I cannot install tensorflow in pycharm on windows 10, though I have tried many different things: went to settings > project interpreter and tried clicking the green plus button to install it, gave me the error: non-zero exit code (1) and told me to try installing via pip in the command line, which was successful, but I can’t figure out
Tag: tensorflow
How to convert Tensorflow dataset to 2D numpy array
I have a TensorFlow dataset which contains nearly 15000 multicolored images with 168*84 resolution and a label for each image. Its type and shape are like this: I need to use it to train my network. That’s why I need to pass it as a parameter to this function that I built my layers in: I tried to convert each
ModuleNotFoundError: No module named ‘tensorflow.examples’
When I import tensorflow I don’t get an error. However, I do get the error below. I’m using spyder if that helps. As per other questions, I ensured up to date (v1.8) tensorflow using both conda and then pip installs. This didn’t resolve the issue. Please assist. Answer Sometimes on downloading the TF, the example directory might not be available.
Python not finding Tensorflow module under Anaconda
I’m a newbie trying to execute the code in first_steps_with_tensor_flow.ipynb locally on Windows 10. I have installed Anaconda Navigator 1.8.2, created an environment where I’ve installed, among others, tensorflow package. I then launch Visual Studio Code from Anaconda and run The last line is marked with red in VS Code, saying E0401:Unable to import ‘tensorflow.python.data’. Running, I get ModuleNotFoundError: No
Save and load model optimizer state
I have a set of fairly complicated models that I am training and I am looking for a way to save and load the model optimizer states. The “trainer models” consist of different combinations of several other “weight models”, of which some have shared weights, some have frozen weights depending on the trainer, etc. It is a bit too complicated
Get the bounding box coordinates in the TensorFlow object detection API tutorial
I am new to both Python and Tensorflow. I am trying to run the object detection tutorial file from the Tensorflow Object Detection API, but I cannot find where I can get the coordinates of the bounding boxes when objects are detected. Relevant code: The place where I assume bounding boxes are drawn is like this: I tried printing output_dict[‘detection_boxes’]
How to use tf.data.Dataset.apply() for reshaping the dataset
I am working with time series models in tensorflow. My dataset contains physics signals. I need to divide this signals into windows as give this sliced windows as input to my model. Here is how I am reading the data and slicing it: I want to reshape this dataset to # {‘mix’: TensorShape([Dimension(32)]), ‘pure’: TensorShape([Dimension(32))} Equivalent transformation in numpy would
Neural Machine Translation model predictions are off-by-one
Problem Summary In the following example, my NMT model has high loss because it correctly predicts target_input instead of target_output. As is evident, the prediction matches up almost 100% with target_input instead of target_output, as it should (off-by-one). Loss and gradients are being calculated using target_output, so it is strange that predictions are matching up to target_input. Model Overview An
Modifying the weights and biases of a restored CNN model in TensorFlow
I have recently started using TensorFlow (TF), and I have come across a problem that I need some help with. Basically, I’ve restored a pre-trained model, and I need to modify the weights and biases of one of its layers before I retest its accuracy. Now, my problem is the following: how can I change the weights and biases using
how to get covariance matrix in tensorflow?
How could I get covariance matrix in tensorflow? Like numpy.cov() in numpy. For example, I want to get covariance matrix of tensor A, now I have to use numpy instead Is there anyway to get cov by tensorflow instead of numpy? It is differnet from the problem how to compute covariance in tensorflow, where their problem is to compute covariance