2.1.6 Forecasting NN

← 2.1 Core NNs

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• 1 Output
• 2 PY scripts
• 4 Code with detailed comments.

For details see #607_2.1_core_NNs_.docx.

1 Output

python d8b_forecasting_sine.py
epoch=0 loss=0.518329
epoch=200 loss=0.001539
epoch=400 loss=0.001482
epoch=600 loss=0.001445
epoch=800 loss=0.001417
epoch=1000 loss=0.001387

Tests:
[0.0, 0.2, 0.39, 0.56, 0.72] predicted_next= 0.8393
[0.84, 0.93, 0.99, 1.0, 0.97] predicted_next= 0.9014
[0.91, 0.81, 0.68, 0.52, 0.33] predicted_next= 0.1453

python d8_forecasting_nn.py
epoch=0 loss=319.777161
epoch=200 loss=0.663086
epoch=400 loss=0.089288
epoch=600 loss=0.031286
epoch=800 loss=0.007858
epoch=1000 loss=0.001386

Tests:
[1, 2, 3, 4, 5] predicted_next= 6.0689
[10, 11, 12, 13, 14] predicted_next= 15.0446
[2, 4, 6, 8, 10] predicted_next= 12.0219
[5, 7, 9, 11, 13] predicted_next= 15.0138

2 PY scripts

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4 Code with detailed comments

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output

Extremely important concept

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