Code for Keras plays catch blog post
python qlearn.py- Generate figures
| import collections, math, random, numpy | |
| import tensorflow as tf | |
| from sklearn.cross_validation import train_test_split | |
| sentences = """hated the movie it was stupid;\ni hated it so boring;\nawesome the movie was inspiring;\nhated it what a disaster;\nwe hated the movie they were idiotic;\nhe was stupid, hated her;\nstupid movie is boring;\ninspiring ourselves, awesome;\ninspiring me, brilliant;\nwe hated it they were rubbish;\nany inspiring movie is amazing;\nit was stupid what a disaster;\nits stupid, rubbish;\nstupid, idiotic!;\nawesome great movie;\nboring, must be hated;\nhe was boring the movie was stupid;\nboring movie was a disaster;\nboth boring and rubbish;\nso boring and idiotic;\ngreat to amazing;\ndisaster, more than hated;\nbetween disaster and stupid;\ndisaster, so boring;\nawesome movie, brilliant;\ntoo awesome she was amazing;\nhe was brilliant loved it;\ndisaster, only idiotic;\nrubbish movie hated him;\nit was rubbish, why so stupid?;\nrubbish, too boring;\nrubbish, disaster!;\nrubbish, very |
Code for Keras plays catch blog post
python qlearn.py| # 6.1 Example: Learning XOR - GBC Book - Chapter 6 - pp. 166 to 171 | |
| # Some parts are inspired by the blog post | |
| # Solving XOR with a Neural Network in TensorFlow | |
| # by Stephen OMAN | |
| # https://github.com/StephenOman/TensorFlowExamples/blob/master/xor%20nn/xor_nn.py | |
| # Activation RELU + sigmoid for binary classification output + MSE loss function | |
| # In order to ensure compatibility with TensorFlow 2 | |
| # import tensorflow as tf | |
| import tensorflow.compat.v1 as tf |
| Epoch: 0 | |
| y_estimated: | |
| [ 0.49961096] | |
| [ 0.9245432] | |
| [ 0.50023597] | |
| [ 0.91048223] | |
| W: | |
| [-0.41441455 -0.10702395] | |
| [-1.52169275 1.46376789] | |
| c: |