Say I have two rank 1 tensors of different (important) length: Now I want to append y to the end of x to give me the tensor: But I can’t seem to figure out how. I will be doing this inside a function that I will decorate with tf.function, and it is my understanding that everything needs to be tensorflow
Tag: deep-learning
Confusion Matrix in transfer learning with keras
I was going to draw confusion matrix in my model and I used Transfer learning concept based on Deep Learning model. Confusion Matrix’s code Now below the shape of test_labels and Predictions are given, The above code is perfectly working but I saw error in below. So please concern below code, and here is the error, Note: This is value
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
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
Couldn’t open file yolov3_custom_last.weights when trying to run darknet detection
I’ve been trying to use YOLO (v3) to implement and train an object detection of Tank with OpenImage dataset. I have tried to get help from this tutorial and my code pretty much looks like it. Also I’m using Google Colab and Google Drive services. everything is going fine through my program. But I hit an error at the final
I keep getting ValueError: Shapes (10, 1) and (10, 3) are incompatible when training my model
Turning the number of inputs when I call makeModel from 3 to 1 allows the program to run without errors but no training actually happens and the accuracy doesn’t change. Answer LabelEncoder transforms the input to an array of encoded values. i.e if your input is [“paris”, “paris”, “tokyo”, “amsterdam”] then they can be encoded as [0, 0, 1, 2].
TF.Keras model.predict is slower than straight Numpy?
Thanks, everyone for trying to help me understand the issue below. I have updated the question and produced a CPU-only run and GPU-only of the run. In general, it also appears that in either case a direct numpy calculation hundreds of times faster than the model. predict(). Hopefully, this clarifies that this does not appear to be a CPU vs
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray). in trying to predict tesla stock
In the end you can see that i have tried converting this into a numpy array but I don’t understand why tensorflow dosen’t support it? I have looked at the other related pages but none seemed to help. Is there some other format i have to do to the data in order to properly fit in model? this is what
Does SHAP in Python support Keras or TensorFlow models while using DeepExplainer?
I am currently using SHAP Package to determine the feature contributions. I have used the approach for XGBoost and RandomForest and it worked really well. Since the data I am working on is a sequential data I tried using LSTM and CNN to train the model and then get the feature importance using the SHAP’s DeepExplainer; but it is continuously