I am trying to create a dense neural network where my input is a sparse 3d matrix. When converted to a dense matrix the shape is (2, None, n) (where n is a numb
The input shape in the first Conv2D layer is supposed to be (100, 100, 1) however the output is (None, 98, 98, 200). I understand what 200 and None determine bu
I'm following some lectures from lynda.com about deep learning using Keras-TensorFlow in a PyCharmCE enviroment and they didn't have this problem. I get this er
I want to predict the center of the pupil from an image. so I used a CNN with 3 Dence layer. so the input is an image and the output is a coordinate (X,Y). my m
How do I use keras function fit_generator() to train and simultaneously save the model weights with lowest validation loss?
Recently I tried to convert mask rcnn in this repository from tensorflow 1 to tensorflow 2. After re-writing the codes and when I run sample "shape" and execute
I am using the headsegmentation dataset. A single mask looks like this All mask images are a single channel. This is my code: image_size = 512 batch = 4 labels
I have a Mac with an M1 Pro chip. I was able to install keras/tensorflow with tensorflow-metal PluggableDevice. My image classification model runs smoothly on m
So basically, I am fairly new to programming and using python. I am trying to build an ANN model for which I have to use Tensor flow, Theano and Keras library.
I have a bunch of poor quality photos that I extracted from a pdf. Somebody I know has the good quality photo's somewhere on her computer(Mac), but it's my unde
I have an issue with tf.callbacks.ModelChekpoint. As you can see in my log file, the warning comes always before the last iteration where the val_acc is calcula
I am following quite closely the Seq2seq for translation tutorial here https://www.tensorflow.org/addons/tutorials/networks_seq2seq_nmt#define_the_optimizer_and
I am trying to implement the Keras libraries for Convolutional Neural Networks on my Spyder IDE using Anaconda as such: from keras.models import Sequential
A newbie for machine learning here. I'm now training a fairly easy model from tutorial using the dataset fashion_mnist on Win10. However, the training process t
I have a text A and a text B. I wish to find the percentage of words in text B (counting all occurrences) not present in the vocabulary (i.e., the list of all u
I train the following model based on GRU, note that I am passing the argument stateful=True to the GRU builder. class LearningToSurpriseModel(tf.keras.Model):
I train the following model based on GRU, note that I am passing the argument stateful=True to the GRU builder. class LearningToSurpriseModel(tf.keras.Model):
I wish to experiement with noisy GRU states instead of resetting them to zero for each batch. I try below an implementation. My initial code was resetting initi
I'm trying to Implement Inception_resnet_v2 inside Faster-RCNN instead of using ResNet50. but when I try to run the code I got this TypeError: TypeError: Inputs
I'm trying to re-implement the text summarization tutorial here. The tutorial employs the Attention Layer proposed for Keras on GitHub (which does not come with