Lithofacies_prediction_1-D_CNN

In this notebook we have applied 1-D CNN model to the Force 2020 Lithology prediction dataset

Here's the model summary: Model: "sequential"

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Layer (type) Output Shape Param #

conv1d (Conv1D) (None, 14, 64) 256

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batch_normalization (BatchNo (None, 14, 64) 256

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conv1d_1 (Conv1D) (None, 12, 64) 12352

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batch_normalization_1 (Batch (None, 12, 64) 256

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max_pooling1d (MaxPooling1D) (None, 8, 64) 0

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batch_normalization_2 (Batch (None, 8, 64) 256

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conv1d_2 (Conv1D) (None, 6, 128) 24704

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batch_normalization_3 (Batch (None, 6, 128) 512

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conv1d_3 (Conv1D) (None, 4, 128) 49280

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batch_normalization_4 (Batch (None, 4, 128) 512

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max_pooling1d_1 (MaxPooling1 (None, 1, 128) 0

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batch_normalization_5 (Batch (None, 1, 128) 512

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flatten (Flatten) (None, 128) 0

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dropout (Dropout) (None, 128) 0

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dense (Dense) (None, 64) 8256

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dropout_1 (Dropout) (None, 64) 0

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dense_1 (Dense) (None, 32) 2080

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dropout_2 (Dropout) (None, 32) 0

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dense_2 (Dense) (None, 16) 528

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dense_3 (Dense) (None, 12) 204

Total params: 99,964 Trainable params: 98,812 Non-trainable params: 1,152

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Training data can be downloaded from the link given below:

https://github.com/bolgebrygg/Force-2020-Machine-Learning-competition/tree/master/lithology_competition/data