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: tensorflow
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
How many epochs does scikit learn use when cross validating?
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
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
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 get the predicted label from a Convolution Neural Net in image classification
I have built a CIFAR-10 image classification model with Convolution Neural Net or CNNs. The model fully completed and has got around 59% accuracy, but my problem is that how to get the predicted label from the model. it can predict these classes(10): What I’m trying to say is that, for example we give the model a image of a
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