华为云AI开发平台ModelArtsTensorFlow_云淘科技

TensorFlow存在两种接口类型,keras接口和tf接口,其训练和保存模型的代码存在差异,但是推理代码编写方式一致。

训练模型(keras接口)

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
from keras.models import Sequential
model = Sequential()
from keras.layers import Dense
import tensorflow as tf

# 导入训练数据集
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

print(x_train.shape)

from keras.layers import Dense
from keras.models import Sequential
import keras
from keras.layers import Dense, Activation, Flatten, Dropout

# 定义模型网络
model = Sequential()
model.add(Flatten(input_shape=(28,28)))
model.add(Dense(units=5120,activation='relu'))
model.add(Dropout(0.2))

model.add(Dense(units=10, activation='softmax'))

# 定义优化器,损失函数等
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.summary()
# 训练
model.fit(x_train, y_train, epochs=2)
# 评估
model.evaluate(x_test, y_test)

保存模型(keras接口)

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
from keras import backend as K  

# K.get_session().run(tf.global_variables_initializer())

# 定义预测接口的inputs和outputs
# inputs和outputs字典的key值会作为模型输入输出tensor的索引键
# 模型输入输出定义需要和推理自定义脚本相匹配
predict_signature = tf.saved_model.signature_def_utils.predict_signature_def(
    inputs={"images" : model.input},
    outputs={"scores" : model.output}
)

# 定义保存路径
builder = tf.saved_model.builder.SavedModelBuilder('./mnist_keras/')

builder.add_meta_graph_and_variables(

    sess = K.get_session(),
    # 推理部署需要定义tf.saved_model.tag_constants.SERVING标签
    tags=[tf.saved_model.tag_constants.SERVING],
    """
    signature_def_map:items只能有一个,或者需要定义相应的key为
    tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
    """
    signature_def_map={
        tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
            predict_signature
    }

)
builder.save()

训练模型(tf接口)

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
from __future__ import print_function

import gzip
import os
import urllib

import numpy
import tensorflow as tf
from six.moves import urllib

# 训练数据来源于yann lecun官方网站http://yann.lecun.com/exdb/mnist/
SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
VALIDATION_SIZE = 5000


def maybe_download(filename, work_directory):
    """Download the data from Yann's website, unless it's already here."""
    if not os.path.exists(work_directory):
        os.mkdir(work_directory)
    filepath = os.path.join(work_directory, filename)
    if not os.path.exists(filepath):
        filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
        statinfo = os.stat(filepath)
        print('Successfully downloaded %s %d bytes.' % (filename, statinfo.st_size))
    return filepath


def _read32(bytestream):
    dt = numpy.dtype(numpy.uint32).newbyteorder('>')
    return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]


def extract_images(filename):
    """Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
    print('Extracting %s' % filename)
    with gzip.open(filename) as bytestream:
        magic = _read32(bytestream)
        if magic != 2051:
            raise ValueError(
                'Invalid magic number %d in MNIST image file: %s' %
                (magic, filename))
        num_images = _read32(bytestream)
        rows = _read32(bytestream)
        cols = _read32(bytestream)
        buf = bytestream.read(rows * cols * num_images)
        data = numpy.frombuffer(buf, dtype=numpy.uint8)
        data = data.reshape(num_images, rows, cols, 1)
        return data


def dense_to_one_hot(labels_dense, num_classes=10):
    """Convert class labels from scalars to one-hot vectors."""
    num_labels = labels_dense.shape[0]
    index_offset = numpy.arange(num_labels) * num_classes
    labels_one_hot = numpy.zeros((num_labels, num_classes))
    labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
    return labels_one_hot


def extract_labels(filename, one_hot=False):
    """Extract the labels into a 1D uint8 numpy array [index]."""
    print('Extracting %s' % filename)
    with gzip.open(filename) as bytestream:
        magic = _read32(bytestream)
        if magic != 2049:
            raise ValueError(
                'Invalid magic number %d in MNIST label file: %s' %
                (magic, filename))
        num_items = _read32(bytestream)
        buf = bytestream.read(num_items)
        labels = numpy.frombuffer(buf, dtype=numpy.uint8)
        if one_hot:
            return dense_to_one_hot(labels)
        return labels


class DataSet(object):
    """Class encompassing test, validation and training MNIST data set."""

    def __init__(self, images, labels, fake_data=False, one_hot=False):
        """Construct a DataSet. one_hot arg is used only if fake_data is true."""

        if fake_data:
            self._num_examples = 10000
            self.one_hot = one_hot
        else:
            assert images.shape[0] == labels.shape[0], (
                    'images.shape: %s labels.shape: %s' % (images.shape,
                                                           labels.shape))
            self._num_examples = images.shape[0]

            # Convert shape from [num examples, rows, columns, depth]
            # to [num examples, rows*columns] (assuming depth == 1)
            assert images.shape[3] == 1
            images = images.reshape(images.shape[0],
                                    images.shape[1] * images.shape[2])
            # Convert from [0, 255] -> [0.0, 1.0].
            images = images.astype(numpy.float32)
            images = numpy.multiply(images, 1.0 / 255.0)
        self._images = images
        self._labels = labels
        self._epochs_completed = 0
        self._index_in_epoch = 0

    @property
    def images(self):
        return self._images

    @property
    def labels(self):
        return self._labels

    @property
    def num_examples(self):
        return self._num_examples

    @property
    def epochs_completed(self):
        return self._epochs_completed

    def next_batch(self, batch_size, fake_data=False):
        """Return the next `batch_size` examples from this data set."""
        if fake_data:
            fake_image = [1] * 784
            if self.one_hot:
                fake_label = [1] + [0] * 9
            else:
                fake_label = 0
            return [fake_image for _ in range(batch_size)], [
                fake_label for _ in range(batch_size)
            ]
        start = self._index_in_epoch
        self._index_in_epoch += batch_size
        if self._index_in_epoch > self._num_examples:
            # Finished epoch
            self._epochs_completed += 1
            # Shuffle the data
            perm = numpy.arange(self._num_examples)
            numpy.random.shuffle(perm)
            self._images = self._images[perm]
            self._labels = self._labels[perm]
            # Start next epoch
            start = 0
            self._index_in_epoch = batch_size
            assert batch_size <= self._num_examples
        end = self._index_in_epoch
        return self._images[start:end], self._labels[start:end]


def read_data_sets(train_dir, fake_data=False, one_hot=False):
    """Return training, validation and testing data sets."""

    class DataSets(object):
        pass

    data_sets = DataSets()

    if fake_data:
        data_sets.train = DataSet([], [], fake_data=True, one_hot=one_hot)
        data_sets.validation = DataSet([], [], fake_data=True, one_hot=one_hot)
        data_sets.test = DataSet([], [], fake_data=True, one_hot=one_hot)
        return data_sets

    local_file = maybe_download(TRAIN_IMAGES, train_dir)
    train_images = extract_images(local_file)

    local_file = maybe_download(TRAIN_LABELS, train_dir)
    train_labels = extract_labels(local_file, one_hot=one_hot)

    local_file = maybe_download(TEST_IMAGES, train_dir)
    test_images = extract_images(local_file)

    local_file = maybe_download(TEST_LABELS, train_dir)
    test_labels = extract_labels(local_file, one_hot=one_hot)

    validation_images = train_images[:VALIDATION_SIZE]
    validation_labels = train_labels[:VALIDATION_SIZE]
    train_images = train_images[VALIDATION_SIZE:]
    train_labels = train_labels[VALIDATION_SIZE:]

    data_sets.train = DataSet(train_images, train_labels)
    data_sets.validation = DataSet(validation_images, validation_labels)
    data_sets.test = DataSet(test_images, test_labels)
    return data_sets

training_iteration = 1000

modelarts_example_path =  './modelarts-mnist-train-save-deploy-example'

export_path = modelarts_example_path + '/model/'
data_path = './'

print('Training model...')
mnist = read_data_sets(data_path, one_hot=True)
sess = tf.InteractiveSession()
serialized_tf_example = tf.placeholder(tf.string, name='tf_example')
feature_configs = {'x': tf.FixedLenFeature(shape=[784], dtype=tf.float32), }
tf_example = tf.parse_example(serialized_tf_example, feature_configs)
x = tf.identity(tf_example['x'], name='x')  # use tf.identity() to assign name
y_ = tf.placeholder('float', shape=[None, 10])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
sess.run(tf.global_variables_initializer())
y = tf.nn.softmax(tf.matmul(x, w) + b, name='y')
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
values, indices = tf.nn.top_k(y, 10)
table = tf.contrib.lookup.index_to_string_table_from_tensor(
    tf.constant([str(i) for i in range(10)]))
prediction_classes = table.lookup(tf.to_int64(indices))
for _ in range(training_iteration):
    batch = mnist.train.next_batch(50)
    train_step.run(feed_dict={x: batch[0], y_: batch[1]})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
print('training accuracy %g' % sess.run(
    accuracy, feed_dict={
        x: mnist.test.images,
        y_: mnist.test.labels
    }))
print('Done training!')

保存模型(tf接口)

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
# 导出模型
# 模型需要采用saved_model接口保存
print('Exporting trained model to', export_path)
builder = tf.saved_model.builder.SavedModelBuilder(export_path)

tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
tensor_info_y = tf.saved_model.utils.build_tensor_info(y)

# 定义预测接口的inputs和outputs
# inputs和outputs字典的key值会作为模型输入输出tensor的索引键
# 模型输入输出定义需要和推理自定义脚本相匹配
prediction_signature = (
    tf.saved_model.signature_def_utils.build_signature_def(
        inputs={'images': tensor_info_x},
        outputs={'scores': tensor_info_y},
        method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))

legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
builder.add_meta_graph_and_variables(
    # tag设为serve/tf.saved_model.tag_constants.SERVING
    sess, [tf.saved_model.tag_constants.SERVING],
    signature_def_map={
        'predict_images':
            prediction_signature,
    },
    legacy_init_op=legacy_init_op)

builder.save()

print('Done exporting!')

推理代码(keras接口和tf接口)

在模型代码推理文件customize_service.py中,需要添加一个子类,该子类继承对应模型类型的父类,各模型类型的父类名称和导入语句如请参考表1。本案例中调用父类“_inference(self, data)”推理请求方法,因此下文代码中不需要重写方法。

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
from PIL import Image
import numpy as np
from model_service.tfserving_model_service import TfServingBaseService


class MnistService(TfServingBaseService):

    # 预处理中处理用户HTTPS接口输入匹配模型输入
    # 对应上述训练部分的模型输入为{"images":}
    def _preprocess(self, data):

        preprocessed_data = {}
        images = []
        # 对输入数据进行迭代
        for k, v in data.items():
            for file_name, file_content in v.items():
                image1 = Image.open(file_content)
                image1 = np.array(image1, dtype=np.float32)
                image1.resize((1,784))
                images.append(image1)
        # 返回numpy array
        images = np.array(images,dtype=np.float32)
        # 对传入的多个样本做batch处理,shape保持和训练时输入一致
        images.resize((len(data), 784))
        preprocessed_data['images'] = images
        return preprocessed_data

    # 对应的上述训练部分保存模型的输出为{"scores":}
    # 后处理中处理模型输出为HTTPS的接口输出
    def _postprocess(self, data):
        infer_output = {"mnist_result": []}
        # 迭代处理模型输出
        for output_name, results in data.items():
            for result in results:
                infer_output["mnist_result"].append(result.index(max(result)))
        return infer_output

父主题: 自定义脚本代码示例

同意关联代理商云淘科技,购买华为云产品更优惠(QQ 78315851)

内容没看懂? 不太想学习?想快速解决? 有偿解决: 联系专家