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), |
keras.layers.Dense(256, activation=\"relu\"), |
keras.layers.Dropout(0.3), |
keras.layers.Dense(256, activation=\"relu\"), |
keras.layers.Dropout(0.3), |
keras.layers.Dense(1, activation=\"sigmoid\"), |
] |
) |
model.summary() |
Model: \"sequential\" |
_________________________________________________________________ |
Layer (type) Output Shape Param # |
================================================================= |
dense (Dense) (None, 256) 7936 |
_________________________________________________________________ |
dense_1 (Dense) (None, 256) 65792 |
_________________________________________________________________ |
dropout (Dropout) (None, 256) 0 |
_________________________________________________________________ |
dense_2 (Dense) (None, 256) 65792 |
_________________________________________________________________ |
dropout_1 (Dropout) (None, 256) 0 |
_________________________________________________________________ |
dense_3 (Dense) (None, 1) 257 |
================================================================= |
Total params: 139,777 |
Trainable params: 139,777 |
Non-trainable params: 0 |
_________________________________________________________________ |
Train the model with class_weight argument |
metrics = [ |
keras.metrics.FalseNegatives(name=\"fn\"), |
keras.metrics.FalsePositives(name=\"fp\"), |
keras.metrics.TrueNegatives(name=\"tn\"), |
keras.metrics.TruePositives(name=\"tp\"), |
keras.metrics.Precision(name=\"precision\"), |
keras.metrics.Recall(name=\"recall\"), |
] |
model.compile( |
optimizer=keras.optimizers.Adam(1e-2), loss=\"binary_crossentropy\", metrics=metrics |
) |
callbacks = [keras.callbacks.ModelCheckpoint(\"fraud_model_at_epoch_{epoch}.h5\")] |
class_weight = {0: weight_for_0, 1: weight_for_1} |
model.fit( |
train_features, |
train_targets, |
batch_size=2048, |
epochs=30, |
verbose=2, |
callbacks=callbacks, |
validation_data=(val_features, val_targets), |
class_weight=class_weight, |
) |
Epoch 1/30 |
112/112 - 2s - loss: 2.4210e-06 - fn: 51.0000 - fp: 29417.0000 - tn: 198012.0000 - tp: 366.0000 - precision: 0.0123 - recall: 0.8777 - val_loss: 0.0759 - val_fn: 9.0000 - val_fp: 611.0000 - val_tn: 56275.0000 - val_tp: 66.0000 - val_precision: 0.0975 - val_recall: 0.8800 |
Epoch 2/30 |
112/112 - 2s - loss: 1.4337e-06 - fn: 35.0000 - fp: 7058.0000 - tn: 220371.0000 - tp: 382.0000 - precision: 0.0513 - recall: 0.9161 - val_loss: 0.1632 - val_fn: 6.0000 - val_fp: 2343.0000 - val_tn: 54543.0000 - val_tp: 69.0000 - val_precision: 0.0286 - val_recall: 0.9200 |
Epoch 3/30 |
112/112 - 2s - loss: 1.2100e-06 - fn: 27.0000 - fp: 7382.0000 - tn: 220047.0000 - tp: 390.0000 - precision: 0.0502 - recall: 0.9353 - val_loss: 0.1882 - val_fn: 5.0000 - val_fp: 3690.0000 - val_tn: 53196.0000 - val_tp: 70.0000 - val_precision: 0.0186 - val_recall: 0.9333 |
Epoch 4/30 |
112/112 - 2s - loss: 1.0770e-06 - fn: 24.0000 - fp: 7306.0000 - tn: 220123.0000 - tp: 393.0000 - precision: 0.0510 - recall: 0.9424 - val_loss: 0.0444 - val_fn: 9.0000 - val_fp: 674.0000 - val_tn: 56212.0000 - val_tp: 66.0000 - val_precision: 0.0892 - val_recall: 0.8800 |
Epoch 5/30 |
112/112 - 2s - loss: 9.3284e-07 - fn: 18.0000 - fp: 5607.0000 - tn: 221822.0000 - tp: 399.0000 - precision: 0.0664 - recall: 0.9568 - val_loss: 0.0455 - val_fn: 8.0000 - val_fp: 604.0000 - val_tn: 56282.0000 - val_tp: 67.0000 - val_precision: 0.0999 - val_recall: 0.8933 |
Epoch 6/30 |
112/112 - 2s - loss: 8.9186e-07 - fn: 21.0000 - fp: 6917.0000 - tn: 220512.0000 - tp: 396.0000 - precision: 0.0542 - recall: 0.9496 - val_loss: 0.0385 - val_fn: 9.0000 - val_fp: 462.0000 - val_tn: 56424.0000 - val_tp: 66.0000 - val_precision: 0.1250 - val_recall: 0.8800 |
Epoch 7/30 |
112/112 - 2s - loss: 6.4562e-07 - fn: 13.0000 - fp: 5878.0000 - tn: 221551.0000 - tp: 404.0000 - precision: 0.0643 - recall: 0.9688 - val_loss: 0.0205 - val_fn: 9.0000 - val_fp: 372.0000 - val_tn: 56514.0000 - val_tp: 66.0000 - val_precision: 0.1507 - val_recall: 0.8800 |
Epoch 8/30 |
112/112 - 2s - loss: 7.3378e-07 - fn: 15.0000 - fp: 6825.0000 - tn: 220604.0000 - tp: 402.0000 - precision: 0.0556 - recall: 0.9640 - val_loss: 0.0188 - val_fn: 10.0000 - val_fp: 246.0000 - val_tn: 56640.0000 - val_tp: 65.0000 - val_precision: 0.2090 - val_recall: 0.8667 |
Epoch 9/30 |
112/112 - 2s - loss: 5.1385e-07 - fn: 9.0000 - fp: 5265.0000 - tn: 222164.0000 - tp: 408.0000 - precision: 0.0719 - recall: 0.9784 - val_loss: 0.0244 - val_fn: 11.0000 - val_fp: 495.0000 - val_tn: 56391.0000 - val_tp: 64.0000 - val_precision: 0.1145 - val_recall: 0.8533 |
Epoch 10/30 |
112/112 - 2s - loss: 8.6498e-07 - fn: 13.0000 - fp: 8506.0000 - tn: 218923.0000 - tp: 404.0000 - precision: 0.0453 - recall: 0.9688 - val_loss: 0.0177 - val_fn: 11.0000 - val_fp: 367.0000 - val_tn: 56519.0000 - val_tp: 64.0000 - val_precision: 0.1485 - val_recall: 0.8533 |
Epoch 11/30 |
112/112 - 2s - loss: 6.0585e-07 - fn: 12.0000 - fp: 6676.0000 - tn: 220753.0000 - tp: 405.0000 - precision: 0.0572 - recall: 0.9712 - val_loss: 0.0356 - val_fn: 9.0000 - val_fp: 751.0000 - val_tn: 56135.0000 - val_tp: 66.0000 - val_precision: 0.0808 - val_recall: 0.8800 |
Epoch 12/30 |
112/112 - 2s - loss: 6.0788e-07 - fn: 9.0000 - fp: 6219.0000 - tn: 221210.0000 - tp: 408.0000 - precision: 0.0616 - recall: 0.9784 - val_loss: 0.0249 - val_fn: 10.0000 - val_fp: 487.0000 - val_tn: 56399.0000 - val_tp: 65.0000 - val_precision: 0.1178 - val_recall: 0.8667 |
Epoch 13/30 |
112/112 - 3s - loss: 8.3899e-07 - fn: 12.0000 - fp: 6612.0000 - tn: 220817.0000 - tp: 405.0000 - precision: 0.0577 - recall: 0.9712 - val_loss: 0.0905 - val_fn: 5.0000 - val_fp: 2159.0000 - val_tn: 54727.0000 - val_tp: 70.0000 - val_precision: 0.0314 - val_recall: 0.9333 |
Epoch 14/30 |
112/112 - 3s - loss: 6.0584e-07 - fn: 8.0000 - fp: 6823.0000 - tn: 220606.0000 - tp: 409.0000 - precision: 0.0566 - recall: 0.9808 - val_loss: 0.0205 - val_fn: 10.0000 - val_fp: 446.0000 - val_tn: 56440.0000 - val_tp: 65.0000 - val_precision: 0.1272 - val_recall: 0.8667 |
Epoch 15/30 |
112/112 - 2s - loss: 3.9569e-07 - fn: 6.0000 - fp: 3820.0000 - tn: 223609.0000 - tp: 411.0000 - precision: 0.0971 - recall: 0.9856 - val_loss: 0.0212 - val_fn: 10.0000 - val_fp: 413.0000 - val_tn: 56473.0000 - val_tp: 65.0000 - val_precision: 0.1360 - val_recall: 0.8667 |
Epoch 16/30 |
112/112 - 2s - loss: 5.4548e-07 - fn: 5.0000 - fp: 3910.0000 - tn: 223519.0000 - tp: 412.0000 - precision: 0.0953 - recall: 0.9880 - val_loss: 0.0906 - val_fn: 8.0000 - val_fp: 1905.0000 - val_tn: 54981.0000 - val_tp: 67.0000 - val_precision: 0.0340 - val_recall: 0.8933 |
Epoch 17/30 |
112/112 - 3s - loss: 6.2734e-07 - fn: 8.0000 - fp: 6005.0000 - tn: 221424.0000 - tp: 409.0000 - precision: 0.0638 - recall: 0.9808 - val_loss: 0.0161 - val_fn: 10.0000 - val_fp: 340.0000 - val_tn: 56546.0000 - val_tp: 65.0000 - val_precision: 0.1605 - val_recall: 0.8667 |
Epoch 18/30 |
112/112 - 3s - loss: 4.9752e-07 - fn: 5.0000 - fp: 4302.0000 - tn: 223127.0000 - tp: 412.0000 - precision: 0.0874 - recall: 0.9880 - val_loss: 0.0186 - val_fn: 10.0000 - val_fp: 408.0000 - val_tn: 56478.0000 - val_tp: 65.0000 - val_precision: 0.1374 - val_recall: 0.8667 |
Epoch 19/30 |
112/112 - 3s - loss: 6.7296e-07 - fn: 5.0000 - fp: 5986.0000 - tn: 221443.0000 - tp: 412.0000 - precision: 0.0644 - recall: 0.9880 - val_loss: 0.0165 - val_fn: 10.0000 - val_fp: 276.0000 - val_tn: 56610.0000 - val_tp: 65.0000 - val_precision: 0.1906 - val_recall: 0.8667 |
Epoch 20/30 |
112/112 - 3s - loss: 5.0178e-07 - fn: 7.0000 - fp: 5161.0000 - tn: 222268.0000 - tp: 410.0000 - precision: 0.0736 - recall: 0.9832 - val_loss: 0.2156 - val_fn: 7.0000 - val_fp: 1041.0000 - val_tn: 55845.0000 - val_tp: 68.0000 - val_precision: 0.0613 - val_recall: 0.9067 |
Epoch 21/30 |
112/112 - 3s - loss: 7.1907e-07 - fn: 7.0000 - fp: 5825.0000 - tn: 221604.0000 - tp: 410.0000 - precision: 0.0658 - recall: 0.9832 - val_loss: 0.0283 - val_fn: 8.0000 - val_fp: 511.0000 - val_tn: 56375.0000 - val_tp: 67.0000 - val_precision: 0.1159 - val_recall: 0.8933 |
Epoch 22/30 |
112/112 - 3s - loss: 3.6405e-07 - fn: 6.0000 - fp: 4149.0000 - tn: 223280.0000 - tp: 411.0000 - precision: 0.0901 - recall: 0.9856 - val_loss: 0.0269 - val_fn: 8.0000 - val_fp: 554.0000 - val_tn: 56332.0000 - val_tp: 67.0000 - val_precision: 0.1079 - val_recall: 0.8933 |
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