I would like to do a test about training a machine learning model on EC2 instance with only CPUs from jupyter notebook. The code is tensorflow 2.8. Based on the
RGB images can be stored in memory in different ways, e.g. see link below planar and interleaved: http://avitevet.com/uncategorized/when-to-use-it-interleaved-v
I need to use the smartwatch_gestures from Tensorflow datasets and here is my code: pip install --upgrade tfds-nightly import tensorflow_datasets as tfds impor
I am training convolutional autoencoder and I have this code for loading data (images): train_ds = tf.keras.preprocessing.image_dataset_from_directory( 'pat
I was trying to train an autoencoder on image patches. My training data consists of single-channel images loaded into a numpy array with shape [10000, 256, 512,
In TensorFlow examples, I can see URLs to download the csv format of the dataset. For example, Iris- https://storage.googleapis.com/download.tensorflow.org/data
First of all, I would like to say that this is my first question in stackOverflow, so I hope that the question as a whole respects the rules. I realize that the
I am trying to load a pandas dataframe into a tensor Dataset. The columns are text[string] and labels[a list in string format] A row would look something like:
My goal is to use the following dataset from tensorflow-datasets for Machine Learning https://www.tensorflow.org/datasets/catalog/wider_face import tensorflow a
Manipulating tf.data.Dataset I get a behavior, I am not able to understand the origin. I am manipulating a tf.data.Dataset a simple integer buffer where I want
I'm working on this project where all the data comes preprocessed and ready as a tensorflow datasets which looks like this: <MapDataset shapes: {input_ids: (
I've been trying to generate a custom dataset from two arrays. One with the shape (128,128,6) (satellite data with 6 channels), and the other with the shape (12
I am following this tutorial to train my own models. https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/ I followed all the steps exactly
I know dataset has output_shapes, but it shows like below: data_set: DatasetV1Adapter shapes: {item_id_hist: (?, ?), tags: (?, ?), client_platform: (?,), en
I want to use tf.data.Dataset.list_files function to feed my datasets. But because the file is not image, I need to load it manually. The problem is tf.data.Dat
I am following the Google Machine Learning Intensive Course. But it uses version 1.x of TensorFlow, so I was planning to change the exercises to be able to run
I am following the Google Machine Learning Intensive Course. But it uses version 1.x of TensorFlow, so I was planning to change the exercises to be able to run
I have a simple code, which DOES work, for training a Keras model in Tensorflow using numpy arrays as features and labels. If I then wrap these numpy arrays usi
I have a large list of numpy arrays that I want to feed into a TensorFlow model. I can not concatenate the lists into one due to RAM memory issues. Below, I hav
I have list of labels corresponding numbers of files in directory example: [1,2,3] train_ds = tf.keras.utils.image_dataset_from_directory( train_path, label