DNNClassifier模型到TensorFlow服务模型

Question

我是ML和TF的新手，我正在尝试使用TensorFlow服务在GCP上托管原始的TensorFlow模型。为此，我需要将DNNClassifier模型转换为TensorFlow服务模型。根据Get Started指南，我需要使用SavedModelBuilder方法，但我无法弄清楚如何在Iris Flower example的情况下定义输入/输出。

有人可以为这个案例发布一个示例代码吗？

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))

Answer 1

在训练和评估模型后，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，输出是三个类之一。对于Builder，输入为'tf session,tag_constants.SERVINGandsignature_def_map`，输出为'Desired_Directory / saved_model.pb'

Answer 2

只需将节奏模式更改为张量样式可能必须交叉合并样式然后使用格式均衡器进行调整。