def setUpClass(cls):
cls.model_dir = tempfile.TemporaryDirectory()
cls.model = DNNClassifierModel(
DNNClassifierModelConfig(directory=cls.model_dir.name,
steps=1000,
epochs=30,
hidden=[10, 20, 10],
classification="string",
classifications=["a", "not a"],
clstype=str))
cls.feature = StartsWithA()
cls.features = Features(cls.feature)
cls.repos = [
Repo(
"a" + str(random.random()),
data={"features": {
cls.feature.NAME: 1,
"string": "a"
}},
) for _ in range(0, 1000)
]
cls.repos += [
Repo(
"b" + str(random.random()),
data={"features": {
cls.feature.NAME: 0,
"string": "not a"
}},
) for _ in range(0, 1000)
]
cls.sources = Sources(MemorySource(
MemorySourceConfig(repos=cls.repos)))
def setUpClass(cls):
cls.model_dir = tempfile.TemporaryDirectory()
cls.feature = Feature("starts_with_a", int, 1)
cls.features = Features(cls.feature)
cls.records = [
Record(
"a" + str(random.random()),
data={"features": {
cls.feature.name: 1,
"string": "a"
}},
) for _ in range(0, 1000)
]
cls.records += [
Record(
"b" + str(random.random()),
data={"features": {
cls.feature.name: 0,
"string": "not a"
}},
) for _ in range(0, 1000)
]
cls.sources = Sources(
MemorySource(MemorySourceConfig(records=cls.records)))
cls.model = DNNClassifierModel(
DNNClassifierModelConfig(
directory=cls.model_dir.name,
steps=1000,
epochs=40,
hidden=[50, 20, 10],
predict=Feature("string", str, 1),
classifications=["a", "not a"],
clstype=str,
features=cls.features,
))
from dffml import CSVSource, Features, DefFeature
from dffml.noasync import train, accuracy, predict
from dffml_model_tensorflow.dnnc import DNNClassifierModel
model = DNNClassifierModel(
features=Features(
DefFeature("SepalLength", float, 1),
DefFeature("SepalWidth", float, 1),
DefFeature("PetalLength", float, 1),
DefFeature("PetalWidth", float, 1),
),
predict=DefFeature("classification", int, 1),
epochs=3000,
steps=20000,
classifications=[0, 1, 2],
clstype=int,
)
# Train the model
train(model, "iris_training.csv")
# Assess accuracy (alternate way of specifying data source)
print("Accuracy:", accuracy(model, CSVSource(filename="iris_test.csv")))
# Make prediction
for i, features, prediction in predict(
model,
{
"PetalLength": 4.2,
"PetalWidth": 1.5,
"SepalLength": 5.9,
from dffml import CSVSource, Features, Feature
from dffml.noasync import train, accuracy, predict
from dffml_model_tensorflow.dnnc import DNNClassifierModel
model = DNNClassifierModel(
features=Features(
Feature("SepalLength", float, 1),
Feature("SepalWidth", float, 1),
Feature("PetalLength", float, 1),
Feature("PetalWidth", float, 1),
),
predict=Feature("classification", int, 1),
epochs=3000,
steps=20000,
classifications=[0, 1, 2],
clstype=int,
directory="tempdir",
)
# Train the model
train(model, "iris_training.csv")
# Assess accuracy (alternate way of specifying data source)
print("Accuracy:", accuracy(model, CSVSource(filename="iris_test.csv")))
# Make prediction
for i, features, prediction in predict(
model,
{
"PetalLength": 4.2,
"PetalWidth": 1.5,
