I am wondering if there is an easy way of creating a way of triggering early stopping in Keras based on user input rather than monitorization of any particular
I'm starting to study the tensorflow with the image classification sample which is the first sample on the tensorflow official document. It creates the Keras Se
In the model I want to launch, I have some variables which have to be initialized with specific values. I currently store these variables into numpy arrays but
I have trained a model with keras and saved it, can I see what the computed metrics during training were, after I load back the mode with keras.models import lo
I've tried tensorflow on both cuda 7.5 and 8.0, w/o cudnn (my GPU is old, cudnn doesn't support it). When I execute device_lib.list_local_devices(), there is
I'm using the following generator: datagen = ImageDataGenerator( fill_mode='nearest', cval=0, rescale=1. / 255, rotation_range=90, width_sh
Below is the order of how I am going to present my problem: First I will show you the script .py that I am using to run the web app in a local host(flask app).
I am trying to build a CNN model to recognise human sketch using the TU-Berlin dataset. I downloaded the png zip file, imported the data to Google Colab and the
How can you write a python script to read Tensorboard log files, extracting the loss and accuracy and other numerical data, without launching the GUI tensorboar
I have a flask application that I would like to run it on an EC2 instance and TensorFlow is needed cause it is image classification. However, after the necessar
I am trying to learn and understand how to implement multiclass classification using ANN. In my case, I have 16 classes(0-15), and my label dataset contains one
I'm trying to train research model ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8 using the MultiWorkerMirroredStrategy (by setting --num_workers=2 in the invocation
I am trying to import import tensorflow.python.keras.applications but it gives the bellow error: ModuleNotFoundError: No module named 'tensorflow.python.keras.
I have a conda env that I build from a requirements.yml file that I obtained from a classmate so we could work on a project together. I tried installing matplot
I am using the Physics Informed Neural Networks (PINNs) methodology to solve non-linear PDEs in high dimension. Specifically, I am using this class https://git
I am training a U-Net in keras by minimizing the dice_loss function that is popularly used for this problem: adapted from here and here def dsc(y_true, y_pred)
I am new to Tensorflow and deep leaning. I am trying to see how the loss decreases over 10 epochs in my RNN model that I created to read a dataset from kaggle w
tf.unique currently only works on 1D tensors. How can I find unique values in a 2D tensor. ip=tf.constant([[1,2,1],[3,4,1],[5,6,1],[1,2,1]]) #op should be = [
tf.unique currently only works on 1D tensors. How can I find unique values in a 2D tensor. ip=tf.constant([[1,2,1],[3,4,1],[5,6,1],[1,2,1]]) #op should be = [
Hey everyone this is my first question post. If I do something wrong or u need more information please just tell me I will try to give my best. I tried to creat