pdfcoffee

accuracy: 0.9962 - val_loss: 0.7770 - val_accuracy: 0.7500
Epoch 10/10
29/29 [==============================] - 3s 99ms/step - loss: 0.0062 -
accuracy: 0.9988 - val_loss: 0.8344 - val_accuracy: 0.7450
Chapter 8
Figure 5: Accuracy and loss plots from TensorBoard for sentiment analysis network training
Our checkpoint callback has saved the best model based on the lowest value of
validation loss, and we can now reload this for evaluation against our held out test
set:
best_model = SentimentAnalysisModel(vocab_size+1, max_seqlen)
best_model.build(input_shape=(batch_size, max_seqlen))
best_model.load_weights(best_model_file)
best_model.compile(
loss="binary_crossentropy",
optimizer="adam",
metrics=["accuracy"]
)
The easiest high-level way to evaluate a model against a dataset is to use the model.
evaluate() call:
test_loss, test_acc = best_model.evaluate(test_dataset)
print("test loss: {:.3f}, test accuracy: {:.3f}".format(
test_loss, test_acc))
This gives us the following output:
test loss: 0.487, test accuracy: 0.782
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