I have a keras model, which takes a 10x10x1 array as input. For example: array = np.array([ [[0],[0],[0],[0],[0],[0],[0],[0],[0],[0]], [[0],[0],[0],[0],[0],[0],
I am trying to read and decode tiff images in tensorflow. I am using tensrflow_io package as follows, I am getting this error that I cant figure out. import ten
I trained a deeplearning model (EfficientnetB0) and now using OpenCV, I want to make real time predictions on the model. But I am unable to do so without creati
First of all, I would like to say that this is my first question in stackOverflow, so I hope that the question as a whole respects the rules. I realize that the
I am following the Tensorflow guide on Functions here, and based on my understanding, TF will trace and create one graph for each call to a function with a dist
Is there a possible way for me to resize or change the way the results are being displayed for my object detection? Any help would be greatly appreciated!
I would like to group rows in a tensorflow dataset by a key and select top k rows in each group by some value. This is easily doable ex. in Pandas or SQL, but n
I defined the following model, which has two distinct outputs: input_layer = keras.layers.Input(shape = (1, 20), name = "input_features") # Shared layers hidde
Manipulating tf.data.Dataset I get a behavior, I am not able to understand the origin. I am manipulating a tf.data.Dataset a simple integer buffer where I want
usage: generate_tfrecord.py [-h] [-i IMAGEDIR] [-o OUTPUTDIR] [-r RATIO] [-x] generate_tfrecord.py: error: unrecognized arguments: /content/training_demo/images
My Code : h_table = tf.lookup.StaticHashTable( initializer=tf.lookup.KeyValueTensorInitializer( keys=[0, 1, 2, 3, 4, 5], values=[12.3,
I'm trying to use cppflow library in windows 10 x64 machine in VS2019 C++. I want to inference my model for batch of images (vector <cv::Mat> ). I write a
I am trying to create my labelmap.pbtxt, but the file is not created. Here's the code Train_Annotations_Path = "C:/Users/JAAD_dataset/Workspace/annotations/Anno
I'm using TensorFlow training a deep learning model and the model is successfully trained however at the end it returns this error message to me: Exception igno
I have codes in the following, train_datagen = ImageDataGenerator( rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal
This is probably going to be a stupid question but I am new to deep learning and TensorFlow. Here I have converted my deep learning model to TF-lite, after that
I am using tf.gradienttape for model training and it is successful to save checkpoints for every epoch. with train_summary_writer.as_default(): with tf.summ
I am trying to create a Custom PyEnvironment for making an agent learn the optimum hour to send the notification to the users, based on the rewards received by
I try to use Functional API for my model, but i don't understand why i have error: ValueError: Shapes (128, 100) and (128, 100, 139) are incompatible My code:
I am new to AWS Lambda and running a tensorflow model in AWS Lambda. Now tensorflow 1.0.0 is the one that fits into the 50Mb limit but since tensorflow 2.0 is