DataBloom

Hi! I am making a drowsiness application and have to extract eye landmarks. Can anybody help how can extract them? Kindly Help! Thank You!

I’ve a piece of code that I believe was written in TF2, but based on a repo written in TF1.

I am trying to run it in TF 1.52

It expressively invokes a piece of LSTM code that causes an error (unknown parameter “scope”)

net = tf.keras.layers.LSTM(32, return_sequences=True, dropout=0.4, recurrent_dropout=0.4)(net, scope=’lstm1′, training=is_training)

net = tf.keras.layers.LSTM(32, dropout=0.4, recurrent_dropout=0.4)(net, scope=’lstm2′, training=is_training)

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All of the other layers have their scope parameter defined as part of a custom layer definition, (with tf.variable_scope(scope, reuse=reuse_weights) as sc )

Without the scope param in the LSTM layers, the kernel fails. I believe the problem is in the lack of a custom layer definition for the LSTM layers with the scope defined accordingly, but I’m not totally sure

Hi, I am doing optical character recognition on my own dataset, consisting of around 17k images of 11 classes (0-9 as well as $). I can train the model no problem, only 2 epochs for now as loss goes down very quickly and it works perfectly immediately after training. The issue is that I try and save the model, then try and load the model, and it is like I never loaded it at all. The classifications are terrible and it barely gets 1 or 2 of the 16 images used for inference testing (completely random).

I’m sure I am doing something wrong, but I just can’t figure out what.

I’ve been following [this](https://keras.io/examples/vision/captcha_ocr/) guide from keras, and [this](https://github.com/BenSisk/CSGO-OCR) is my GitHub repo with my specific code.

Question 1: As part of a project I trained a custom model from the tensorflow model Zoo found here:

https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md

Now I retrained this model from a checkpoint, so that it would look specifically at one category from the coco dataset. However, I’m wondering if the training was even needed, seeing as the model was initially trained on the coco dataset. So my question is, does retraining on the same dataset have advantages, when looking at one particular element of the dataset? (narrowing from 90 to 1 categories).

Question 2: To remedy this, I thought I might want to train a model from ‘scratch’. They provide a link in the above link to some untrained model pre-sets.

https://github.com/tensorflow/models/tree/master/research/object_detection/configs/tf2

However I noticed in the config files they link, it still says:

Trained on COCO17, initialized from Imagenet classification checkpoint

and the model config has an entry:

fine_tune_checkpoint: “PATH_TO_BE_CONFIGURED/mobilenet_v2.ckpt-1”

Can anyone explain what’s going on here and how a model can be trained ‘from scratch?’

I am doing a project classifying land cover types and I was wondering if/how you could do supervised classification. Supervised classification being the manual selection of pure pixels that are within a specific class, and tensor flow uses those values to identify the entirety of that class within a whole image.

An example would be selecting 20 groups of pixels that are all trees, 20 groups that are all grassland, and 20 pixels that are all water, and then the entire image is categorized into one of those three classes.

Thanks for the help!

Hello.

Im tring to export a model as TFLITE and I get this error.

The following operation(s) need TFLite custom op implementation(s): Custom ops: BitwiseAnd

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Do you have an idea how to import BitwiseAnd function to the model?

My code to export the model is this.

converter = tf.lite.TFLiteConverter.from_keras_model(model) converter.allow_custom_ops = True converter.experimental_new_converter =True tflite_model = converter.convert() with open('lp4.tflite', 'wb') as f: f.write(tflite_model) 

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Thanks for your help

This package makes it easy for us to create efficient image Dataset generator.

github link

Features

• Simple, Easy and Efficient image dataset creator for segmentation and classification models.
• Avoiding some limitations which cause performance bottleneck of learning, generate the Dataset by one-liner.
• Augment multiple input images and multiple label images with the same transformations.
• Adjusting sampling ratios from multiple tfrecord files are supported.

Install

python -m pip install git+https://github.com/piyop/tfaug

Supported Augmentations

• standardize
• resize
• random_rotation
• random_flip_left_right
• random_flip_up_down
• random_shift
• random_zoom
• random_shear
• random_brightness
• random_saturation
• random_hue
• random_contrast
• random_crop
• random_noise

Hi

I have the following kotlin code:

val mydataarray = Array(100) { FloatArray(200) } set_values_for_mydataarray(mydataarray) val inputFeature0 = TensorBuffer.createFixedSize(intArrayOf(1, 100, 200), DataType.FLOAT32) inputFeature0.loadArray(mydataarray) 

loadArray(mydataarray) is returning this error:

None of the following functions can be called with the arguments supplied:

public open fun loadArray(p0: FloatArray)

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I suppose the shape of mydataarray is wrong.

Do you know how it has to be shaped?

Or do you think the problem is another?

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Thanks

Hi. So I wanted to ask if it is possible to create a speech to text using a dialect in the Philippines. I would only be using simple words of the dialect.