I have a subclassed model with some custom attributes like this: class MyModel(tf.keras.Model): def __init__(self, *args, my_var, **kwargs): super()
I have a subclassed model with some custom attributes like this: class MyModel(tf.keras.Model): def __init__(self, *args, my_var, **kwargs): super()
I would like to get to know the real sequence_length in Keras for a LSTM/RNN. Unfortunately, when I print the model I only get None all the time as a value. Her
I am trying to make a custom loss function where I perform an inverse fast Fourier transform to a set of data and then do the following calculations. When I run
I'm running a toy model for learning, on Ubuntu 21.10, in a conda environment that comprises python 3.74, keras 2.4.3 and talos 1.0, among many other packages.
so I have 2 images, img1 and img2 both with shape=(20,20), to which I expand_dims to (1,20,20) 1 being batch size and feed them to the network
I have a model that takes two inputs of the same shape (batch_size,512,512,1), and predict two masks each of shape (batch_size,512,512,1). dataset_input = tf.da
first 'im not a developer by trade, my developer is not available for health reasons but i have some experience in python/spacy development. I need some guidanc
I am trying to implement this model: https://github.com/abhishekkrthakur/is_that_a_duplicate_quora_question/blob/master/deepnet.py but it is from an older versi
I am trying to make a mixed dataset but I am struggling. I want to use an image and float value for the inputs. Then output a linear regression. I've tried rese
I have a script which goes through a simple 2d CNN and I'm trying to run through a range of different values for the number of layers and neurons per layer in m
please I'm trying to build an NLP classifier on top of BERT but I'm struggling with data imbalance. I'm looking for an implementation of weighted CategoricalCro
I am using ResNext architecture for classification. the training dataset contains approximately 31000 images distributed among 61 classes. And validation datase
I was training a network and I decided to add more data for training. my data set is selected from another data but both have (460,620,3) and Uint8 type. but wh
I have a code to forecast a time series using an attention mechanism. Here's what I've got so far, but I'm getting an error. def dot_product(x, kernel): if
I have defined few parameters in my config.yaml like as below. params: epochs: 10 batch_size: 128 num_classes: 10 loss_function: sparse_categorical_cros
Task: Mask RCNN train_shapes.ipynb tutorial. Training to segment different shapes in the artificially generated shapes dataset. Problem: Matterport's Mask RCNN
Is the GlobalAveragePooling1D Layer the same like calculating the mean with a custom Lambda Layer? The data is temporal, so x has shape (batch, time, features)
I am trying to load a pandas dataframe into a tensor Dataset. The columns are text[string] and labels[a list in string format] A row would look something like:
I am using a Keras network which I am calling predict() many times on a single input. A rough calculation based on the layers gives ~3Mops. Running on my CPU sh