I wasn't expecting this to happen. The relevant code pieces are: import os import tensorflow as tf os.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices' .
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 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 try to use Functional API for my model, but i don't understand why i have error: ValueError: Shapes (128, 100) and (128, 100, 139) are incompatible My code:
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
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
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
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
I created a network, but got the error: AttributeError: in user code: C:\Users\LocalAdmin\.conda\envs\newenvt\lib\site-packages\keras_contrib\metrics\crf_accura
What is the tf.keras equivalent of encoder.get_feature_names found in sklearn? As shown shown in this SO question Need this to get all the one-hot encoded featu
If I freeze my base_model with trainable=false, I get strange numbers with trainable_weights. Before freezing my model has 162 trainable_weights. After freezin
If I freeze my base_model with trainable=false, I get strange numbers with trainable_weights. Before freezing my model has 162 trainable_weights. After freezin
I am following this course : TensorFlow Developer Certificate in 2022: Zero to Mastery This is the following code : # Set random seed tf.random.set_seed(42) #
I'm trying to use a modified version of this custom loss and I'm getting the error below InvalidArgumentError: The second input must be a scalar, but it has sh
I want to use the Segmentation_Models UNet (with ResNet34 Backbone) for uncertainty estimation, so i want to add some Dropout Layers into the upsampling part. T
I am training a CNN with an dataset of images that consists of 2410 RGB images and belongs to two categories, i.e., crops and another is grass. After training t