Building a tflite model for multi class classification

» android » Building a tflite model for multi class classification

By : mlinfoot

Date : November 28 2020, 12:01 PM

will help you You can easily train a Convolutional Neural Network ( CNN ) using TensorFlow ( specifically using Keras ). There are tons of examples on the internet. See here and here.
Next, we convert the Keras saved model ( .h5 file ) to a .tflite file using tf.lite.TFLiteConverter,

code :

import tensorflow as tf

converter = tf.lite.TFLiteConverter.from_keras_model_file("keras_model.h5")
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)

private float[][][][] convertImageToFloatArray ( Bitmap image ) {
   float[][][][] imageArray = new   float[1][modelInputDim][modelInputDim][1] ;
   for ( int x = 0 ; x < modelInputDim ; x ++ ) {
       for ( int y = 0 ; y < modelInputDim ; y ++ ) {
           float R = ( float )Color.red( image.getPixel( x , y ) );
           float G = ( float )Color.green( image.getPixel( x , y ) );
           float B = ( float )Color.blue( image.getPixel( x , y ) );
           double grayscalePixel = (( 0.3 * R ) + ( 0.59 * G ) + ( 0.11 * B )) / 255;
           imageArray[0][x][y][0] = (float)grayscalePixel ;
       }
   }
   return imageArray ;
}

private int modelInputDim = 28 ;
private int outputDim = 3 ;

private float[] performInference(Bitmap frame , RectF cropImageRectF ) {
   Bitmap croppedBitmap = getCroppedBitmap( frame , cropImageRectF ) ;
   Bitmap croppedFrame = resizeBitmap( croppedBitmap );
   float[][][][] imageArray = convertImageToFloatArray( croppedFrame ) ;
   float[][] outputArray = new float[1][outputDim] ;
   interpreter.run( imageArray , outputArray ) ;
   return outputArray[0] ;
}

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Using OneVsRestClassifier from sklearn to tune a customized binary classification into a multi-class classification

By : Meeta Pal

Date : March 29 2020, 07:55 AM

it helps some times I have binary classification method name FMclassifier I need to apply it on a multi-class classification problem, so far I know it is possible to use some estimators to turn a binary classifier or a regressor into a multiclass classifier. I was wondering if this goal can be reached using OneVsRestClassifier in sklearn ? If so, I need to know how can I apply it in my code? is something like the following code a right way? , Yes, it would be something like

code :

classifier = OneVsRestClassifier(MyClassifier(param1 = A, param2 = B))
classifier.fit(X_train, Y)
predicted = classifier.predict(X_test)

.tflite model (converted from keras .h5 model) always predicts the same class with same probability

By : ramki.4a3

Date : March 29 2020, 07:55 AM

like below fixes the issue In your training code, you normalize the images to the range [0..1], which is specified by the following lines:

code :

train_datagen = ImageDataGenerator(rescale=1. / 255, ...)
test_datagen = ImageDataGenerator(rescale=1. / 255)

input_mean = 127.5
input_std = 127.5
input_img = (np.float32(input_img) - input_mean) / input_std

input_mean = 0.
input_std = 255.

Model() got multiple values for argument 'nr_class' - SpaCy multi-classification model (BERT integration)

By : user3086664

Date : December 27 2020, 04:54 PM

Hope this helps This is a regression in the most recent version we released of spacy-pytorch-transformers. Sorry about this!
The root cause is, this is another case of the evils of **kwargs. I'm looking forward to refining the spaCy API to prevent these issues in future.

Scikit multi-class classification metrics, classification report

By : Wycliff pwadi

Date : March 29 2020, 07:55 AM

This might help you It seems like you have to run your classification report with the binarized labels:

building a u-net model for multi-class semantic segmenation

By : Brian Steuber

Date : March 29 2020, 07:55 AM

I wish did fix the issue. There doesn't seem to be anything that wrong in your model.
Softmax is ok, as it defaults to the last axis, and you're clearly using 'channels_last' as config. So it's ok.