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polishing #2

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Sep 3, 2022
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polishing #2

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0a25ba1
license added in setup.py
davorrunje Sep 3, 2022
108501f
index fixed
davorrunje Sep 3, 2022
370f09e
index fixed
davorrunje Sep 3, 2022
5e37836
blind guesses
davorrunje Sep 3, 2022
4041549
remove .egg-info from git
davorrunje Sep 3, 2022
753f51f
.egg-info added to .gitignore
davorrunje Sep 3, 2022
9a7d776
_proc removed from git and added to .gitignore
davorrunje Sep 3, 2022
e00bfa3
polishing
davorrunje Sep 3, 2022
c5fbd90
merge with main
davorrunje Sep 3, 2022
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47 changes: 22 additions & 25 deletions README.md
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
mono-dense-keras
Constrained Monotonic Neural Networks
================

<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
Expand Down Expand Up @@ -71,7 +71,7 @@ def generate_data(no_samples: int, noise: float):
y = x[:, 0] ** 3
y += np.sin(x[:, 1] / (2*np.pi))
y += np.exp(-x[:, 2])
y += 0.1 * rng.normal(size=no_samples)
y += noise * rng.normal(size=no_samples)
return x, y

x_train, y_train = generate_data(10_000, noise=0.1)
Expand All @@ -98,9 +98,6 @@ model.add(Dense(128, activation="elu"))
model.add(Dense(1))
```

/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!
warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "

We’ll train the network using the `Adam` optimizer and the
`ExponentialDecay` learning rate schedule:

Expand All @@ -121,25 +118,25 @@ train_model(model, initial_learning_rate=.1)
```

Epoch 1/10
313/313 [==============================] - 1s 2ms/step - loss: 9.1129 - val_loss: 9.2426
313/313 [==============================] - 1s 2ms/step - loss: 9.1098 - val_loss: 9.2552
Epoch 2/10
313/313 [==============================] - 1s 2ms/step - loss: 7.8147 - val_loss: 8.4241
313/313 [==============================] - 1s 2ms/step - loss: 7.7995 - val_loss: 8.3143
Epoch 3/10
313/313 [==============================] - 1s 2ms/step - loss: 7.5386 - val_loss: 8.0456
313/313 [==============================] - 1s 2ms/step - loss: 7.5270 - val_loss: 8.0499
Epoch 4/10
313/313 [==============================] - 1s 2ms/step - loss: 7.0556 - val_loss: 7.3737
313/313 [==============================] - 1s 2ms/step - loss: 7.2095 - val_loss: 7.5935
Epoch 5/10
313/313 [==============================] - 1s 2ms/step - loss: 5.8239 - val_loss: 4.4619
313/313 [==============================] - 1s 2ms/step - loss: 6.0665 - val_loss: 6.7911
Epoch 6/10
313/313 [==============================] - 1s 2ms/step - loss: 2.3169 - val_loss: 1.5477
313/313 [==============================] - 1s 2ms/step - loss: 3.1178 - val_loss: 1.5964
Epoch 7/10
313/313 [==============================] - 1s 2ms/step - loss: 1.0104 - val_loss: 0.7407
313/313 [==============================] - 1s 2ms/step - loss: 1.1686 - val_loss: 0.8541
Epoch 8/10
313/313 [==============================] - 1s 2ms/step - loss: 0.6479 - val_loss: 0.9755
313/313 [==============================] - 1s 2ms/step - loss: 0.7370 - val_loss: 1.5969
Epoch 9/10
313/313 [==============================] - 1s 2ms/step - loss: 0.5144 - val_loss: 0.3477
313/313 [==============================] - 1s 2ms/step - loss: 0.6011 - val_loss: 0.3739
Epoch 10/10
313/313 [==============================] - 1s 2ms/step - loss: 0.3837 - val_loss: 0.2306
313/313 [==============================] - 1s 2ms/step - loss: 0.4458 - val_loss: 0.3114

Now, we’ll use the
[`MonotonicDense`](https://airtai.github.io/mono-dense-keras/layers.html#monotonicdense)
Expand Down Expand Up @@ -179,22 +176,22 @@ train_model(model, initial_learning_rate=.001)
```

Epoch 1/10
313/313 [==============================] - 1s 2ms/step - loss: 0.3578 - val_loss: 0.1557
313/313 [==============================] - 1s 2ms/step - loss: 0.3646 - val_loss: 0.2042
Epoch 2/10
313/313 [==============================] - 1s 2ms/step - loss: 0.2650 - val_loss: 0.2754
313/313 [==============================] - 1s 2ms/step - loss: 0.2895 - val_loss: 0.1387
Epoch 3/10
313/313 [==============================] - 1s 2ms/step - loss: 0.2238 - val_loss: 0.0917
313/313 [==============================] - 1s 2ms/step - loss: 0.2756 - val_loss: 0.1027
Epoch 4/10
313/313 [==============================] - 1s 2ms/step - loss: 0.1867 - val_loss: 0.0798
313/313 [==============================] - 1s 2ms/step - loss: 0.2281 - val_loss: 0.0814
Epoch 5/10
313/313 [==============================] - 1s 2ms/step - loss: 0.1724 - val_loss: 0.7418
313/313 [==============================] - 1s 2ms/step - loss: 0.1816 - val_loss: 0.0634
Epoch 6/10
313/313 [==============================] - 1s 2ms/step - loss: 0.1356 - val_loss: 0.4525
313/313 [==============================] - 1s 2ms/step - loss: 0.1631 - val_loss: 0.1443
Epoch 7/10
313/313 [==============================] - 1s 2ms/step - loss: 0.1533 - val_loss: 0.0503
313/313 [==============================] - 1s 2ms/step - loss: 0.1455 - val_loss: 0.1299
Epoch 8/10
313/313 [==============================] - 1s 2ms/step - loss: 0.1274 - val_loss: 0.0395
313/313 [==============================] - 1s 2ms/step - loss: 0.1701 - val_loss: 0.0709
Epoch 9/10
313/313 [==============================] - 1s 2ms/step - loss: 0.1171 - val_loss: 0.0381
313/313 [==============================] - 1s 2ms/step - loss: 0.1250 - val_loss: 0.0644
Epoch 10/10
313/313 [==============================] - 1s 2ms/step - loss: 0.1005 - val_loss: 0.0500
313/313 [==============================] - 1s 2ms/step - loss: 0.1426 - val_loss: 0.0405
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