is supposed to return the values into the active worksheet (Column "J") and go through all the rows ("B" given as RC[-8] = Lookup value). The problem, I guess, is that I don't really know how the syntax is for giving the arrays into the formula or is it something else entirly? Mixing between RC-Annotation and A1-Annotation maybe?

Thank you.

...

ANSWER

Answered 2021-Apr-08 at 11:16

I don't have XLOOKUP on my version of Excel but this would be using VLOOKUP

Source https://stackoverflow.com/questions/66994087

QUESTION

Training acc decreasing, validation - increasing. Training loss, validation loss decreasing

Asked 2019-Jan-25 at 17:57

I am trying to implement LRCN but I face obstacles with the training. Currently, I am trying to train only the CNN module, alone, and then connect it to the RNN. The result you see below is somewhat the best possible one I have achieved so far. The problem with it is that everything seems to be going well except the training accuracy. It is gradually dropping.

My model has aggressive dropouts between the FC layers, so this may be one reason but still, do you think something is wrong with these results and what should I aim for changing if they continue the trend?

The number classes to predict is 3.The code is written in Keras.

Epoch 1/20 16602/16602 [==============================] - 2430s 146ms/step - loss: 1.2583 - acc: 0.3391 - val_loss: 1.1373 - val_acc: 0.3306

Epoch 00001: val_acc improved from -inf to 0.33058, saving model to weights.01-1.14.hdf5 Epoch 2/20 16602/16602 [==============================] - 2441s 147ms/step - loss: 1.1998 - acc: 0.3356 - val_loss: 1.1342 - val_acc: 0.3719

Epoch 00002: val_acc improved from 0.33058 to 0.37190, saving model to weights.02-1.13.hdf5 Epoch 3/20 8123/16602 [=============>................] - ETA: 20:30 - loss: 1.1889 - acc: 0.3325

I have 2 more short questions which I cannot answer in a while.

Why the tensor I output from my custom video data generator is of dimensions: (4, 288, 224, 1) but the layer of my input shape is generated as (None, 288, 224, 1)? To clarify the shape, I am classifying batches of 4 containing single images in a non-time distributed CNN. I use functional API.
Later, when I train the RNN, I will have to make predictions per time-step, then average them out and choose the best one as a prediction of my overall model's prediction. Does metrics['accuracy'] do that or I need a custom metric function? If the latter, how do I write one as according to: Keras doc. the results from evaluating a metric are not used when training the model. Do I need a custom objective (loss) function for that purpose?

Any help, expertise will be highly appreciated, I really need it. Thank you in advance!

...

ANSWER

Answered 2019-Jan-25 at 17:57

As long as the loss keeps dropping the accuracy should eventually start to grow. Since you only yet trained for 2-3 Epochs, I would say it's normal that the accuracy may fluctuate.

As for your other questions:

The notion for the input shape of a layer is (batchSize, dim1, dim2, nChannels). Since your model doesn't know the batch size before training None is used as a sort of placeholder. The image dimensions seem to be right and a number of Channels of one means that you don't use colored images, so there's only one entry per pixel.
I think that the accuracy metric should do fine, however I have no experience with RNN, so maybe someone else can answer this.

Source https://stackoverflow.com/questions/54368659

QUESTION

Loading a video dataset (Keras)

Asked 2019-Jan-02 at 14:12

I'm trying to implement an LRCN/C(LSTM)RNN to classify emotions in videos. My dataset structure is split in two folders - "train_set" and "valid_set". When you open, either of them, you can find 3 folders, "positive", "negative" and "surprise". Lastly, each of these 3 folders has video-folders, each of which is a collection of frames of a video in .jpg. Videos have different length, hence a video-folder can have 200 frames, the one next to it 1200, 700...! To load the dataset I am using flow_from_directory. Here, I need a few clarifications:

Will in my case flow_from_directory load the videos 1 by 1, sequentially? Their frames?
If I load into batches, does flow_from_directory take a batch based on the sequential ordering of the images in a video?
If I have video_1 folder of 5 images and video_2 folder of 3 videos, and a batch size of 7, will flow_from_directory end up selecting two batches of 5 and 3 videos or it will overlap the videos, taking all 5 images from the first folder + 2 of the second? Will it mix my videos?
Is the dataset loading thread-safe? Worker one fetches video frames sequentially from folder 1, worker 2 from folder 2 etc... or each worker can takes frames from anywhere and any folder, which can spoil my sequential reading?
If I enable shuffle, will it shuffle the order in which it would read the video folders or it will start fetching frames in random order from random folders?
What does TimeDisributed layer do as from the documentation I cannot really imagine? What if I apply it to a CNN's dense layer or to each layer of a CNN?

...

ANSWER

Answered 2019-Jan-02 at 11:33

flow_from_directory is made for images, not movies. It will not understand your directory structure and will not create a "frames" dimension. You need your own generator (usually better to implement a keras.utils.Sequence)
You can only load into batches if :
- you load movies one by one due to their different lengths
- you pad your videos with empty frames to make them all have the same length
Same as 1.
If you make your own generator implementing a keras.utils.Sequence(), the safety will be kept as long as your implementation knows what is each movie.
It would shuffle images if you were loading images
TimeDistributed allows data with an extra dimension at index 1. Example: a layer that usually takes (batch_size, ...other dims...) will take (batch_size, extra_dim, ...other dims...). This extra dimension may mean anything, not necessarily time, and it will remain untouched.
- Recurrent layers don't need this (unless you really want an extra dimension there for unusual reasons), they already consider the index 1 as time.
- CNNs will work exactly the same, for each image, but you can organize your data in the format (batch_size, video_frames, height, width, channels)

Source https://stackoverflow.com/questions/54004682

QUESTION

Message type "caffe.LayerParameter" has no field named "lstm_param"

Asked 2017-Mar-14 at 07:28

I was trying to run the LRCN example from Jeff Donahue's recurrent-rebase-cleanup branch of Caffe. I have installed the latest caffe version from the master branch. According to my knowledge Caffe now supports LSTM layers. But when I run the solver I get this error. Is the name of the field wrong? If so then what is the correct field name and how can I find caffe layer parameter & field names for future use?

I also tried running with the parameter name as recurrent_param but still get the same error.

...

ANSWER

Answered 2017-Mar-14 at 07:28

If you are using "LSTM" layer from the latest "master" branch, you need to use recurrent_param instead of lstm_param.
For more information see caffe.help.

Generally speaking, if you are trying to run a model build in a specific branch of caffe, you should build and use caffe of that specific branch as layer names/parameters may vary across branches (as it seems to be the case here)

Source https://stackoverflow.com/questions/42774738

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Install lrcn

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