Tag: deep-learning

ValueError when using vae.fit in Keras

deep-learning keras machine-learning python tensorflow

I am creating a vae model over the mnist dataset with hopes of plotting the loss function against the epochs. However I am experiencing some issues and was unable to find a solution online. During my imports I have the following imports (just to give some context to the code): I have also created a function called bn for my

Neural Network loss is significantly changing for same set of weights – Keras

deep-learning keras neural-network python tensorflow

I use pre-initialized weights as initial weights of the neural network, but the loss value keeps changing every time I train the model. If the initial weights are the same, then the model should predict exactly the same value every time I train it. But the mse keeps changing. Is there anything that I am missing? Answer You have all

Building wheel for neural-renderer-pytorch (setup.py) …installing multiperson and neural mesh renderer doesn’t work for pytorch 1.6

computer-vision deep-learning pose-estimation python pytorch

I am trying to install a github repo named multiperson for PyTorch 1.6 and I get the following error. How can I make it work for PyTorch 1.6? I have: Answer You would need to change all AT_CHECK in neural mesh renderer to TORCH_CHECK

ValueError: You have to specify either decoder_input_ids or decoder_inputs_embeds

deep-learning huggingface-transformers python torchscript

Trying to convert a question-generation t5 model to torchscript model, while doing that Running into this error ValueError: You have to specify either decoder_input_ids or decoder_inputs_embeds here’s the code that I ran on colab. got this error how to resolve this issue? or is there a better way for converting the t5 model to torchscript. thank you. Answer Update: refer

Tensorflow 2.3, Tensorflow dataset, TypeError: () takes 1 positional argument but 4 were given

deep-learning python tensorflow tensorflow-datasets tensorflow2.0

I use tf.data.TextLineDataset to read 4 large files and I use tf.data.Dataset.zip to zip these 4 files and create “dataset”. However, I can not pass “dataset” to dataset.map to use tf.compat.v1.string_split and split with t separator and finally use batch, prefetch and finally feed into my model. This is my code: This is error message: What should I do? Answer

Ensemble with voting in deep learning models

deep-learning keras machine-learning python tensorflow

I am working on a multimodal deep learning classifiers with RGB-D images. i have developed two seperate models for each case. The first one is a LSTM with CNN in the begining for the RGB images with shape (3046,200,200,3) , and the second one is an LSTM for the depth images with shape (3046,200,200) . I’m trying to figure out

Errors in installing keras using pip?

deep-learning h5py keras pip python

I am trying to install Keras library using pip in windows 10. I have all the requirements installed, python>=3.8, pip>=20.0.0, NumPy, pandas, matplotlib, virtualenv. But I’m getting the following error. I thought this error was caused due to improper installation of h5py & hdf5. I tried installing them using pip install h5py but then I encountered the following error: Please

My deep learning model is not training. How do I make it train?

conv-neural-network deep-learning keras python tensorflow

I’m fairly new to Keras, please excuse me if I made a fundamental error. So, my model has 3 Convolutional (2D) layers and 4 Dense Layers, interspersed with Dropout Layers. I am trying to train a Regression Model using images. X_train.shape = (5164, 160, 320, 3) y_train.shape = (5164) When I try to run this model, the training loss decreases

In an image recognition task how to deal with unexpected images [closed]

deep-learning keras machine-learning python tensorflow

Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers. This question does not appear to be about programming within the scope defined in the help center. Closed 2 years ago. Improve this question I’m trying to develop an image classifier with Keras, I followed the tutorial on the page: https://www.tensorflow.org/tutorials/images/classification?hl=en Still using the flower

How many epochs does scikit learn use when cross validating?

deep-learning python scikit-learn tensorflow

I’m doing some model cross validation with scikit learn in time series data where a Multi Layer Perceptron is trained with Keras. (We are able to use cross_val_score from scikit learn thanks to the keras wrapper). Basically using: The issue is I don’t understand how many epochs its using on each training. Let me explain with an example. Assume X_train