Deep-Learning | Deep learning practice : handwritten digit recognition
kandi X-RAY | Deep-Learning Summary
kandi X-RAY | Deep-Learning Summary
:computer: Deep learning practice: handwritten digit recognition, Discuz verification code recognition, garbage classification, semantic segmentation
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Top functions reviewed by kandi - BETA
- Train CRACK Captcha
- Crack capture
- Get next batch of images
- Get training images
- Train a model
- Compute accuracy
- Update the statistics
- Get next batch of images
- Validate the given model
- Mask a video
- Gradient check function
- Calculates the gradient of the function
- Train the given net
- NNN layer
- Download random captcha
- Train and evaluate and evaluate the network
- Calculate the derivative of the filter
- Test the model
- Performs the forward computation
- Train a LinearUnit
- Train and return a Perceptron
- Get a logger
- Add a video to ffmpeg
- Convert video to mp3
- Forward through the input array
- Calculates the gradient for the given label
Deep-Learning Key Features
Deep-Learning Examples and Code Snippets
from trax.supervised import training
# Training task.
train_task = training.TrainTask(
labeled_data=train_batches_stream,
loss_layer=tl.WeightedCategoryCrossEntropy(),
optimizer=trax.optimizers.Adam(0.01),
n_steps_per_checkpoint=500,
class Embedding(base.Layer):
"""Trainable layer that maps discrete tokens/IDs to vectors."""
def __init__(self,
vocab_size,
d_feature,
kernel_initializer=init.RandomNormalInitializer(1.0)):
"""Ret
from tensorflow.keras.models import Sequential
model = Sequential()
from tensorflow.keras.layers import Dense
model.add(Dense(units=64, activation='relu'))
model.add(Dense(units=10, activation='softmax'))
model.compile(loss='categorical_crossentr
Community Discussions
Trending Discussions on Deep-Learning
QUESTION
Suppose you have a neural network with 2 layers A and B. A gets the network input. A and B are consecutive (A's output is fed into B as input). Both A and B output predictions (prediction1 and prediction2) Picture of the described architecture You calculate a loss (loss1) directly after the first layer (A) with a target (target1). You also calculate a loss after the second layer (loss2) with its own target (target2).
Does it make sense to use the sum of loss1 and loss2 as the error function and back propagate this loss through the entire network? If so, why is it "allowed" to back propagate loss1 through B even though it has nothing to do with it?
This question is related to this question https://datascience.stackexchange.com/questions/37022/intuition-importance-of-intermediate-supervision-in-deep-learning but it does not answer my question sufficiently. In my case, A and B are unrelated modules. In the aforementioned question, A and B would be identical. The targets would be the same, too.
(Additional information) The reason why I'm asking is that I'm trying to understand LCNN (https://github.com/zhou13/lcnn) from this paper. LCNN is made up of an Hourglass backbone, which then gets fed into MultiTask Learner (creates loss1), which in turn gets fed into a LineVectorizer Module (loss2). Both loss1 and loss2 are then summed up here and then back propagated through the entire network here.
Even though I've visited several deep learning lectures, I didn't know this was "allowed" or makes sense to do. I would have expected to use two loss.backward()
, one for each loss. Or is the pytorch computational graph doing something magical here? LCNN converges and outperforms other neural networks which try to solve the same task.
ANSWER
Answered 2021-Jun-09 at 10:56From the question, I believe you have understood most of it so I'm not going to details about why this multi-loss architecture can be useful. I think the main part that has made you confused is why does "loss1" back-propagate through "B"? and the answer is: It doesn't. The fact is that loss1 is calculated using this formula:
QUESTION
I am trying to create a GPU instance (n1-standard-2
with 1 NVIDIA T4 GPU) on Compute Engine and I have been getting this error since yesterday:
ANSWER
Answered 2021-Jun-06 at 19:50Finally, I was able to launch a preemptible GPU instance without a problem. So it really seems like Google Cloud doesn't have enough GPU resources to reserve an on-demand GPU VM at the moment.
QUESTION
Sorry if my description is long and boring but I want to give you most important details to solve my problem. Recently I bought a Jetson Nano Developer Kit with 4Gb of RAM, finally!, and in order to get, which I consider, the best configuration for object detection I am following this guide made by Adrian Rosebrock from Pyimagesearch:
https://www.pyimagesearch.com/2020/03/25/how-to-configure-your-nvidia-jetson-nano-for-computer-vision-and-deep-learning/ Date:March, 2020. A summary of this guide is the following:
- 1: Flash Jetson Pack 4.2 .img inside a microSD for Jetson Nano(mine is 32GB 'A' Class)
- 2: Once inserted on the Nano board, configure Ubuntu 18.04 and get rid of Libreoffice entirely to get more available space
- 3: Step #5: Install system-level dependencies( Including cmake, python3, and nano editor)
- 4: Update CMake (without any errors)
- 5: Install OpenCV system-level dependencies and other development dependencies
- 6: Set up Python virtual environments on your Jetson Nano( succesfully installed virtualenv and virtualenvwrapper without errors including the bash file edition with nano)
- 7: Create virtaul env with python 3 and install protobuf and libprotobuf to get an more efficient Tensorflow. Succesfully installed. It took an hour to finish, that's normal
- 8: Here comes the headbreaker: install numpy and cython inside this env and check it importing numpy library When I try to do this step I get: Illegal instruction(core dumped) as you can see in the image: [Error with Python3.6.9]: https://i.stack.imgur.com/rAZhm.png
I said, well let's continue with this tutorial anyway:
- 9: Install Scipy v1.3.3: everything is ok with first three lines, but when I have to use python to execute the stup.py file, IT shows up again(not the clown). [Can't execute this line either]: https://i.stack.imgur.com/wFmnt.jpg
Then I ran an experiment, I have created this "p2cv4" env with Python 2, installed numpy and tested it: [With Python 2]: https://i.stack.imgur.com/zCWif.png
I can exit() whenever I want and execute other lines that use python So I concluded that is a python version issue. When I want to execute any python code, terminal ends the program with core dumping, apt-get or pip DO NOT show any errors. And I want to use python 3 because someday in the future a package or library will require python 3.
For python 3 last version for the Jetson Nano is 3.6.9, and idk which version was currently active in March, 2020, like the one Adrian used at that time
In other posts I read that this SIGILL appears when a package or library version like Numpy of TF is not friendly anymore with a specific old or low power CPU, like in this posts: Illegal hardware instruction when trying to import tensorflow, https://github.com/numpy/numpy/issues/9532
So I want to downgrade to a older python version like 3.6.5 or 3.5 but I can't find clear steps to do so in Ubuntu. I thinks this will fix this error and let me continue with configurations on the Jetson Nano.
The pyimageseach guide uses Python 3.6 but it do not specifies if is last 3.6.9 or another. If is not python causing this error let me know. HELP please!
...ANSWER
Answered 2021-Jan-09 at 15:30I had this very same problem following the same guide. BTW, in this scenario, numpy worked just fine in python when NOT in a virtualenv. GDB pointed to a problem in libopenblas.
My solution was to start from scratch with a fresh image of jetson-nano-4gb-jp441-sd-card-image.zip and repeat that guide without using virtualenv. More than likely you are the sole developer on that Nano and can live without virtualenv.
I have followed these guides with success: https://qengineering.eu/install-opencv-4.5-on-jetson-nano.html
Skip the virtualenv portions https://www.pyimagesearch.com/2019/05/06/getting-started-with-the-nvidia-jetson-nano/
I found this to also be required at this point: "..install the official Jetson Nano TensorFlow by.."
QUESTION
In Python you can use a pretrained model as a layer as shown below (source here)
...ANSWER
Answered 2021-May-06 at 09:21Solved using this API modification in Sequential.cs:
QUESTION
I need to use face detection to finish my homework and then I searched on the Internet and I think that using a pre-trained deep learning face detector model with OpenCV's DNN module is easy and good, it works well. Where I learnt it is here: https://www.pyimagesearch.com/2018/02/26/face-detection-with-opencv-and-deep-learning/ , but I am really confused about the 4D array returned by net.forward():
...ANSWER
Answered 2021-May-02 at 15:053rd dimension helps you iterate over predictions and
in the 4th dimension, there are actual results
class_lable = int(inference_results[0, 0, i,1])
--> gives one hot encoded class label for ith box
conf = inference_results[0, 0, i, 2]
--> gives confidence of ith box prediction
TopLeftX,TopLeftY, BottomRightX, BottomRightY = inference_results[0, 0, i, 3:7]
-->gives
co-ordinates bounding boxes for resized small image
and 2nd dimension is used when the predictions are made in more than one stages, for example in YOLO the predictions are done at 3 different layers.
you can iterate over these predictions using 2nd dimension like [:,i,:,:]
QUESTION
I have this code in which mtcnn detects faces on an image, draws a red rectangle around each face and prints on the screen.
Code taken from: https://machinelearningmastery.com/how-to-perform-face-detection-with-classical-and-deep-learning-methods-in-python-with-keras/
But I want to save the image with the red boxes arround each face. So that i can do some preprocessing on it. Any help is good.
...ANSWER
Answered 2021-Apr-28 at 01:21You can use matplotlib.pyplot.savefig
. For example:
QUESTION
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 13:15As of April 19. 2020 it is not possible with the .NET Wrapper as documented in this issue on the GitHub page for Keras.NET
QUESTION
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 12:47Solved the issue parsing manually the attribute '.str' of 'line0' into an array of ints.
QUESTION
I am trying to find the direction of triangles in an image. below is the image:
These triangles are pointing upward/downward/leftward/rightward. This is not the actual image. I have already used canny edge detection to find edges then contours and then the dilated image is shown below.
My logic to find the direction:
The logic I am thinking to use is that among the three corner coordinates If I can identify the base coordinates of the triangle (having the same abscissa or ordinates values coordinates), I can make a base vector. Then angle between unit vectors and base vectors can be used to identify the direction. But this method can only determine if it is up/down or left/right but cannot differentiate between up and down or right and left. I tried to find the corners using cv2.goodFeaturesToTrack
but as I know it's giving only the 3 most effective points in the entire image. So I am wondering if there is other way to find the direction of triangles.
Here is my code in python to differentiate between the triangle/square and circle:
...ANSWER
Answered 2021-Apr-05 at 18:03Well, Mark has mentioned a solution that may not be as efficient but perhaps more accurate. I think this one should be equally efficient but perhaps less accurate. But since you already have a code that finds triangles, try adding the following code after you have found triangle contour:
QUESTION
I'm working on a deep learning project and I tried following a tutorial to evaluate my model with Cross-Validation.
I was looking at this tutorial: https://machinelearningmastery.com/use-keras-deep-learning-models-scikit-learn-python/
I started by first splitting my dataset into features and labels:
...ANSWER
Answered 2021-Apr-06 at 06:08model = KerasClassifier(build_fn=create_model(), ...)
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No vulnerabilities reported
Install Deep-Learning
You can use 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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