our sensory systems on Darwin's theory were not shaped to show us the truth. They were shaped to keep you alive long enough to reproduce successfully. Period. That's all Dharm's theory actually says

Code to reproduce issue

ONNX 1.17.1 prints:

Whenever FreezeSavedModel function is called in the code, tensorflow::ClientSession cannot execute properly. Things work absolutely fine if FreezeSavedModel function is commented out.

Facing the same issue here. My Os is Ubuntu 18.04.

The behavior you mentioned, where tf.squeeze() is converted to a reshape operator when using the TOCO (Tens

I'm able to replicate the same behavior from my end

Standalone code to reproduce the issue

To reproduce: $ bazel-bin/tensorflow/compiler/mlir/tf-opt verify.mlir.

lestevementioned this on Sep 16, 2024⚠️ CI failed on Wheel builder (last failure: Sep 16, 2024) ⚠️ scikit-learn/scikit-learn#29852

Standalone code to reproduce the issue

tf.estimator.Estimator(model_fn)

I've managed to reproduce this error without modelstore.

Conv3DTranspose_class = tf.keras.layers.Conv3DTranspose(filters, kernel_size, strides=strides, padding=padding, output_padding=output_padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint) layer = Conv3DTranspose_class inputs = __input___0 with tf.GradientTape() as g:

I was able to reproduce the issue on tensorflow v2.8, v2.9 and nightly. Kindly find the gist of it here.

Could you please submit a minimal code snippet for reproduction of issue. Also the dataset modified_train.txt is missing.

I was able to replicate this issue on colab, please find the gist here. Thank you!

t is not clear how you are getting different results on quantization perhaps you can explain more. Are you observing incorrect results with tf.compat.v1.lite.TFLiteConverter.from_keras_model_file and correct results with tf.lite.TFLiteConverter.from_keras_model ?

Copy-and-paste code to reproduce this:

Standalone code to reproduce the issue

there was a paper that came out a few years ago showing that five degrees at the pace we're doing would be 00:40:13 is like easily sufficient to reproduce some of these catastrophes in Earth history

a forest actually moves um and and trees move but one of the things that they do is utilize other organisms to move them to move them 00:41:41 because reproduction is a way in which they plant transplant themselves further away from their sight of of of of their rooted site

This is the kind of bug we don't want to have, since it can be subtle, non-obvious, and hard to reproduce.