def test_category_accuracy(self):
layer = tl.CategoryAccuracy()
targets = np.array([0, 1, 2])
model_outputs = np.array([[.7, .2, .1, 0.], [.2, .7, .1, 0.],
[.2, .1, .7, 0.]])
accuracy = layer([model_outputs, targets])
self.assertEqual(accuracy, 1.0)
model_outputs = np.array([[.2, .1, .7, 0.], [.2, .1, .7, 0.],
[.2, .1, .7, 0.]])
accuracy = layer([model_outputs, targets])
self.assertEqual(accuracy, 1 / 3)
train_data, len_input,
batch_size))
recommender = RecommenderTransformer(n_classes_in=len(classes),
embedding_size=embedding_size,
n_out_classes=len(classes),
dropout_rate=dropout_rate)
n_train_b = math.floor(float(len(train_data)) / float(batch_size))
train_task = ts.training.TrainTask(
labeled_data=inputs_train,
loss_layer=tl.CategoryCrossEntropy(),
optimizer=trax.optimizers.Adam(learning_rate),
n_steps_per_checkpoint=
400, #This will print the results at every 200 training steps.
)
n_eval_b = math.floor(float(len(val_data)) / float(batch_size))
# Evaluaton task.
eval_task = ts.training.EvalTask(
labeled_data=inputs_test,
metrics=[tl.CategoryCrossEntropy(),
tl.CategoryAccuracy()],
n_eval_batches=n_eval_b)
training_loop = ts.training.Loop(recommender,
train_task,
eval_tasks=[eval_task],
output_dir=out_folder)
# Run 2000 steps (batches).
training_loop.run(2000)