Category: Misc

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.

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

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 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 🙂

I have been trying to develop my machine learning model dealing with Nifti files. I originally just loaded them into a numpy array but after augmentation the amount of data was too large for the RAM.

I discovered that one should make use of things such as generators / tools provided by tf.Data.

As such I attempted the following.

def load_images(imagePath): image = nib.load(imagePath) image = image.get_fdata() image = tf.image.per_image_standardization(image) label = (int)(imagePath.split('Grade')[1][0]) - 1 return (image, label) dataset = tf.data.Dataset.from_tensor_slices(all_paths) dataset = (dataset .shuffle(1024) .map(load_images, num_parallel_calls=AUTOTUNE) .cache() .repeat() .batch(64) .prefetch(AUTOTUNE) ) 

Hopefully the code is straightforward but if anything needs further clarification please do ask. Originally the first two lines of load_images() were it’s own function but I tried it like this to try and resolve the issue.

The issue is that I am getting the following error at the map line:

TypeError: in user code: File "<ipython-input-64-56e9744da5d3>", line 5, in load_images * image = nib.load(imagePath) File "/usr/local/lib/python3.7/dist-packages/nibabel/loadsave.py", line 42, in load * stat_result = os.stat(filename) TypeError: stat: path should be string, bytes, os.PathLike or integer, not Tensor 

I don’t think it likes me using nibabelfunctions in the mapping function but I cannot think of any other way to do it. I tried following the answer here but this just gave me another error about using tf.function