背景资料：
我编写了一个 TensorFlow 模型，与 TensorFlow 提供的预制虹膜分类模型非常相似。差异相对较小：
- 我正在对足球练习进行分类，而不是虹膜种类。
- 我有 10 个功能和一个标签，而不是 4 个功能和一个标签。
- 我有 5 种不同的练习，而不是 3 种鸢尾花。
- 我的 trainData 包含大约 3500 行，而不仅仅是 120。
- 我的 testData 包含大约 330 行，而不仅仅是 30。
- 我正在使用 n_classes=6，而不是 3 的 DNN 分类器。
我现在想将模型导出为.tflite文件。但是根据TensorFlow Developer Guide，我需要先将模型导出到tf.GraphDef文件中，然后将其冻结，然后才能进行转换。但是， TensorFlow 提供的从自定义模型创建文件的教程.pb似乎只针对图像分类模型进行了优化。
问题：
那么如何将虹膜分类示例模型之类的模型转换为.tflite文件呢？有没有更简单、更直接的方法来做到这一点，而不必将其导出到.pb文件中，然后将其冻结等等？基于虹膜分类代码的示例或指向更明确教程的链接将非常有用！
其他信息：
- 操作系统：macOS 10.13.4 High Sierra
- TensorFlow 版本：1.8.0
- Python版本：3.6.4
- 使用 PyCharm 社区 2018.1.3
代码：
可以通过输入以下命令来克隆虹膜分类代码：
git clone https://github.com/tensorflow/models
但如果你不想下载整个包，这里是：
这是名为的分类器文件premade_estimator.py：
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""An Example of a DNNClassifier for the Iris dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import tensorflow as tf
import iris_data
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', default=100, type=int, help='batch size')
parser.add_argument('--train_steps', default=1000, type=int,
help='number of training steps')
def main(argv):
args = parser.parse_args(argv[1:])
# Fetch the data
(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))
template = '\nPrediction is "{}" ({:.1f}%), expected "{}"'
for pred_dict, expec in zip(predictions, 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))
if __name__ == '__main__':
# tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run(main)
这是名为的数据文件iris_data.py：
import pandas as pd
import tensorflow as tf
TRAIN_URL = "http://download.tensorflow.org/data/iris_training.csv"
TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"
CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth',
'PetalLength', 'PetalWidth', 'Species']
SPECIES = ['Setosa', 'Versicolor', 'Virginica']
def maybe_download():
train_path = tf.keras.utils.get_file(TRAIN_URL.split('/')[-1], TRAIN_URL)
test_path = tf.keras.utils.get_file(TEST_URL.split('/')[-1], TEST_URL)
return train_path, test_path
def load_data(y_name='Species'):
"""Returns the iris dataset as (train_x, train_y), (test_x, test_y)."""
train_path, test_path = maybe_download()
train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)
train_x, train_y = train, train.pop(y_name)
test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)
test_x, test_y = test, test.pop(y_name)
return (train_x, train_y), (test_x, test_y)
def train_input_fn(features, labels, batch_size):
"""An input function for training"""
# Convert the inputs to a Dataset.
dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
# Shuffle, repeat, and batch the examples.
dataset = dataset.shuffle(1000).repeat().batch(batch_size)
# Return the dataset.
return dataset
def eval_input_fn(features, labels, batch_size):
"""An input function for evaluation or prediction"""
features = dict(features)
if labels is None:
# No labels, use only features.
inputs = features
else:
inputs = (features, labels)
# Convert the inputs to a Dataset.
dataset = tf.data.Dataset.from_tensor_slices(inputs)
# Batch the examples
assert batch_size is not None, "batch_size must not be None"
dataset = dataset.batch(batch_size)
# Return the dataset.
return dataset
**更新**
好的，所以我在此页面上找到了一段看似非常有用的代码：
import tensorflow as tf
img = tf.placeholder(name="img", dtype=tf.float32, shape=(1, 64, 64, 3))
val = img + tf.constant([1., 2., 3.]) + tf.constant([1., 4., 4.])
out = tf.identity(val, name="out")
with tf.Session() as sess:
tflite_model = tf.contrib.lite.toco_convert(sess.graph_def, [img], [out])
open("test.tflite", "wb").write(tflite_model)
这个小家伙直接把一个简单的模型转换成一个TensorFlow Lite Model。现在我所要做的就是找到一种方法来适应虹膜分类模型。有什么建议么？