tensorflow_cookbook
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nfmcclure
Code for Tensorflow Machine Learning Cookbook
Bumps [numpy](https://github.com/numpy/numpy) from 1.14.5 to 1.22.0. Release notes Sourced from numpy's releases. v1.22.0 NumPy 1.22.0 Release Notes NumPy 1.22.0 is a big release featuring the work of 153 contributors spread...
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Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 1.15.0 to 2.7.2. Release notes Sourced from tensorflow's releases. TensorFlow 2.7.2 Release 2.7.2 This releases introduces several vulnerability fixes: Fixes a code injection in saved_model_cli (CVE-2022-29216) Fixes...
Bumps [pillow](https://github.com/python-pillow/Pillow) from 6.2.0 to 9.0.1. Release notes Sourced from pillow's releases. 9.0.1 https://pillow.readthedocs.io/en/stable/releasenotes/9.0.1.html Changes In show_file, use os.remove to remove temporary images. CVE-2022-24303 #6010 [@radarhere, @hugovk] Restrict builtins within...
There is no need to calculate the distance in a quadratic form by generating the diagonal matrix intermediately. Just arranging the weights as a vector and calculating the linear combination...
Ch4,04_svm_kernels.py,line71: `prediction = tf.sign(prediction_output - tf.reduce_mean(prediction_output))` I can't find that line in any book/posts. Instead, my book said when you predict, you should calculate the bias first. Who can explain...
Hi I would like to propose a better implementation for 'test_indices': We can remove the unneeded np.array casting: Cleaner/New: test_indices = list(set(range(len(texts))) - set(train_indices)) Old: test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))
Sorry my English is not well but i am really want to solve this problem so i come for your help. After learn your code where (**tensorflow_cookbook/04_Support_Vector_Machines/06_Implementing_Multiclass_SVMs/)** I am a...
Hi, I want to apply 04_Support_Vector_Machines to Mnist dataset. I found that I couldn't use the following function for testing (because it contained lable): prediction_output = tf.matmul(tf.multiply(y_target, b), pred_kernel) prediction...
