udacity-deep-learning | Udacity Deep Learning class with TensorFlow | Machine Learning library

 by   hankcs Python Version: Current License: GPL-3.0

kandi X-RAY | udacity-deep-learning Summary

kandi X-RAY | udacity-deep-learning Summary

udacity-deep-learning is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Numpy, Jupyter applications. udacity-deep-learning has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However udacity-deep-learning build file is not available. You can download it from GitHub.

Assignments for Udacity Deep Learning class with TensorFlow in PURE Python, not IPython Notebook
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              udacity-deep-learning has a low active ecosystem.
              It has 66 star(s) with 54 fork(s). There are 10 watchers for this library.
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              It had no major release in the last 6 months.
              There are 0 open issues and 1 have been closed. On average issues are closed in 3 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of udacity-deep-learning is current.

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              udacity-deep-learning has no bugs reported.

            kandi-Security Security

              udacity-deep-learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              udacity-deep-learning is licensed under the GPL-3.0 License. This license is Strong Copyleft.
              Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

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              udacity-deep-learning releases are not available. You will need to build from source code and install.
              udacity-deep-learning has no build file. You will be need to create the build yourself to build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed udacity-deep-learning and discovered the below as its top functions. This is intended to give you an instant insight into udacity-deep-learning implemented functionality, and help decide if they suit your requirements.
            • Merge trainable dataset
            • Create nb_arrays
            • Return the next batch
            • Returns the id of a character
            • Trains logistic regression
            • Displays a sample
            • Pick a list of images
            • Load a single letter file
            • Display the number of images in a pickle file
            • Check if all images are well balanced
            • Create a Seq2Sequence model
            • Returns the next batch
            • Build dataset
            • Return the ID of a character
            • Plot a sample dataset
            • Find overlap between two datasets
            • Extract data from a file
            • Sanitize two datasets
            • Generate a batch of data
            • Plot embeddings
            • Generate fake label
            • Display overlap plot
            • Plot the balance
            • Try to download file
            • Plots samples in each folder
            Get all kandi verified functions for this library.

            udacity-deep-learning Key Features

            No Key Features are available at this moment for udacity-deep-learning.

            udacity-deep-learning Examples and Code Snippets

            No Code Snippets are available at this moment for udacity-deep-learning.

            Community Discussions

            QUESTION

            What does the dataset = dataset[0:num_images, :, :] code does?
            Asked 2021-Mar-17 at 10:49

            I am working on a deep-learning course from udacity and there is this one line of code that I do not understand as what it does? and why?. Can anyone help me understand it? It will be great if anyone can share any document related to it.

            ...

            ANSWER

            Answered 2021-Mar-17 at 10:49

            What you have in dataset is a tensor (or a multidimensional array). In your case it has 3 dimensions.

            Source https://stackoverflow.com/questions/66670862

            QUESTION

            How to handle "ValueError: Could not find a format to read the specified file in single-image mode"?
            Asked 2021-Mar-16 at 00:20

            Source code link: https://github.com/rndbrtrnd/udacity-deep-learning/blob/master/1_notmnist.ipynb

            I am working with some of the existing code already written to read image's data as part of deep-learning course using ndimage.imread which is depricated and I changed it to imageio.imread to proceed. But I am still facing some issues (I think it is related to bad image's data).

            Initial Code to read image data using ndimage.imread (depreciated). This code does not work as ndimage is depricated from 1.2.0 version of SciPy.

            ...

            ANSWER

            Answered 2021-Mar-16 at 00:20

            I spent a whole day to figure out a way to handle it and it turns out the solution is simpler than I thought.

            Solution: I was able to solve this issue by catching the ValueError exception and continue with the reading of image files.

            Here is the code:

            Source https://stackoverflow.com/questions/66638797

            QUESTION

            Can anyone help me understand, How the below code works?
            Asked 2021-Mar-14 at 07:32

            Part of code that I do not understand, how it work?

            ...

            ANSWER

            Answered 2021-Mar-14 at 05:21

            I read it like this:

            Join path and folder name which is coming from the for loop one at a time, if the joined path and folder name is a directory. If not, just skip the said path.

            The purpose of the code is to build a list of sorted directories.

            Source https://stackoverflow.com/questions/66621320

            QUESTION

            Does bias in the convolutional layer really make a difference to the test accuracy?
            Asked 2020-Feb-10 at 09:53

            I understand that bias are required in small networks, to shift the activation function. But in the case of Deep network that has multiple layers of CNN, pooling, dropout and other non -linear activations, is Bias really making a difference? The convolutional filter is learning local features and for a given conv output channel same bias is used.

            This is not a dupe of this link. The above link only explains role of bias in small neural network and does not attempt to explain role of bias in deep-networks containing multiple CNN layers, drop-outs, pooling and non-linear activation functions.

            I ran a simple experiment and the results indicated that removing bias from conv layer made no difference in final test accuracy. There are two models trained and the test-accuracy is almost same (slightly better in one without bias.)

            • model_with_bias,
            • model_without_bias( bias not added in conv layer)

            Are they being used only for historical reasons?

            If using bias provides no gain in accuracy, shouldn't we omit them? Less parameters to learn.

            I would be thankful if someone who have deeper knowledge than me, could explain the significance(if- any) of these bias in deep networks.

            Here is the complete code and the experiment result bias-VS-no_bias experiment

            ...

            ANSWER

            Answered 2020-Feb-10 at 09:53

            Biases are tuned alongside weights by learning algorithms such as gradient descent. biases differ from weights is that they are independent of the output from previous layers. Conceptually bias is caused by input from a neuron with a fixed activation of 1, and so is updated by subtracting the just the product of the delta value and learning rate.

            In a large model, removing the bias inputs makes very little difference because each node can make a bias node out of the average activation of all of its inputs, which by the law of large numbers will be roughly normal. At the first layer, the ability for this to happens depends on your input distribution. For MNIST for example, the input's average activation is roughly constant. On a small network, of course you need a bias input, but on a large network, removing it makes almost no difference.

            Although in a large network it has no difference, it still depends on network architecture. For instance in LSTM:

            Most applications of LSTMs simply initialize the LSTMs with small random weights which works well on many problems. But this initialization effectively sets the forget gate to 0.5. This introduces a vanishing gradient with a factor of 0.5 per timestep, which can cause problems whenever the long term dependencies are particularly severe. This problem is addressed by simply initializing the forget gates bias to a large value such as 1 or 2. By doing so, the forget gate will be initialized to a value that is close to 1, enabling gradient flow.

            See also:

            Source https://stackoverflow.com/questions/51959507

            QUESTION

            Tensorflow LSTM example input format batches2string
            Asked 2017-Jul-27 at 07:24

            I'm following Udacity's LSTM tutorial but having a hard time understanding input data format of LSTM. https://github.com/rndbrtrnd/udacity-deep-learning/blob/master/6_lstm.ipynb

            Can someone explain what num_unrolling in below code? or how to generate a training batch for the LSTM model?

            ...

            ANSWER

            Answered 2017-Jul-27 at 07:24

            Remember the purpose of the RNN you are training is to predict the next character in a string and you have one LSTM for each character position. In the code above the cited code they have also made a mapping from a character to a number ' ' is 0, 'a' is 1, 'b' is 2 etc. And this is further translated into '1-hot' encodings that is ' ' which is 0 is encoded as [1 0 0 0 ... 0], 'a' which is 1 as [0 1 0 0 ... 0], 'b' as [0 0 1 0 0 ... 0]. In my explanation below I skip this mapping for clarity, so all my characters should really be numbers or actually 1-hot encodings.

            Let me start with the simpler case, where batch_size = 1 and num_unrollings =1. Let us also say your training data is "anarchists advocate social relations based upon voluntary association of autonomous individuals mutu"

            In this case your first character is the 'a' in anarchists and the expected output (label) is the 'n'. In the code this is represented by the return value of next(). batches = [ [ 'a' ], ['n' ]], where the first element of the list is the input and the last element is the label. This is then in step 0 fed into the RNN. In the next step the input is 'n' and the label is 'a' (the third letter in 'anarchi...', so the next batches = [ ['n'], ['a'] ] and the third step batches is batches = [ ['a'] , ['r']] and so on. Notice how the last element (self._last_batch) in the inner list is the first element in the inner list at the next time-step (batches = [self._last_batch]).

            This is if the num_unrollings = 1. If num_unrollings = 5 then instead of only stepping one lstm unit forward every time you step num_unrolling=5 lstm units forward in every time-step. Thus the next function should provide the input for the 5 first RNN that is the 5 characters 'a','n','a','r','c' and also the corresponding labels 'n','a','r','c','h'. Notice the last four input characters are the same as the first 4 labels, so to be more memory efficient this is encoded as a list of the first 6 characters i.e.

            batches = [ [ 'a'],['n'],['a'],['r'],['c'], ['h'] ],

            with the understanding that the first 5 charaters are the input and the last 5 characters are the labels. and the next time next is called next returns the input and labels for the next 5 lstms

            batches = [ [ 'h'], ['i'], ['s'], ['t'], ['s'], [' '] ] ], Notice the 'h' is also in this list as it before only was used as a label and now only is used as an input.

            if batch_size > 1 you simoultaneously feed several sequences into the RNN update step. Notice here cursor is not a cursor but a list of cursors --- one for each sequence. consider now batch_size = 2. In the above example where the trainingdata is the 100 characters "anarchists advocate social relations based upon voluntary association of autonomous individuals mutu" the second sequence of text simply starts in the middle "luntary association of autonomous individuals mutu", so the batches in the first step contain the information [ 'a','n','a','r','c', 'h'] and ['l','u','n','t','a','r'], corresponding to the same 6 first characters as before and the first 6 characters after the middle. but it is organized as follows (tranposed) batches = [ [ 'a', 'l'], ['n', 'u'], ['a', 'n'], ['r', 't'], [c', 'a'], ['h', 'r']] the batches in the second time-step is contains the information [ 'h', 'i', 's', 't', 's', ' ' ] and ['r','y', ' ', 'a', 's', 's'] , but again tranposed batches = [[ 'h', 'r'], ['i', 'y'], ['s', ' '], ['t', 'a'], ['s', 's', [' ', 's'] ] and so on.

            The above is a technical answer to what num_unrollings means for the generation of the batches. However num_unrollings is also the number of characters you go back in the backpropagation part of the updating of the weights in the RNN. This is so as in each time-step in the RNN learning algorithm you feed in num_unrolling input characters and you only calculate on the corresponding lstm's, while the (hidden) input from the previous part of the sequence is stored in variables that are not trainable. You may try to set the num_urollings to 1 and see whether it is harder to learn long range correlations. (you might need many timesteps).

            Source https://stackoverflow.com/questions/45042444

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            Install udacity-deep-learning

            You can download it from GitHub.
            You can use udacity-deep-learning like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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