I’m trying to train an LSTM classifier in TensorFlow. Here is a reproducible example Using BinaryAccuracy: Using Accuracy: I have used the ‘Accuracy’ metric for binary classification before, can someone explain why this is happening? Answer The metric is ‘accuracy’, not ‘Accuracy’.
Tag: tensorflow
Problems with importing Tensoflow in Jupyter. DLL load failed. Specific module failed to load
I opened up a Jupyter notebook and tried importing tensorflow as tf and got the following error message And here’s the message I received I tried installing it with the following commands But I’m still getting the same error message. I’m using windows 10 if that helps. Oh I also checked in my command line to see if it was
Predicting in parallel using concurrent.futures of tensorflow.keras models
I am trying to implement some parallel jobs using concurrent.futures. Each worker requires a copy of a TensorFlow model and some data. I implemented it in the following way (MWE) simple_model() creates the model. clone_model clones a TensorFlow model. work represents an MWE of possible work. worker assigns the work in parallel. This is not working, it just stuck and
shape of an output tensor after convolutional filter on a colour image
I find it difficult to understand a notion about tensors. For VGG (https://www.tensorflow.org/api_docs/python/tf/keras/applications/VGG16), we start from a batch of colour images (none,224,224,3) and apply 64 2D convolutional filters. At the output we obtain a tensor of (none,224,224,64), we can see this by making a summary of the model. However, a filter must treat all 3 colours and my intuition tells
Keras model.evaluate accuracy stuck at 50 percent while using ImageDataGenerator
I am trying to find the accuracy of my saved Keras model using model.evaluate. I have loaded in my model using this: I have a CSV file with two columns, one for the filename of an image and one for the label. Here is a sample: I have loaded this CSV into a pandas dataframe and fed it into an
The most robust way to work with the specific version of Tensorflow with GPU support
The project I am working on uses the Tensorflow 2.0, which is not the most recent version of this Deep Learning Framework and I would like to enable GPU support. I have already locally installed Tensorflow 2.4.1 for another purposes with CUDA-11.0. This version of Tensorflow sees the GPU on my PC and I can perform training and inference without
TypeError: Input ‘y’ of ‘Mul’ Op has type float32 that does not match type int64 of argument ‘x’
after this code i am getting the error in categoricalfocalloss i m not getting whereint64 error is coming model description here in this code , in the loss categoricalfocal loss is used here in the model i used categorical focal loss when i run this ,in train dataset i am not getting how tcovert itintointoint64 error is got is mentioned
Tensorflow Warning every time I import it – ‘cudart64_101.dll not found’. Is there a way to get rid of only this warning?
The execution of the code tends to pause for a while when this occurs. Is there a solution to this? Answer If you’re working with Tensorflow 2.0, setting TF_CPP_MIN_LOG_LEVEL should still work You can disable all logs using os.environ: Here, (The above is tested on TensorFlow 0.12 and 1.0)
why am I getting warning/error when working with tensorflow (use functional API and not implemented error)
I am trying to follow this tutorial but with my data: https://www.tensorflow.org/tutorials/structured_data/feature_columns All of my data is numerical values. when I ran this part of code: I am getting this type of warning for all of the parameters: I am getting this warning twice for each variable! and then I am getting this error: What is the problem and how
Tensorflow dataset from numpy array
I have two numpy Arrays (X, Y) which I want to convert to a tensorflow dataset. According to the documentation it should be possible to run When doing this however I get the error: ValueError: Shapes (15, 1) and (768, 15) are incompatible This would make sense if the shapes of the numpy Arrays would be incompatible to the expected