我是 ML 和 TF 方面的新手,我正在尝试使用 TensorFlow Serving 在 GCP 上托管原始 TensorFlow 模型。为此,我需要将 DNNClassifier 模型转换为 TensorFlow Serving 模型。根据Get Started我需要使用的指南
SavedModelBuilder
方法,但我不知道如何在 Iris Flower example 的情况下定义输入/输出.
有人可以发布此案例的示例代码吗?
完整代码:
(train_x, train_y), (test_x, test_y) = iris_data.load_data()
# Feature columns describe how to use the input.
my_feature_columns = []
for key in train_x.keys():
my_feature_columns.append(tf.feature_column.numeric_column(key=key))
# Build 2 hidden layer DNN with 10, 10 units respectively.
classifier = tf.estimator.DNNClassifier(
feature_columns=my_feature_columns,
# Two hidden layers of 10 nodes each.
hidden_units=[10, 10],
# The model must choose between 3 classes.
n_classes=3)
# Train the Model.
classifier.train(
input_fn=lambda:iris_data.train_input_fn(train_x, train_y,
args.batch_size),
steps=args.train_steps)
# Evaluate the model.
eval_result = classifier.evaluate(
input_fn=lambda:iris_data.eval_input_fn(test_x, test_y,
args.batch_size))
print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))
# Generate predictions from the model
expected = ['Setosa', 'Versicolor', 'Virginica']
predict_x = {
'SepalLength': [5.1, 5.9, 6.9],
'SepalWidth': [3.3, 3.0, 3.1],
'PetalLength': [1.7, 4.2, 5.4],
'PetalWidth': [0.5, 1.5, 2.1],
}
predictions = classifier.predict(
input_fn=lambda:iris_data.eval_input_fn(predict_x,
labels=None,
batch_size=args.batch_size))
for pred_dict, expec in zip(predictions, expected):
template = ('\nPrediction is "{}" ({:.1f}%), expected "{}"')
class_id = pred_dict['class_ids'][0]
probability = pred_dict['probabilities'][class_id]
print(template.format(iris_data.SPECIES[class_id],
100 * probability, expec))
最佳答案
训练和评估模型后,您就可以保存模型。
(train_x, train_y), (test_x, test_y) = iris_data.load_data()
# Feature columns describe how to use the input.
my_feature_columns = []
for key in train_x.keys():
my_feature_columns.append(tf.feature_column.numeric_column(key=key))
# Build 2 hidden layer DNN with 10, 10 units respectively.
classifier = tf.estimator.DNNClassifier(
feature_columns=my_feature_columns,
# Two hidden layers of 10 nodes each.
hidden_units=[10, 10],
# The model must choose between 3 classes.
n_classes=3)
# Train the Model.
classifier.train(
input_fn=lambda:iris_data.train_input_fn(train_x, train_y,
args.batch_size),
steps=args.train_steps)
# Evaluate the model.
eval_result = classifier.evaluate(
input_fn=lambda:iris_data.eval_input_fn(test_x, test_y,
args.batch_size))
export_path = 'Your Desired new Path '
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
sess = tf.InteractiveSession()
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING]
builder.save()
根据您的应用程序,您还可以将signature_def_map
添加到builder.add_meta_graph_and_variables()函数。
请注意,对于分类器,输入是 feature_columns,输出是三个类别之一。对于 Builder,输入为“tf session,
tag_constants.SERVINGand
signature_def_map`,输出为“Desired_Directory/saved_model.pb”
关于python - DNNClassifier 模型到 TensorFlow Serving 模型,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/48940091/