Deep-Learning-with-Python | 《Python 深度学习》一书的代码学习记录，使用中文注释 | Machine Learning library

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kandi X-RAY | Deep-Learning-with-Python Summary

Deep-Learning-with-Python is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Numpy applications. Deep-Learning-with-Python has no bugs, it has no vulnerabilities and it has low support. However Deep-Learning-with-Python build file is not available. You can download it from GitHub.

《Python 深度学习》(Deep Learning with Python )一书的代码学习记录，使用中文注释

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Deep-Learning-with-Python has a low active ecosystem.

It has 12 star(s) with 4 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

Deep-Learning-with-Python has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Deep-Learning-with-Python is current.

Quality

Deep-Learning-with-Python has no bugs reported.

Security

Deep-Learning-with-Python has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Deep-Learning-with-Python 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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Deep-Learning-with-Python releases are not available. You will need to build from source code and install.

Deep-Learning-with-Python 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 Deep-Learning-with-Python and discovered the below as its top functions. This is intended to give you an instant insight into Deep-Learning-with-Python implemented functionality, and help decide if they suit your requirements.

Generate samples
Extract features from a directory
Generate pattern
Decomprocessing image
Deprocessing image
Build the model
Smooth a curve
Evaluate the mean squared error
Generate a random probability distribution
Preprocess image
Computes the gradient of the loss function
Evaluate the loss function
Calculate loss
Calculate the variance of the cross entropy
Calculate style loss for a given style combination
Computes the gram matrix
Calculate the total variation loss between two images
Vectorize sequences
Resize an image
Save image to fname

Get all kandi verified functions for this library.

Deep-Learning-with-Python Key Features

No Key Features are available at this moment for Deep-Learning-with-Python.

Deep-Learning-with-Python Examples and Code Snippets

No Code Snippets are available at this moment for Deep-Learning-with-Python.

Community Discussions

Trending Discussions on Deep-Learning-with-Python

Keras.NET Using a Model as a Layer

Keras.NET How to Use KerasIterator

Numpy.NET Getting Values

ImageDataGenerator doesn't generate enough samples

fit_generator hangs on first step in R

Installing Tensorflow 2 gets a dll failed to load in pywrap_tensorflow.py

Optimization and Regularization for CNN Feature Visualization in Keras

How to resolve KeyError: 'val_mean_absolute_error' Keras 2.3.1 and TensorFlow 2.0 From Chollet Deep Learning with Python

Keras Tensorflow 'Cannot apply softmax to a tensor that is 1D'

Converting ndarray from float32 to uint8 messes the image up

QUESTION

Keras.NET Using a Model as a Layer

Asked 2021-May-06 at 09:21

In Python you can use a pretrained model as a layer as shown below (source here)

...

ANSWER

Answered 2021-May-06 at 09:21

Solved using this API modification in Sequential.cs:

Source https://stackoverflow.com/questions/67105434

QUESTION

Keras.NET How to Use KerasIterator

Asked 2021-Apr-13 at 13:27

I want to do the same as F. Chollet's notebook but in C#.

However, I can't find a way to iterate over my KerasIterator object:

...

ANSWER

Answered 2021-Apr-13 at 13:15

As of April 19. 2020 it is not possible with the .NET Wrapper as documented in this issue on the GitHub page for Keras.NET

Source https://stackoverflow.com/questions/67075485

QUESTION

Numpy.NET Getting Values

Asked 2021-Apr-13 at 12:47

I'm trying to "convert" the Keras notebooks made by F. Chollet to C# / .NET applications. You can find them here. I am specifically working on "3.5 - Movie Reviews" as of right now.

The problem is, I can't convert my NDarrays to C# arrays to use the values. I tried this method (in README - section Performance Considerations), but I get random values or Python Runtime errors.

...

ANSWER

Answered 2021-Apr-13 at 12:47

Solved the issue parsing manually the attribute '.str' of 'line0' into an array of ints.

Source https://stackoverflow.com/questions/66866731

QUESTION

ImageDataGenerator doesn't generate enough samples

Asked 2021-Jan-24 at 21:03

I am following F.Chollet book "Deep learning with python" and can't get one example working. In particular, I am running an example from chapter "Training a convnet from scratch on a small dataset". My training dataset has 2000 sample and I am trying to extend it with augmentation using ImageDataGenerator. Despite that my code is exactly the same, I am getting error:

Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least steps_per_epoch * epochs batches (in this case, 10000 batches).

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ANSWER

Answered 2021-Jan-24 at 13:35

It seems the batch_size should be 20 not 32.

Since you have steps_per_epoch = 100, it will execute next() on train generator 100 times before going to next epoch.

Now, in train_generator the batch_size is 32, so it can generate 2000/32 number of batches, given that you have 2000 number of training samples. And that is approximate 62.

So on 63th time executing next() on train_generator will give nothing and it will tell Your input ran out of data;

Ideally,

Source https://stackoverflow.com/questions/65870942

QUESTION

fit_generator hangs on first step in R

Asked 2020-Oct-10 at 18:55

Trying to run the temperature forecasting problem from Deep Learning in R. When I get to the section "A basic machine learning approach," running the fit_generator function below causes R to hang indefinitely.

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ANSWER

Answered 2020-Oct-10 at 18:55

That's a known issue, please refer to https://github.com/rstudio/keras/issues/1090

One of the solutions that sometimes works is to wrap original generator from R with keras:::as_generator.function function:

Source https://stackoverflow.com/questions/62307460

QUESTION

Installing Tensorflow 2 gets a dll failed to load in pywrap_tensorflow.py

Asked 2020-Jul-26 at 16:09

I have had problems here, here and there installing TensorFlow 2 over the last year or so. So I am trying Miniconda.

I have an AMD Radeon hd 6670 and an AMD Radeon hd 6450.

I just downloaded Miniconda and made an environment and did a pip install --upgrade tensorflow in a Miniconda prompt on Windows 8.1 and got TensorFlow 2.2.

When I try to import tensorflow I get the stack trace below.

I did download Visual Studio to get the latest redistributebles (I think).

seems like this occurs near this line: from tensorflow.python.pywrap_tensorflow_internal import *

Edit 1: I used this yaml file for python 3.6 (the other was 3.7), but it produced the same error.

Edit 2: I upgraded to Conda 4.8.3 and Python 3.7 (in the yaml file) and got the same error. This is the line in pywrap internal that shows the problem:

...

ANSWER

Answered 2020-Jul-26 at 16:09

Status Quo

I ran into a comparable problem (this is the furthest i got) reproducibly on two machines. Some of the discussed issues seems to be known for example here: 1 2 3 4. Not only to reproduce 2, it makes sense to also start using virtual environments in order to test multiple tf versions. This can be achieved like this: (link for virtualenv on windows)

Source https://stackoverflow.com/questions/62376660

QUESTION

Optimization and Regularization for CNN Feature Visualization in Keras

Asked 2020-Jun-12 at 02:37

I'm trying to implement this distill article on feature visualization for VGGFace model. I was able to find a tutorial but it didn't go in detail about optimization and regularization, which the distill article emphasized are crucial in feature visualization. So my question is how to (1) optimize and (2) regularize (using a learned prior like distill article)? My code here used very simple techniques and achieved results that are far from those generated by OpenAI Microscope on VGG16. Can someone help me please?

...

ANSWER

Answered 2020-Jun-12 at 02:37

So upon closer look at the distill article, in footnote[9]

Images were optimized for 2560 steps in a color-decorrelated fourier-transformed space, using Adam at a learning rate of 0.05. We used each of following transformations in the given order at each step of the optimization: • Padding the input by 16 pixels to avoid edge artifacts • Jittering by up to 16 pixels • Scaling by a factor randomly selected from this list: 1, 0.975, 1.025, 0.95, 1.05 • Rotating by an angle randomly selected from this list; in degrees: -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5 • Jittering a second time by up to 8 pixels • Cropping the padding

Source https://stackoverflow.com/questions/62332522

QUESTION

How to resolve KeyError: 'val_mean_absolute_error' Keras 2.3.1 and TensorFlow 2.0 From Chollet Deep Learning with Python

Asked 2020-Apr-16 at 04:32

I am on section 3.7 of Chollet's book Deep Learning with Python. The project is to find the median price of homes in a given Boston suburbs in the 1970's.

https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/3.7-predicting-house-prices.ipynb

At section "Validating our approach using K-fold validation" I try to run this block of code:

...

ANSWER

Answered 2020-Feb-04 at 09:09

The problem in your code is that, when you compile your model, you do not add the specific 'mae' metric.

If you wanted to add the 'mae' metric in your code, you would need to do like this:

model.compile('sgd', metrics=[tf.keras.metrics.MeanAbsoluteError()])
model.compile('sgd', metrics=['mean_absolute_error'])

After this step, you can try to see if the correct name is val_mean_absolute_error or val_mae. Most likely, if you compile your model like I demonstrated in option 2, your code will work with "val_mean_absolute_error".

Also, you should also put the code snippet where you compile your model, it is missing in the question text from above(i.e. the build_model() function)

Source https://stackoverflow.com/questions/60053547

QUESTION

Keras Tensorflow 'Cannot apply softmax to a tensor that is 1D'

Asked 2019-Apr-07 at 22:37

I'm going over the Book Deep Learning with Python from F. Chollet. https://www.manning.com/books/deep-learning-with-python

I'm trying to follow along with the code examples. I just installed keras, and I am getting this error when trying to run this: from this notebook: https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/2.1-a-first-look-at-a-neural-network.ipynb

...

ANSWER

Answered 2019-Apr-07 at 22:37

Seems like you have an incompatible Tensorflow version (which Keras is using as a backend). For details look here

Source https://stackoverflow.com/questions/55564133

QUESTION

Converting ndarray from float32 to uint8 messes the image up

Asked 2019-Mar-18 at 10:56

Following this blog I am trying to apply heatmap to the original image.

However I have a problem converting float32 to uint8. Before converting to uint8 if I save the image with:

...

ANSWER

Answered 2019-Mar-18 at 10:56

You have only negative values in heatmap. Since uint8 can only hold numbers between 0 and 255 the line heatmap = np.uint8(255 * heatmap) will only work as intended if the original values of heatmap are lying between 0. and 1..

Solution:
Rescale the array to the range of [0,255] before casting it to uint8:

Source https://stackoverflow.com/questions/55218120

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Deep-Learning-with-Python

You can download it from GitHub.
You can use Deep-Learning-with-Python 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.

Support

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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