keras-preprocessing | image data , text data | Machine Learning library
kandi X-RAY | keras-preprocessing Summary
kandi X-RAY | keras-preprocessing Summary
Keras Preprocessing is the data preprocessing and data augmentation module of the Keras deep learning library. It provides utilities for working with image data, text data, and sequence data. Read the documentation at:
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Top functions reviewed by kandi - BETA
- Construct a batch of batched images
- Apply an Affine transform
- Apply transform
- Transform matrix to center center
- List all valid filenames in a directory
- Generator that yields valid files
- Zoom the image using random affine transformation
- Random shift the image
- Random shear transformation
- Random rotation
- Performs one - hot hashing
- Generate a list of words with n words
- Convert text to a sequence of words
- Generate batches of transformed samples
- Save an image
- Calculate a random brightness
- Return a keras submodule
- Iterate through the index
- Set the index array
- Serialize timeseries to JSON
- Return a dictionary representation of the model
- Return a JSON representation of the tokenizer
- Returns a dictionary with the configuration
- Apply a random channel shift
keras-preprocessing Key Features
keras-preprocessing Examples and Code Snippets
Lines of Code : 83License : Strong Copyleft (CC BY-SA 4.0)
Copyimport os
folders = os.listdir('/content/data')
print(f'folders: {folders}')
total_images = 0
names = []
paths = []
white_list_formats = ('png', 'jpg', 'jpeg', 'bmp', 'ppm', 'tif', 'tiff')
for f in folders:
paths.append(os.listdir(f'/texts = [
'hello world' ,
'hello python' ,
'python' ,
'hello java' ,
'hello java' ,
'hello python'
]
# Frequency of words, hello -> 5, python -> 3 , java -> 2 , world -> 1
tokenizer = tf.keras.preprodef Exec_ShowImgGrid(ObjTensor, ch=1, size=(28,28), num=16):
#tensor: 128(pictures at the time ) * 784 (28*28)
Objdata= ObjTensor.detach().cpu().view(-1,ch,*size) #128 *1 *28*28
Objgrid= make_grid(Objdata[:num],nrow=4).permutetensorflow/tensorflow:1.14.0-gpu-py3
module purge
module load devel/cuda
/usr/bin/python3.8 -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
seq = tokenizer.text_to_sequences(lines)
seq = tokenizer.texts_to_sequences(lines)
opencv-python==4.2.0
opencv-python-headless==4.2.0.32
A: 50
B: 100
C: 200
wa = 350/(3*50) = 2.3
wb = 350/(3*100) = 1.16
wc = 350/(3*200) = 0.58
#Import the function
from sklearn.utils import class_weight
# get class weights
class_weights = class_weight.compute_cla(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
y_train = tf.keras.utils.to_categorical(y_train, 10)
y_test = tf.keras.utils.to_categorical(y_test, 10)
datagen = tf.keras.preprocessing.image.ImageDataGeneratorlow = np.array([cv2.imread(f.path) / 255 for f in os.scandir("/data/low")])
high = np.array([cv2.imread(f.path) / 255 for f in os.scandir("/data/high")])
train_low = low[:205]
validate_low = low[205:]
train_high = high[:205]
validate_out Community Discussions
Trending Discussions on keras-preprocessing
QUESTION
I'm trying to create N x N tensor using tf.while_loop in my custom Keras layer. Here, N (timesteps in code) is a Keras symbolic tensor (integer scalar). The below code is __call__ method of my custom Keras layer in Functional Model.
ANSWER
Answered 2022-Mar-27 at 13:45timesteps_mixed_outer = tf.concat([timesteps_mixed_outer, [timesteps_mixed_inner]], axis = 0)
You have to check the shape of timesteps_mixed_outer and timesteps_mixed_inner. try to change the axis value.
or try this.
QUESTION
Background
I am trying to plot an image noise using pytorch, however, when I reach to that point, the kernel dies. I am attempting the same code at Google Colab where I do get results
Result at Google Colab
Result at Jupyter
I do not think that it has something to do with the code itself, but I am posting the function to plot the grid:
...ANSWER
Answered 2022-Feb-28 at 22:25After a few days I was able to find the solution
Firstly, my code needed to be fixed to correctly call the params needed with the proper name
QUESTION
I got this error
AttributeError: module 'TensorFlow' has no attribute 'variable_scope'
when I ran my code and I'm searching in Google for this. They said it required TensorFlow lower than 2.0. Then I started to install it with this code in cmd pip install tensorflow==1.15 then I got another error
ERROR: Could not find a version that satisfies the requirement tensorflow==1.15 (from versions: 2.5.0rc0, 2.5.0rc1, 2.5.0rc2, 2.5.0rc3, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0rc0, 2.6.0rc1, 2.6.0rc2, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.7.0rc0, 2.7.0rc1, 2.7.0, 2.7.1, 2.8.0rc0, 2.8.0rc1, 2.8.0) ERROR: No matching distribution found for tensorflow==1.15
I tried a lot of ways on the internet to find a way to solve it! My information Tensorflow and computer
Name: tensorflow Version: 2.8.0 Summary: TensorFlow is an open source machine learning framework for everyone. Home-page: https://www.tensorflow.org/ Author: Google Inc. Author-email: packages@tensorflow.org License: Apache 2.0 Location: c:\users\admin\appdata\roaming\python\python39\site-packages Requires: absl-py, astunparse, flatbuffers, gast, google-pasta, grpcio, h5py, keras, keras-preprocessing, libclang, numpy, opt-einsum, protobuf, setuptools, six, tensorboard, tensorflow-io-gcs-filesystem, termcolor, tf-estimator-nightly, typing-extensions, wrapt
Python 3.9.7
Computer information:
Intel(R) Core(TM) i7-8750H CPU Processor @ 2.20 GHz 2.21 GHz Installed RAM 16.0 GB (15.9 GB usable) Device ID 26A28871-033A-43F4-9933-B82BE00E75C3 Product ID 00330-80000-00000-AA214 System type 64 bit Operating system, processor based on x64 Pen and apps No pen or touchpad for this monitor
Thank you for your help 🥰🥰
...ANSWER
Answered 2022-Feb-09 at 09:43You probably have CUDA11 install on your system. Tensorflow 1.X can't run on CUDA 11 unless you use Docker and Nvidia Container Toolkit.
I recommend you to use a Docker container provided by Tensorflow with the required version.
QUESTION
I cannot find a way to pip install the following Python modules without compatibility issues (from a requirements.txt file, on Red Hat Enterprise Linux release 8.2):
...ANSWER
Answered 2022-Feb-09 at 08:11The problem was caused by jupyter/tensorflow being loaded in the background. The following solved the issue:
QUESTION
I have pretrained model for object detection (Google Colab + TensorFlow) inside Google Colab and I run it two-three times per week for new images I have and everything was fine for the last year till this week. Now when I try to run model I have this message:
...ANSWER
Answered 2022-Feb-07 at 09:19It happened the same to me last friday. I think it has something to do with Cuda instalation in Google Colab but I don't know exactly the reason
QUESTION
I have tried the similar problems' solutions on here but none seem to work. It seems that I get a memory error when installing tensorflow from requirements.txt. Does anyone know of a workaround? I believe that installing with --no-cache-dir would fix it but I can't figure out how to get EB to do that. Thank you.
Logs:
...ANSWER
Answered 2022-Feb-05 at 22:37The error says MemoryError. You must upgrade your ec2 instance to something with more memory. tensorflow is very memory hungry application.
QUESTION
I'm working with machine learning on gcloud using SDK on my local terminal.
I'm running the following command:
...ANSWER
Answered 2022-Feb-02 at 18:42In the end it was a simple error, I just needed to change the comand line on my terminal.
QUESTION
I can't find the proper way to add dependencies to my Azure Container Instance for ML Inference.
I basically started by following this tutorial : Train and deploy an image classification model with an example Jupyter Notebook
It works fine.
Now I want to deploy my trained TensorFlow model for inference. I tried many ways, but I was never able to add python dependencies to the Environment.
From the TensorFlow curated environmentUsing AzureML-tensorflow-2.4-ubuntu18.04-py37-cpu-inference :
...ANSWER
Answered 2022-Jan-24 at 12:45If you want to create a custom environment you can use the below code to set the env configuration.
Creating the enviromentmyenv = Environment(name="Environment")
myenv.docker.enabled = True
myenv.python.conda_dependencies = CondaDependencies.create(conda_packages = ['numpy','scikit-learn','pip','pandas'], pip_packages = ['azureml-defaults~= 1.34.0','azureml','azureml-core~= 1.34.0',"azureml-sdk",'inference-schema','azureml-telemetry~= 1.34.0','azureml- train-automl~= 1.34.0','azure-ml-api-sdk','python-dotenv','azureml-contrib-server','azureml-inference-server-http'])
QUESTION
I installed tensorflow with pip. When I check my tensorflow version inside the conda environment it shows as 2.7.0 as I want it to be. However when I check it inside python on the same environment, I get 2.6.0. How can I change this so that in both cases I get 2.7.0? I already tried uninstalling and reinstalling tensorflow inside the environment and upgrading it with pip.
...ANSWER
Answered 2022-Jan-12 at 23:24I don't know exactly what the problem was but I created a new environment directly installing tensorflow during creation and inside that environment it still used 2.6.0 with python. Then I forced install 2.7.0 and it magically worked!
Here's a list of the commands I used:
QUESTION
There are multiple pages (like this and this) that present examples about the effect of channel_shift_range in images. At first glance, it appears as if the images have only had a change in brightness applied.
This issue has multiple comments mentioning this observation. So, if channel_shift_range and brightness_range do the same, why do they both exist?
ANSWER
Answered 2021-Dec-31 at 04:47Channel shift and Brightness change are completely different.
Channel Shift: Channel shift changes the color saturation level(eg. light Red/dark red) of pixels by changing the [R,G,B] channels of the input image. Channel shift is used to introduce the color augmentation in the dataset so as to make the model learn color based features irrespective of its saturation value. Below is the example of Channel shift from mentioned the article: In the above image, if you observe carefully, objects(specially cloud region) are still clearly visible and distinguishable from their neighboring regions even after channel shift augmentation.
Brightness change: Brightness level of the image explains the light intensity throughout the image and used to add under exposure and over exposure augmentation in the dataset. Below is the example of Brightness augmentation: In the above image, at low brightness value objects(eg. clouds) have lost their visibility due to low light intensity level.
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Install keras-preprocessing
You can use keras-preprocessing 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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