async def test_00_train(self):
await train(
self.model,
DirectorySource(
foldername=str(self.traindir) + "/rps",
feature="image",
labels=["rock", "paper", "scissors"],
),
)
async def test_01_accuracy(self):
acc = await accuracy(
self.model,
DirectorySource(
foldername=str(self.testdir) + "/rps-test-set",
feature="image",
labels=["rock", "paper", "scissors"],
),
)
self.assertGreater(acc, 0)
async def test_02_predict(self):
target = self.model.config.predict.name
predict_value = 0
async for key, features, prediction in predict(
self.model,
DirectorySource(foldername=str(self.predictdir), feature="image",),
):
pred = prediction
break
self.assertTrue(pred)
self.assertTrue(pred[target])
results = pred[target]
self.assertIn("value", results)
self.assertIn("confidence", results)
self.assertIn(results["value"], self.model.config.classifications)
self.assertTrue(results["confidence"])
directory="rps_model",
network=RockPaperScissorsModel,
epochs=10,
batch_size=32,
imageSize=150,
validation_split=0.2,
loss=Loss,
optimizer="Adam",
enableGPU=True,
patience=2,
)
# Define source for training image dataset
train_source = DirectorySource(
foldername="rps",
feature="image",
labels=["rock", "paper", "scissors"],
)
# Define source for testing image dataset
test_source = DirectorySource(
foldername="rps-test-set",
feature="image",
labels=["rock", "paper", "scissors"],
)
# Define source for prediction image dataset
predict_source = DirectorySource(
foldername="rps-predict",
feature="image",
)