context("Test training methods")
source("helper-utils.R")
test_succeeds("train_and_evaluate() work for canned estimators", {
skip_if_tensorflow_below("1.4")
specs <- mtcars_regression_specs()
est <- dnn_linear_combined_regressor(
linear_feature_columns = specs$linear_feature_columns,
dnn_feature_columns = specs$dnn_feature_columns,
dnn_hidden_units = c(1L, 1L),
dnn_optimizer = "Adagrad"
)
tr_spec <- train_spec(input_fn = specs$input_fn, max_steps = 10)
ev_spec <- eval_spec(input_fn = specs$input_fn, steps = 2)
train_and_evaluate(
est,
train_spec = tr_spec,
eval_spec = ev_spec
)
})
test_succeeds("train_and_evaluate() work for custom estimators", {
skip_if_tensorflow_below("1.4")
input <- input_fn(
object = iris,
response = "Species",
features = c(
"Sepal.Length",
"Sepal.Width",
"Petal.Length",
"Petal.Width"),
batch_size = 10L
)
est <- estimator(model_fn = simple_custom_model_fn)
tr_spec <- train_spec(input_fn = input, max_steps = 2)
ev_spec <- eval_spec(input_fn = input, steps = 2)
train_and_evaluate(
est,
train_spec = tr_spec,
eval_spec = ev_spec
)
})
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