I’m trying to use multiple inputs in custom layers in Tensorflow-Keras. Usage can be anything, right now it is defined as multiplying the mask with the image. I’ve search SO and the only answer I could find was for TF 1.x so it didn’t do any good. Answer EDIT: Since TensorFlow v2.3/2.4, the contract is to use a list of
Tag: tensorflow
How to convert a CoreML Model to a TensorFlow Model?
I read that it is possible to convert a TensorFlow Model (.pb) file to CoreML (.mlmodel) to run it on iOS platforms. But I have some .mlmodel files that I want to run on tensorflow. So I want to know if there is any way I could convert them to .pb files or any other way I could use them
CuDNN crash in TF 2.x after many epochs of training
I’m currently becoming more and more desperate concerning my tensorflow project. It took many hours installing tensorflow until I figured out that PyCharm, Python 3.7 and TF 2.x are somehow not compatible. Now it is running, but I get a really unspecific CuDNN error after many epochs of training. Do you know if my code is wrong or if there
In Keras, can I use an arbitrary algorithm as a loss function for a network?
I has been trying to understand this machine learning problem for many days now and it really confuses me, I need some help. I am trying to train a neural network whose input is an image, and which generates another image as output (it is not a very large image, it is 8×8 pixels). And I have an arbitrary fancy_algorithm()
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
Can CNN do better than pretrained CNN?
With all I know. pretrained CNN can do way better than CNN. I have a dataset of 855 images. I have applied CNN and got 94% accuracy.Then I applied Pretrained model (VGG16, ResNet50, Inception_V3, MobileNet)also with fine tuning but still i got highest 60% and two of them are doing very bad on classification. Can CNN really do better than
How to reproduce the Bottleneck Blocks in Mobilenet V3 with Keras API?
Using Keras API, I am trying to write the MobilenetV3 as explained in this article: https://arxiv.org/pdf/1905.02244.pdf with the architecture as described in this picture: For that, I need to implement the bottloneck_blocks from the previous article https://arxiv.org/pdf/1801.04381.pdf. See image for architecture: I managed to glue together the Initial and final Conv layers: Where the bottleneck_block is given in the next
coursera assignment python file compiling error
i have a coursera assignment in jupyter notebook the problem is that in jupyter it runs correctly but when i submit it fail and shows this error : Can’t compile the student’s code. invalid syntax (student_solution.py, line 23) the task is : In this exercise you’ll try to build a neural network that predicts the price of a house according
Implementing a minimal LSTMCell in Keras using RNN and Layer classes
I am trying to implement a simple LSTMCell without the “fancy kwargs” defaultly implemented in the tf.keras.layers.LSTMCell class, following a schematic model like this. It doesn’t really have a direct purpose, I would just like to practice implementing a more complex RNNCell than the one described here in the Examples section. My code is the following: However, when I tried
Can’t pip install Tensorflow ‘msvcp140_1.dll’ missing
I am currently trying to pip install tensorflow, which works but after I install it, and then import it into my python module via import tensorflow as tf I get following error message: I installed the msvcp140_1.dll and put it into C:UsersUserAppDataLocalProgramsPythonPython37 which is contained in my path environment variable. As you can see I am using Python 3.7 as