DataBloom

I am a beginner in the machine learning field and while learning the tensorflow introductory I understood that tensor is just nothing but a generalized name for quantities that require multiple variables (features) for it’s description.

Then there is this line -> Matrix and Vectors are the tensors with different rank. Matrix is 2d tensor and vector is 1 d tensor.

I then searched for the difference between Matrix and Vector and this text confused me

A vector is a matrix with just one row or column

And later in the tensor’s definition

A tensor is often thought of as a generalized matrix. That is, it could be a 1-D matrix (a vector is actually such a tensor)

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I am coming from the CS background where I have learnt that 1D is array 2D is matrix and we used to take 1D array like std::vector<float> height_vector

So what is difference between arr[10] and arr[1][10] or arr[10][1] ?

My keras model achieved a test accuracy of 98%. I ran model.predict on the test data within the same script and got pretty much the same results. I then tried model.save() and tf.keras.models.load_model() within the same script that I trained in and got the same results with model.predict. But when I load the model in a different script, and then do model.predict on the test data, accuracy is 0%. I have tensorflow 2.7.0. Has this happened for anyone else?

I’m building a tensorflow model which will have some variable sized inputs, with zero (or some other value) padding used to bring smaller inputs up to the standard input size. I also intend to use some sort of weight decay (L1/L2 regularization).

My concern is, that during training whenever padded input comes in, the weights leading out of the zero-inputs will continue to be decayed by whatever regularization I use. Ideally, I would like to disable my L1/L2 regularization on weights that have no gradient due to zero-inputs. Is there a way to get TF to do this? Disabling weight updates on those weights would also work.

If it helps, I can certainly pad with a value that doesn’t appear in the natural data anywhere, so that any occurrence with this value would indicate that weight updates should be masked. The layers will be convolutional.

Hi, I’m new using tensorflow and ML, and I have a question. How can I load a image folder with thousands of imagen with tensorflow? I know that tensorflow can load multiple folders in a directory or categorize data from multiple folders but I only have one folder with that images and I can’t found in the documentation of tensorflow how to load that single folder without convert in ZIP.

I’m sorry if this is a dumb question, but I don’t know how to solve this. Some people told me to load the data with system.os but the RAM don’t support that magnitude of images.

Thank you

I have a set of images, but I am not working on a classification problem like most tensorflow tutorials have. Rather, I am trying to have the model recognize/develop its own features without a validation set to label and categorize.

I don’t actually know what this type of model is called, so I am having a hard time finding tutorials.

My goal is to have a model that recognizes similarities in new photos and can select them them if they cross a threshold of similarity

I am new to using Machine Learning with Tensorflow in CNN. I have a problem, when I try to make a CNN they give me the images in a folder and the labels in a CSV and I don’t know how to categorize the images based on that CSV to then train the model with Tensorflow.

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Images are in a single training folder with the names a bit random.

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Labels are in CSV that contains two columns: Image name and the associated label.

I want to create a dataset for image recognition but most of the tutorials i could find where really old. Or i just didn’t understand them.

Does anyone have some code wich creates a dataset out of multiple dictionarys (with images in it of) and saves it in a file. (.pickle .npy etc.)

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thanks 🙂