Datasets:
HenryAI
/
KerasCodeExamples.txt

Dataset card Data Studio Files
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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\")
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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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