def testDNN(self, classes):
train_features, train_labels = test_utils.make_input_data(256, classes)
feature_columns = []
for key in train_features:
feature_columns.append(tf.feature_column.numeric_column(key=key))
optimizer = functools.partial(DPKerasSGDOptimizer,
learning_rate=0.5,
l2_norm_clip=1.0,
noise_multiplier=0.0,
num_microbatches=1)
classifier = dnn.DNNClassifier(hidden_units=[10],
activation_fn='relu',
feature_columns=feature_columns,
n_classes=classes,
optimizer=optimizer,
loss_reduction=tf.losses.Reduction.NONE)
classifier.train(input_fn=test_utils.make_input_fn(
train_features, train_labels, True, 16))
test_features, test_labels = test_utils.make_input_data(64, classes)
classifier.evaluate(input_fn=test_utils.make_input_fn(
test_features, test_labels, False, 16))
predict_features, predict_labels = test_utils.make_input_data(
64, classes)
classifier.predict(input_fn=test_utils.make_input_fn(
predict_features, predict_labels, False))
def testCreateTPUEstimatorSpec(self):
"""Tests that an Estimator built with this head works."""
train_features, train_labels = test_utils.make_input_data(256, 2)
feature_columns = []
for key in train_features:
feature_columns.append(tf.feature_column.numeric_column(key=key))
head = binary_class_head.DPBinaryClassHead()
optimizer = DPKerasSGDOptimizer(learning_rate=0.5,
l2_norm_clip=1.0,
noise_multiplier=0.0,
num_microbatches=2)
model_fn = test_utils.make_model_fn(head, optimizer, feature_columns)
classifier = tf.estimator.Estimator(model_fn=model_fn)
classifier.train(input_fn=test_utils.make_input_fn(
train_features, train_labels, True),
steps=4)
test_features, test_labels = test_utils.make_input_data(64, 2)
classifier.evaluate(input_fn=test_utils.make_input_fn(
test_features, test_labels, False),
steps=4)
predict_features, predict_labels_ = test_utils.make_input_data(64, 2)
classifier.predict(input_fn=test_utils.make_input_fn(
predict_features, predict_labels_, False))
def testCreateTPUEstimatorSpec(self, n_classes):
"""Tests that an Estimator built with a binary head works."""
train_features, train_labels = test_utils.make_input_data(
256, n_classes)
feature_columns = []
for key in train_features:
feature_columns.append(tf.feature_column.numeric_column(key=key))
head = head_lib._binary_logistic_or_multi_class_head(
n_classes=n_classes,
weight_column=None,
label_vocabulary=None,
loss_reduction=tf.compat.v1.losses.Reduction.NONE)
optimizer = DPGradientDescentGaussianOptimizer(learning_rate=0.5,
l2_norm_clip=1.0,
noise_multiplier=0.0,
num_microbatches=2)
model_fn = make_model_fn(head, optimizer, feature_columns)
classifier = tf_estimator.Estimator(model_fn=model_fn)
classifier.train(input_fn=test_utils.make_input_fn(
train_features, train_labels, True),
steps=4)
test_features, test_labels = test_utils.make_input_data(64, n_classes)
classifier.evaluate(input_fn=test_utils.make_input_fn(
test_features, test_labels, False),
steps=4)
predict_features, predict_labels = test_utils.make_input_data(
64, n_classes)
classifier.predict(input_fn=test_utils.make_input_fn(
predict_features, predict_labels, False))