text stringlengths 0 4.99k |
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5/5 [==============================] - 0s 29ms/step - loss: 33.3177 - root_mean_squared_error: 5.7700 - val_loss: 36.2135 - val_root_mean_squared_error: 6.0164 |
Epoch 4/500 |
5/5 [==============================] - 0s 30ms/step - loss: 35.1247 - root_mean_squared_error: 5.9232 - val_loss: 35.6158 - val_root_mean_squared_error: 5.9663 |
Epoch 5/500 |
5/5 [==============================] - 0s 23ms/step - loss: 34.7653 - root_mean_squared_error: 5.8936 - val_loss: 34.3038 - val_root_mean_squared_error: 5.8556 |
... |
Epoch 495/500 |
5/5 [==============================] - 0s 24ms/step - loss: 0.6978 - root_mean_squared_error: 0.8162 - val_loss: 0.6258 - val_root_mean_squared_error: 0.7723 |
Epoch 496/500 |
5/5 [==============================] - 0s 22ms/step - loss: 0.6448 - root_mean_squared_error: 0.7858 - val_loss: 0.6372 - val_root_mean_squared_error: 0.7808 |
Epoch 497/500 |
5/5 [==============================] - 0s 23ms/step - loss: 0.6871 - root_mean_squared_error: 0.8121 - val_loss: 0.6437 - val_root_mean_squared_error: 0.7825 |
Epoch 498/500 |
5/5 [==============================] - 0s 23ms/step - loss: 0.6213 - root_mean_squared_error: 0.7690 - val_loss: 0.6581 - val_root_mean_squared_error: 0.7922 |
Epoch 499/500 |
5/5 [==============================] - 0s 22ms/step - loss: 0.6604 - root_mean_squared_error: 0.7913 - val_loss: 0.6522 - val_root_mean_squared_error: 0.7908 |
Epoch 500/500 |
5/5 [==============================] - 0s 22ms/step - loss: 0.6190 - root_mean_squared_error: 0.7678 - val_loss: 0.6734 - val_root_mean_squared_error: 0.8037 |
Model training finished. |
Train RMSE: 0.805 |
Evaluating model performance... |
Test RMSE: 0.801 |
Since we have trained a BNN model, the model produces a different output each time we call it with the same input, since each time a new set of weights are sampled from the distributions to construct the network and produce an output. The less certain the mode weights are, the more variability (wider range) we will see in the outputs of the same inputs. |
def compute_predictions(model, iterations=100): |
predicted = [] |
for _ in range(iterations): |
predicted.append(model(examples).numpy()) |
predicted = np.concatenate(predicted, axis=1) |
prediction_mean = np.mean(predicted, axis=1).tolist() |
prediction_min = np.min(predicted, axis=1).tolist() |
prediction_max = np.max(predicted, axis=1).tolist() |
prediction_range = (np.max(predicted, axis=1) - np.min(predicted, axis=1)).tolist() |
for idx in range(sample): |
print( |
f\"Predictions mean: {round(prediction_mean[idx], 2)}, \" |
f\"min: {round(prediction_min[idx], 2)}, \" |
f\"max: {round(prediction_max[idx], 2)}, \" |
f\"range: {round(prediction_range[idx], 2)} - \" |
f\"Actual: {targets[idx]}\" |
) |
compute_predictions(bnn_model_small) |
Predictions mean: 5.63, min: 4.92, max: 6.15, range: 1.23 - Actual: 6.0 |
Predictions mean: 6.35, min: 6.01, max: 6.54, range: 0.53 - Actual: 6.0 |
Predictions mean: 5.65, min: 4.84, max: 6.25, range: 1.41 - Actual: 7.0 |
Predictions mean: 5.74, min: 5.21, max: 6.25, range: 1.04 - Actual: 5.0 |
Predictions mean: 5.99, min: 5.26, max: 6.29, range: 1.03 - Actual: 5.0 |
Predictions mean: 6.26, min: 6.01, max: 6.47, range: 0.46 - Actual: 7.0 |
Predictions mean: 5.28, min: 4.73, max: 5.86, range: 1.12 - Actual: 5.0 |
Predictions mean: 6.34, min: 6.06, max: 6.53, range: 0.47 - Actual: 6.0 |
Predictions mean: 6.23, min: 5.91, max: 6.44, range: 0.53 - Actual: 6.0 |
Predictions mean: 6.33, min: 6.05, max: 6.54, range: 0.48 - Actual: 7.0 |
Train BNN with the whole training set. |
num_epochs = 500 |
bnn_model_full = create_bnn_model(train_size) |
run_experiment(bnn_model_full, mse_loss, train_dataset, test_dataset) |
compute_predictions(bnn_model_full) |
Start training the model... |
Epoch 1/500 |
17/17 [==============================] - 2s 32ms/step - loss: 25.4811 - root_mean_squared_error: 5.0465 - val_loss: 23.8428 - val_root_mean_squared_error: 4.8824 |
Epoch 2/500 |
17/17 [==============================] - 0s 7ms/step - loss: 23.0849 - root_mean_squared_error: 4.8040 - val_loss: 24.1269 - val_root_mean_squared_error: 4.9115 |
Epoch 3/500 |
17/17 [==============================] - 0s 7ms/step - loss: 22.5191 - root_mean_squared_error: 4.7449 - val_loss: 23.3312 - val_root_mean_squared_error: 4.8297 |
Epoch 4/500 |
17/17 [==============================] - 0s 7ms/step - loss: 22.9571 - root_mean_squared_error: 4.7896 - val_loss: 24.4072 - val_root_mean_squared_error: 4.9399 |
Epoch 5/500 |
17/17 [==============================] - 0s 6ms/step - loss: 21.4049 - root_mean_squared_error: 4.6245 - val_loss: 21.1895 - val_root_mean_squared_error: 4.6027 |
... |
Epoch 495/500 |
17/17 [==============================] - 0s 7ms/step - loss: 0.5799 - root_mean_squared_error: 0.7511 - val_loss: 0.5902 - val_root_mean_squared_error: 0.7572 |
Epoch 496/500 |
17/17 [==============================] - 0s 6ms/step - loss: 0.5926 - root_mean_squared_error: 0.7603 - val_loss: 0.5961 - val_root_mean_squared_error: 0.7616 |
Epoch 497/500 |
17/17 [==============================] - 0s 7ms/step - loss: 0.5928 - root_mean_squared_error: 0.7595 - val_loss: 0.5916 - val_root_mean_squared_error: 0.7595 |
Epoch 498/500 |
17/17 [==============================] - 0s 7ms/step - loss: 0.6115 - root_mean_squared_error: 0.7715 - val_loss: 0.5869 - val_root_mean_squared_error: 0.7558 |
Epoch 499/500 |
17/17 [==============================] - 0s 7ms/step - loss: 0.6044 - root_mean_squared_error: 0.7673 - val_loss: 0.6007 - val_root_mean_squared_error: 0.7645 |
Epoch 500/500 |
17/17 [==============================] - 0s 7ms/step - loss: 0.5853 - root_mean_squared_error: 0.7550 - val_loss: 0.5999 - val_root_mean_squared_error: 0.7651 |
Model training finished. |
Train RMSE: 0.762 |
Evaluating model performance... |
Test RMSE: 0.759 |
Predictions mean: 5.41, min: 5.06, max: 5.9, range: 0.84 - Actual: 6.0 |
Predictions mean: 6.5, min: 6.16, max: 6.61, range: 0.44 - Actual: 6.0 |
Predictions mean: 5.59, min: 4.96, max: 6.0, range: 1.04 - Actual: 7.0 |
Predictions mean: 5.67, min: 5.25, max: 6.01, range: 0.76 - Actual: 5.0 |
Predictions mean: 6.02, min: 5.68, max: 6.39, range: 0.71 - Actual: 5.0 |
Predictions mean: 6.35, min: 6.11, max: 6.52, range: 0.41 - Actual: 7.0 |
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