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Epoch 00002: val_loss improved from 0.16357 to 0.13362, saving model to model_checkpoint.h5 |
1172/1172 [==============================] - 107s 92ms/step - loss: 0.1271 - val_loss: 0.1336 |
Epoch 3/10 |
1172/1172 [==============================] - ETA: 0s - loss: 0.1089 |
Epoch 00005: val_loss did not improve from 0.13362 |
1172/1172 [==============================] - 110s 94ms/step - loss: 0.1089 - val_loss: 0.1481 |
Epoch 6/10 |
271/1172 [=====>........................] - ETA: 1:12 - loss: 0.1117 |
We can visualize the loss with the function below. After one point, the loss stops decreasing. |
def visualize_loss(history, title): |
loss = history.history[\"loss\"] |
val_loss = history.history[\"val_loss\"] |
epochs = range(len(loss)) |
plt.figure() |
plt.plot(epochs, loss, \"b\", label=\"Training loss\") |
plt.plot(epochs, val_loss, \"r\", label=\"Validation loss\") |
plt.title(title) |
plt.xlabel(\"Epochs\") |
plt.ylabel(\"Loss\") |
plt.legend() |
plt.show() |
visualize_loss(history, \"Training and Validation Loss\") |
png |
Prediction |
The trained model above is now able to make predictions for 5 sets of values from validation set. |
def show_plot(plot_data, delta, title): |
labels = [\"History\", \"True Future\", \"Model Prediction\"] |
marker = [\".-\", \"rx\", \"go\"] |
time_steps = list(range(-(plot_data[0].shape[0]), 0)) |
if delta: |
future = delta |
else: |
future = 0 |
plt.title(title) |
for i, val in enumerate(plot_data): |
if i: |
plt.plot(future, plot_data[i], marker[i], markersize=10, label=labels[i]) |
else: |
plt.plot(time_steps, plot_data[i].flatten(), marker[i], label=labels[i]) |
plt.legend() |
plt.xlim([time_steps[0], (future + 5) * 2]) |
plt.xlabel(\"Time-Step\") |
plt.show() |
return |
for x, y in dataset_val.take(5): |
show_plot( |
[x[0][:, 1].numpy(), y[0].numpy(), model.predict(x)[0]], |
12, |
\"Single Step Prediction\", |
) |
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