I got an error ,IndexError: list index out of range. it worked on a other machine but after i transferred it to a other machine it doesn’t work anymore. Python: 3.8.5 tensorflow: 2.3.1 Traceback says: My code: I really cannot understand why such an error happens. What is wrong in my codes? How should I fix this? Answer Define the
Tag: tensorflow
AttributeError: partially initialized module ‘tensorflow’ has no attribute ‘config’ (most likely due to a circular import)
I keep on receiving this error: Here is my code: Here are my Tensorflow versions: tensorboard-2.3.0 tensorflow-2.3.1 tensorflow-estimator-2.3.0 tensorflow-gpu-2.3.1 I am on Python 3.8.0 Answer When python executes a script, it includes the script’s directory in the python path. This makes it easy for programmers to supply their own modules for the script without an explicit install. Also, python doesn’t
How to train my own image dataset for text recognition and create the trained model for use in OCR [closed]
Closed. This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 2 years ago. Improve this question I created image data set including 62992 images with 128x128px resolution that contains characters, numbers and symbols with four kinds
Error while running CNN for 1 dimensional data in R
I am trying to run 1 dimensional CNN in R using keras package. I am trying to create one-dimensional Convolutional Neural Network (CNN) architecture with the following specification But it is giving me following error Error in py_call_impl(callable, dots$args, dots$keywords) : ValueError: Negative dimension size caused by subtracting 4 from 1 for ‘conv1d_20/conv1d’ (op: ‘Conv2D’) with input shapes: [?,1,1,128], [1,4,128,256].
Using pretrained model with keras: AttributeError: ‘NoneType’ object has no attribute ‘shape’
I’m running a Keras Neural Network model for a binary classification of images. I use the first layer of a pretrained VGG16 model and i created the last fully connected layers from the tutorial: https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html With Tensorflow backend 2.3.1, Python 3.6, Keras 2.4.3 While i’m training my model (using presaved weights) with an ImageDataGenerator, this exception occurs: That’s my code
Model for measuring grammatical text quality [closed]
Closed. This question is opinion-based. It is not currently accepting answers. Want to improve this question? Update the question so it can be answered with facts and citations by editing this post. Closed 2 years ago. Improve this question I generate text via transformer models and I am looking for a way of measuring the grammatical text-quality. Like the text:
Tensor Flow Conv1D for binary classification CNN
I’m creating a Conv1D layer in a CNN for binary classification, and I’m quite new to Machine Learning and I need some help to figure out the correct values for Conv1D: I would like to know if there is a way to determine the “Right” hyper-parameters that will fit my dataset best. Answer For the completion, here is the documentation
Why is my deep learning model predicting very similar but wrong values
So I’ve done some really basic supervised learning in the past and decided to try predictive maintenance and because I am new to this subject I decided to watch some tutorials in the world wide web. After a couple of hours into it i came across this specific tutorial (link down below) in which it is used a dataset from
ValueError: Input 0 of layer sequential is incompatible with the layer: : expected min_ndim=4, found ndim=2. Full shape received: [None, 2584]
I’m working in a project that isolate vocal parts from an audio. I’m using the DSD100 dataset, but for doing tests I’m using the DSD100subset dataset from I only use the mixtures and the vocals. I’m basing this work on this article First I process the audios to extract a spectrogram and put it on a list, with all the
Easiest way to see the output of a hidden layer in Tensorflow/Keras?
I am working on a GAN and I’m trying to diagnose how any why mode collapse occurs. I want to be able to look “under the hood” and see what the outputs of various layers in the network look like for the last minibatch. I saw you can do something like model.layers[5].output, but this produces a tensor of shape [None,