deep-learning-with-python | Deep Learning with Python - Francis Chollet | 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 Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras 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.

Examples and Exercises from Deep Learning with Python - Francis Chollet

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deep-learning-with-python has a low active ecosystem.

It has 5 star(s) with 3 fork(s). There are 1 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.

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deep-learning-with-python has 0 bugs and 0 code smells.

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

deep-learning-with-python code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

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deep-learning-with-python does not have a standard license declared.

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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.

It has 2303 lines of code, 65 functions and 35 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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.

Generates a random pattern
Decomprocessing image
Plots smoothed learning curves
Smooth a list of points
Train model
Compute the loss and loss for each epoch
Smooth a curve
Evaluate the mean absolute error
Plots the temperature of the dataset
Vectorise a sequence of sequences
Evaluate a model
Create a recurrent dropout model
Construct a basic GRU
Create a model
Build model
Create a bidirectional GRU model
Construct a basic ML model
Create a 1D convolutional Convnet model
Prints out the summary
Extract features from given directory
Plot training and validation curves
Builds a recurrent model
Generate samples and targets

Get all kandi verified functions for this library.

deep-learning-with-python Key Features

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deep-learning-with-python Examples and Code Snippets

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Community Discussions

Trending Discussions on deep-learning-with-python

how should I code LSTM layer with Keras and Tensorflow 2.4.1?

Matrix size error when trying to visualize maximum activation of CNN prediction layer in Keras

Invalid argument: indices[124,0] = 2629 is not in [0, 64) (multi-input model)

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

QUESTION

how should I code LSTM layer with Keras and Tensorflow 2.4.1?

Asked 2022-Feb-02 at 09:57

I am studying Deep Learning with Python Book by François Chollet book chapter 10.2.5 I use tensorflow 2.4.1.

Here is the code for weather forcast by LSTM :

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ANSWER

Answered 2022-Feb-02 at 09:57

I solve the problem by downgrading numpy from 1.21 to 1.19

Source https://stackoverflow.com/questions/70942553

QUESTION

Matrix size error when trying to visualize maximum activation of CNN prediction layer in Keras

Asked 2022-Jan-03 at 08:18

Inspired by François Chollet's book "Deep Learning with Python" (1rst edition) I'm trying to generate a picture that maximizes a prediction of a VGG16 model.

The original procedure for intermediate layers is described here (from cell 12 on):

https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/first_edition/5.4-visualizing-what-convnets-learn.ipynb

Essentially, this involves a gradient descent for the input image:

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ANSWER

Answered 2022-Jan-03 at 08:18

Finally I found a workaround for this by writing an own random search function that minimizes the prediction difference to a given prediction:

Source https://stackoverflow.com/questions/70533302

QUESTION

Invalid argument: indices[124,0] = 2629 is not in [0, 64) (multi-input model)

Asked 2021-Dec-26 at 06:46

I am following Deep Learning with Python section 7.1.2 Multi-input models. Here on code of Listing 7.1, I am facing following errors:

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ANSWER

Answered 2021-Dec-26 at 06:46

For TF, a common method is to use model.summary() to check for the output shape at each layer of the network. Running your code returns

Source https://stackoverflow.com/questions/70484395

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)

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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.

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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.

...

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

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Install deep-learning-with-python

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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.

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