I keep getting F tensorflow/core/platform/default/env.cc:73] 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
Tag: conv-neural-network
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
Tensorflow – Dense and Convolutional layers connection
I’m new to Deep Learning and I can’t find anywhere how to do the bottleneck in my AE with convolutional and dense layers. The code below is the specific part where I’m struggling: I tried some solutions, like flatten and reshape, but nothing seems to work here. The point is that I need the latent space to be a dense
Due to huge images. Can we generalize flow_from_directory method for regression problem (CNN): input image and output is x,y (float)
Data is in a CSV file which includes image path and target (x and y). where x and y belong to [-1 to 1] after scale (in keras becasue there are so many image . I can not load all in X_train like normal). Thank you so much for help! data in csv file Answer I will write here as
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: tuner_cnn.search(datagen.flow(X_trainRusReshaped,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
mat1 and mat2 shapes cannot be multiplied (128×4 and 128×64)
Could not find out why the mat1 from the convolutional network is 128×4 and not 4×128. The following is the convolutional network used: The model training code is as follows: The error log shown is: mat1 should be the output of the convolutional network after it is flattened, and mat2 is the linear network following it. Appreciate any help. Thanks!
Data type preference for training CNN?
I originally was using input data of int8 type ranging from 0-255 before learning that standardizing and normalizing should increase learning speeds and accuracy. I attempted both, with and without a mean of zero, and none of these methods improved learning speed or accuracy for my model relative to 0-255, int8 approach. I’m just wondering whether training with, for example,
Value error in convolutional neural network due to data shape
I am trying to predict the of number peaks in time series data by using a CNN and keep on getting a data shape error. My data looks as follows: X = list of 520 lists (each is a time series) of various lengths (shortest = 137 elements, longest = 2297 elements) y = list with 520 elements, each being
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