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Tag: conv-neural-network

Keras and Tensorflow OS resource requirement

I keep getting F tensorflow/core/platform/default/] Check failed: ret == 0 (11 vs. 0)Thread tf_data_private_threadpool creation via pthread_create() failed. errors during training, although the machine is quite powerful: altogether 64 logical cores ulimit -s gives 32768, ulimit -u gives 1030608 I want to train the following network with a bunch of online generated 512*512 grayscale images along with two additional parameters

How can I print the training and validation graphs, and training and validation loss graphs?

I need to plot the training and validation graphs, and trarining and validation loss for my model. Answer history object contains both accuracy and loss for both the training as well as the validation set. We can use matplotlib to plot from that. In these plots x-axis is no_of_epochs and the y-axis is accuracy and loss value. Below is one

Keras tuner Bayesian Optmization graph error

I am trying to optimize a convolutional neural network with Bayesian Optimization algorithm provided in keras tuner library. When I perform the line:,Y_trainRusHot), epochs=50, batch_size=256) I encounter this error: InvalidArgumentError: Graph execution error One-Hot-Encode y_train and y_test as the following: I defined my model builder like that: perform the tuner search: I also tried to do: But it does

Synchronization for video/audio/text message in flask web app framework for facial emotion recognition

I have trained a CNN model in Google Colab for facial expression detection with the FER2013 dataset containing 7 emotion classes (‘Angry’, ‘Disgust’, ‘Fear’, ‘Happy’, ‘Sad’, ‘Surprise’, ‘Neutral’). Used flask framework to build a web application. OpenCV’s haarcascade_frontalface_default.xml is used to detect faces. With this I’m able to do real-time live streaming of the video using my laptop’s webcam and

How do I read two folders in a directory and combine them under one label using flow_from_directory?

Tensorflow/Keras I want to classify images into either “Circle”, “Square” or “Triangle”. I have a directory containing 6 folders with each shape having a separate “shaded” or “unshaded” folder. How can I combine them into one category? For example: shaded and unshaded circles will be given a label “0” using flow_from_directory. I will then feed this into my CNN model