This program works on Unix and I'm trying to transition it to windows. It uses multiprocessing and I understand it's an issue with being forced to use spawning
This program works on Unix and I'm trying to transition it to windows. It uses multiprocessing and I understand it's an issue with being forced to use spawning
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 know this problem has been answered previously in the link below,but it does not apply to my situation.(Tensorflow - ValueError: Failed to convert a NumPy arr
I'm learning batchnormalisation and dropout. Saw this https://www.kaggle.com/ryanholbrook/dropout-and-batch-normalization. The model model = keras.Sequential([
I am following an online course through linkedin regrading the Building of models through Keras. This is my code. (This is claimed to work) import pandas as p
I am new to AWS Lambda and running a tensorflow model in AWS Lambda. Now tensorflow 1.0.0 is the one that fits into the 50Mb limit but since tensorflow 2.0 is
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 have this error RuntimeError: MetaGraphDef associated with tags 'serve' could not be found in SavedModel. To inspect available tag-sets in the SavedModel, pl
When trying to use Tensorflow (gpu), it won't run because of this : Could not load library cudnn_cnn_infer64_8.dll. Error code 193 Please make sure cudnn_cnn_i
PLEASE NOTE: I have tried other solutions accross the web and didnt find the working result. I am detecting objects from live feed using tensorflow object detec
QUESTION: I'm new to the world of computer vision and this is my second project with it. I am running an edited version of the Matterport Mask RCNN that runs wi
I just implemented a LSTM, but I'm not sure if I interpreted the structure right. is in this context testPredict = model.predict(Xtest) the last value of the se
I have selected this dataset: https://www.kaggle.com/karangadiya/fifa19 Now, I would like to convert this CSV file into the federated dataset to fit in the mod
Coming from recommender systems, precision@k is a popular metric. precision@k = number of relevant predictions in top k / k On the tensorflow docs for tf.kera
In version 0.11.0 of Tensorflow Probability, I can define a TransformedDistribution as follows, indicating event and batch shape: mvn = tfd.TransformedDistribut
In version 0.11.0 of Tensorflow Probability, I can define a TransformedDistribution as follows, indicating event and batch shape: mvn = tfd.TransformedDistribut
I'm trying to modify a jupyter notebook to run on colab. It's from a somewhat older repo with known compatibility issues for tensorflow/keras versions after ~2.
I am using Docker and Docker-Compose on Ubuntu 20. The application I am deploying on container is using Tensorflow. Docker-Compose build is able to be executed
The code is like this: import tensorflow as tf from keras_contrib.layers import CRF from tensorflow import keras def create_model(max_seq_len, adapter_size=64