So I was having different results with a self-implemented code and Tensorflow results. I wanted to test each value to see where was my error (loss, gradients, optimizer, etc). Therefore I did a test code like the one in this repo inspired on the fashion mnist example. Just for simplicity I will copy-paste it at the end of the question.
Tag: tensorflow
how to use more three channels input in train_datagen
I am trying to apply Keras for images with more than three spectral channels. I noticed that train_datagen handles images with three channels based on color_mode=’rgb’. Is there any way to increase the number input channels or are there any alternative methods? Answer You can have 1, 3 or 4 channels. See the docs. color_mode One of “grayscale”, “rgb”, “rgba”.
TensorFlow CNN Incompatible Shapes: 4D input shape
I have sample data in the form: Data[n][31][31][5][2] with: “[n]” being the sample “[31][31]” being the array of data points “[5]” being the number of bits within that data point and “[2]” being one-hot encoding of the bits (eg a bit of 1 would be [1, 0] and a zero [0, 1]) The output is intended to either be a
Tensorflow Keras evaluate method return a empty list
Hello i am follow the time series forecasting tutorial in tensorflow https://www.tensorflow.org/tutorials/structured_data/time_series, I have the same project, the only difference is that I am using a different dataset, when evaluating the models, the model.evaluate () method returns an empty list, it does not return a value. When the model is trained with the fit() method, evaluation values are generated in
Gradient Accumulation with Custom model.fit in TF.Keras?
Please add a minimum comment on your thoughts so that I can improve my query. Thank you. -) I’m trying to train a tf.keras model with Gradient Accumulation (GA). But I don’t want to use it in the custom training loop (like) but customize the .fit() method by overriding the train_step.Is it possible? How to accomplish this? The reason is
Keras: Classification report accuracy is different between model.predict accuracy for multiclass
Colab link is here: The data is imported the following was The model is trained the following way I am struggling with getting the right predicted categories and right true_categories to get the classification report to work: At the moment the output of the epoch is contradicting the classification report The validation set on the model returns while the classification
How to convert tensor from 2D to 4D
I’m currently working with DICOM files and the TensorFlow IO library for DICOM files seems to throw some errors. So what I was originally doing was this: Anyways, I instead decided to load the DICOM files with pydicom, which seems to work loading the data into a numpy array. Yet, when I create a tensor from the data, I can’t
How to get multiprocessing for subtask work with tensorflow-gpu?
Basically I use tf-gpu 2.3rc0 to perform image detection on a video stream. For each loop, there’s a subtask runs separately. So I try to use Pool from multiprocessing The structure is like: It runs without error, but the tf seems to re-initiate for each frame, resulting to a extremely slow speed… Specifically, the same as follows shows for each
tensorflow: InvalidArgumentError while find the AUC score
I have a dataset with labels 0’s and 1’s, which is binary classification problem. Getting error while try to find AUC score using tf.keras.metrics.AUC() as metrics in model.compile(.. function. Code: If I removed AUC from metrics, the code runs fine. Error: InvalidArgumentError: assertion failed: [predictions must be >= 0] [Condition x >= y did not hold element-wise:] [x (sequential_48/dense_293/BiasAdd:0) =
How to Calculate Confusion Matrix on test Data?
I want to plot a confusion matrix on the validation data. Specifically, I want to calculate a confusion matrix of the model output on the validation data. I tried everything online, but couldn’t figure it out. here is my model: Answer Here is a dummy example. DataSet Model Confusion Matrix Your interest is mostly here. Visualization Let’s visualize. Update Based