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
I have Keras model: pre-trained CV model + a few added layers on top I would want to be able to do model.predict before model.fit Q: how do I instantiate model
I am new to tensorflow. i've tried to fit X and y both shape=8 float64 tensors X as feature set and y as target set. X = np.array([-7.0, -4.0, -1.0, 2.0, 5.0, 8
I want to load FaceNet in Keras but I am getting errors. the modal facenet_keras.h5 is ready but I can't load it. you can get facenet_keras.h5 from this link: h
Hello guys i am a biggner at computer vision and classification, i am trying to train a model using cnn method with tensorflow and keras, but i keep getting the
I want to create a sklearn pipeline that consists of two steps: Custom transformer function Keras classification model This is my data set (of course, I'm provi
After training a model using Google Colab, I downloaded it using the following command (inside Google Colab): model.save('model.h5') from google.colab import fi
I have a dozen pre-trained DNNs that I wish to add to a sklearn ensemble. The issue is that it seems I can not provide pre-trained models to KerasClassifier. cl
What I want to do is to add three external trainable parameters in the VAE network using the following function: def gmmpara_init(): theta_init = tf.Variab
I'm building a chain classifier for a multiclass problem that uses Keras binary Classifier model in a chain. I have 17 labels as classification target and datas
I'm trying to save a model which is a composite model of composite models. The first model is a sequential model of two sequential models. Both of the two sub-m
I'm creating an Ensemble of Vgg19, DenseNet, and EfficientNetB1. The code is as follows: IMAGE_SIZE = (224,224,3) import tensorflow as tf vgg19 = tf.keras.appl
I'm a beginner of deeplearning.I copied the code with python3 in colab. Code is from the book called"Book - Practical Deep Learning for Cloud, Mobile & Edge
I have a keras model with 5 outputs. My labels include 5 values to compare these to, but also 25 additional values representing a correlation matrix for the 5 v
I designed a CNN for a multitask classification in keras, where I have one input and two different class of classes in output. I compiled the model in this way