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
Tag: keras
In an image recognition task how to deal with unexpected images [closed]
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
Unable to load numpy array into `model.fit`
i’m new to deep learning with Keras, so please inform me if i need to include more data in this post! So currently i have done some image augmentation to my training set for the MNIST dataset i had. So, i referred to this post here and i tried to save my augmented image models into the array. But when
How to append rank 1 tensors using only tensorflow operations?
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
Regarding the accuracy of the Siamese CNN
and the result of training is as follows The model is predicting with good accuracy, when i am comparing two dissimilar images. Further it is predicting really good with same class of images. But when I am comparing Image1 with image1 itself, it is predicting that they are similar only with the probability of 0.5. in other case if I
Can a neural network accept an object (i.e. not numerical nor string) as an input? [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 need to build a neural network accepting data from a particular .csv file where most columns’ type is object,
How to extract validation data from training data
I have following statement I would like to extract from following Dir Data, separate Validation Data Greetings DA Answer in the call to image_dataset_from_directory, set subset=’training for the train dataset and set it to ‘validation’ for the validation set as shown below
How to add an attention layer to LSTM autoencoder built as sequential keras model in python?
So I want to build an autoencoder model for sequence data. I have started to build a sequential keras model in python and now I want to add an attention layer in the middle, but have no idea how to approach this. My model so far: So far I have tried to add an attention function copied from here and
ImageDataGenerator() for CNN with input and output as an Image
I’m looking for a training map with something like this: Grayscale Image -> Coloured Image But the dataset can’t be loaded all to the ram as X and Y because of obvious reasons. I looked up the ImageDataGenerator() library, but it didn’t give me a clear answer as to make it work here. Summary: Input Shape = (2048, 2048, 1)
num_units in GRU and LSTM layers in keras Tensorflow 2 – confuse meaning
I know that this question raised many time, but I could not get a clear answer because there are different answers: In tf.keras.layers.LSTM tf.keras.layers.GRU layers there is a parameter called num_units. I saw a lot of questions over the internet about this parameter. and there is not clear answer for what this parameter mean expect for the obvious meaning which