Using the Bert code, I see this import line: from tensorflow.compiler.xla.experimental.xla_sharding import xla_sharding which causes this error: ModuleNotFoundError: No module named ‘tensorflow.compiler.xla.experimental’ Below you can see the versions and packages I have installed: $ pip list | grep tensorflow tensorflow 2.12.0 tensorflow-addons 0.20.0 tensorflow-estimator 2.12.0 tensorflow-io-gcs-filesystem 0.32.0 $ find python3.8/site-packages/ -name compiler python3.8/site-packages/google/protobuf/compiler python3.8/site-packages/tensorflow/python/compiler python3.8/site-packages/tensorflow/xla_aot_runtime_src/tensorflow/compiler… Read More Tensoflow 2.12.0 does not recognize tensorflow.compiler.xla.experimental

I noticed that saving a Tensorflow model prior to training causes the training to perform poorly. Obviously the solution is to just save the model later, but I’m curious why this is happening in the first place. The following code runs fine, and produces the following output: import tensorflow as tf from tensorflow import keras… Read More Training fails if model is saved beforehand

I want to do some image processing. To do so, I put in an image and get back another one (a mask) For testing, my dataset consists of only one image and its mask: train_data = tf.data.Dataset.from_tensors((img, mask)) both are of shape (720, 1280, 3). I tried using a simple model: model = tf.keras.Sequential([ tf.keras.layers.experimental.preprocessing.Rescaling(1./255,… Read More Tensorflow: dataset or model is in wrong shape

So i want to calculate the mean of the elements that are greater than a fixed threshold (threshold=0.8). Task in numpy : X = np.array([[0.11,0.99,0.70]]) print(np.nanmean(X[X>0.8])) Out : 0.99 What is the equivalent in tensorflow without converting the tensor ‘c’ to a numpy array? Exmp : c = tf.constant([[0.11,0.99,0.70]]) tf.reduce_mean(tf.where(tf.greater(c,(tf.constant(0.8, dtype=tf.float32))))) the output is equal… Read More how to calculate the average of elements above a certain threshold in tensorflow

When I run my code: import tensorflow as tf import numpy as np A = np.array([ [0,1,0,1,1,0,0,0,0,1], [0,1,0,1,1,0,0,0,0,0], [0,1,0,1,0,0,0,0,0,1] ]) sliced = A[:, -1] bool_tensor = tf.math.equal(sliced, 0) with tf.compat.v1.Session() as tfs: print(‘run(bool_tensor) : ‘,tfs.run(bool_tensor)) print(tf.cond(bool_tensor, lambda: 999, lambda: -999)) I get: run(bool_tensor) : [False True False] ValueError: Shape must be rank 0 but is… Read More Tensorflow: tf.cond, how to return multi dim tensors instead of simple values?

I have a middleware: export async function imageTagging(image){ console.log(image) const response = await axios.get(‘/api/tensorflow’, { image: image }); return response.data[0]; } which logs me (image): <img data-v-713aaf8f="" id="MLIMAGE" src="/img/Erdmännchen.b5f674c2.jpg" width="50%" crossorigin="anonymous" class="card-img-top embed-responsive-item" style="border-style: solid;"> my API-Call in the backend looks like: app.get(‘/api/tensorflow’, userMiddleware.isLoggedIn, (req, res) => { console.log(req.query); console.log(res.query); }) it logs me: {}… Read More How can I bring a HTML Image to the backend?

I am trying to convert all nan values to zero in my final results. I am not able to execute it properly! The following is the code: also availvable on colab : link import tensorflow as tf import numpy as np ts = tf.constant([[0,0]]) tx = tf.constant([[0,1]]) out = ts / (ts + fx) out.numpy()… Read More Converting all nan values to zero in tensforflow