我是TensorFlow的新手。我正在尝试使用Google ML引擎上的估算器构建和提供模型。但是，我不确定如何在尝试几种方法后保存模型以服务。
我已经以可接受的准确性成功地训练了该模型。当我试图保存模型以进行服务时，我四处搜索并找到了几种方法。但是，我仍然遇到许多问题...
我尝试了三种基于对其他一些问题的建议出口方式：
1(获取一个序列化示例作为输入 - 我遇到了一个错误" typeError：类型字节的对象不是JSON序列化"。另外，我找不到一个很好的方法来喂养有效服务的序列化示例。当我使用ML引擎进行服务时，似乎更容易使用JSON输入。
2(将JSON作为"基本"预处理的输入 - 我能够成功导出该模型。将模型加载到ML引擎后，我尝试做出一些预测。尽管返回了预测结果，但我发现，无论我如何更改JSON输入，都将返回相同的结果。我查看了培训期间获得的验证结果。该模型应该能够返回各种结果。我认为服务功能中的预处理有问题，所以我尝试了第三种方法...
3(JSON输入具有"相同"预处理 - 我无法解决这个问题，但是我认为可能需要与在模型培训期间处理数据的处理方式完全相同。但是，由于服务输入功能使用TF。地点持有人，我不知道如何复制相同的预处理以使导出的模型起作用...
(请原谅我的不良编码样式...(
培训代码：
col_names = ['featureA','featureB','featureC']
target_name = 'langIntel'
col_def = {}
col_def['featureA'] = {'type':'float','tfType':tf.float32,'len':'fixed'}
col_def['featureB'] = {'type':'int','tfType':tf.int64,'len':'fixed'}
col_def['featureC'] = {'type':'bytes','tfType':tf.string,'len':'var'}
def _float_feature(value):
if not isinstance(value, list): value = [value]
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def _int_feature(value):
if not isinstance(value, list): value = [value]
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
def _bytes_feature(value):
if not isinstance(value, list): value = [value]
return tf.train.Feature(
bytes_list=tf.train.BytesList(
value=[p.encode('utf-8') for p in value]
)
)
functDict = {'float':_float_feature,
'int':_int_feature,'bytes':_bytes_feature
}
training_targets = []
# Omitted validatin partition
with open('[JSON FILE PATH]') as jfile:
json_data_input = json.load(jfile)
random.shuffle(json_data_input)
with tf.python_io.TFRecordWriter('savefile1.tfrecord') as writer:
for item in json_data_input:
if item[target_name] > 0:
feature = {}
for col in col_names:
feature[col] = functDict[col_def[col]['type']](item[col])
training_targets.append(item[target_name])
example = tf.train.Example(
features=tf.train.Features(feature=feature)
)
writer.write(example.SerializeToString())
def _parse_function(example_proto):
example = {}
for col in col_names:
if col_def[col]['len'] == 'fixed':
example[col] = tf.FixedLenFeature([], col_def[col]['tfType'])
else:
example[col] = tf.VarLenFeature(col_def[col]['tfType'])
parsed_example = tf.parse_single_example(example_proto, example)
features = {}
for col in col_names:
features[col] = parsed_example[col]
labels = parsed_example.get(target_name)
return features, labels
def my_input_fn(batch_size=1,num_epochs=None):
dataset = tf.data.TFRecordDataset('savefile1.tfrecord')
dataset = dataset.map(_parse_function)
dataset = dataset.shuffle(10000)
dataset = dataset.repeat(num_epochs)
dataset = dataset.batch(batch_size)
iterator = dataset.make_one_shot_iterator()
features, labels = iterator.get_next()
return features, labels
allColumns = None
def train_model(
learning_rate,
n_trees,
n_batchespl,
batch_size):
periods = 10
vocab_list = ('vocab1', 'vocab2', 'vocab3')
featureA_bucket = tf.feature_column.bucketized_column(
tf.feature_column.numeric_column(
key="featureA",dtype=tf.int64
), [5,10,15]
)
featureB_bucket = tf.feature_column.bucketized_column(
tf.feature_column.numeric_column(
key="featureB",dtype=tf.float32
), [0.25,0.5,0.75]
)
featureC_cat = tf.feature_column.indicator_column(
tf.feature_column.categorical_column_with_vocabulary_list(
key="featureC",vocabulary_list=vocab_list,
num_oov_buckets=1
)
)
theColumns = [featureA_bucket,featureB_bucket,featureC_cat]
global allColumns
allColumns = theColumns
regressor = tf.estimator.BoostedTreesRegressor(
feature_columns=theColumns,
n_batches_per_layer=n_batchespl,
n_trees=n_trees,
learning_rate=learning_rate
)
training_input_fn = lambda: my_input_fn(batch_size=batch_size,num_epochs=5)
predict_input_fn = lambda: my_input_fn(num_epochs=1)
regressor.train(
input_fn=training_input_fn
)
# omitted evaluation part
return regressor
regressor = train_model(
learning_rate=0.05,
n_trees=100,
n_batchespl=50,
batch_size=20)
导出试验1：
def _serving_input_receiver_fn():
serialized_tf_example = tf.placeholder(dtype=tf.string, shape=None,
name='input_example_tensor'
)
receiver_tensors = {'examples': serialized_tf_example}
features = tf.parse_example(serialized_tf_example, feature_spec)
return tf.estimator.export.ServingInputReceiver(features,
receiver_tensors
)
servable_model_dir = "[OUT PATH]"
servable_model_path = regressor.export_savedmodel(servable_model_dir,
_serving_input_receiver_fn
)
导出试验2：
def serving_input_fn():
feature_placeholders = {
'featureA': tf.placeholder(tf.int64, [None]),
'featureB': tf.placeholder(tf.float32, [None]),
'featureC': tf.placeholder(tf.string, [None, None])
}
receiver_tensors = {'inputs': feature_placeholders}
feature_spec = tf.feature_column.make_parse_example_spec(allColumns)
features = tf.parse_example(feature_placeholders, feature_spec)
return tf.estimator.export.ServingInputReceiver(features,
feature_placeholders
)
servable_model_dir = "[OUT PATH]"
servable_model_path = regressor.export_savedmodel(
servable_model_dir, serving_input_fn
)
导出试验3：
def serving_input_fn():
feature_placeholders = {
'featureA': tf.placeholder(tf.int64, [None]),
'featureB': tf.placeholder(tf.float32, [None]),
'featureC': tf.placeholder(tf.string, [None, None])
}
def toBytes(t):
t = str(t)
return t.encode('utf-8')
tmpFeatures = {}
tmpFeatures['featureA'] = tf.train.Feature(
int64_list=feature_placeholders['featureA']
)
# TypeError: Parameter to MergeFrom() must be instance
# of same class: expected tensorflow.Int64List got Tensor.
tmpFeatures['featureB'] = tf.train.Feature(
float_list=feature_placeholders['featureB']
)
tmpFeatures['featureC'] = tf.train.Feature(
bytes_list=feature_placeholders['featureC']
)
tmpExample = tf.train.Example(
features=tf.train.Features(feature=tmpFeatures)
)
tmpExample_proto = tmpExample.SerializeToString()
example = {}
for key, tensor in feature_placeholders.items():
if col_def[key]['len'] == 'fixed':
example[key] = tf.FixedLenFeature(
[], col_def[key]['tfType']
)
else:
example[key] = tf.VarLenFeature(
col_def[key]['tfType']
)
parsed_example = tf.parse_single_example(
tmpExample_proto, example
)
features = {}
for key in tmpFeatures.keys():
features[key] = parsed_example[key]
return tf.estimator.export.ServingInputReceiver(
features, feature_placeholders
)
servable_model_dir = "[OUT PATH]"
servable_model_path = regressor.export_savedmodel(
servable_model_dir, serving_input_fn
)
为了使JSON文件被输入预测，应如何构造服务输入函数？非常感谢您的任何见解！
只是为了提供更新 - 我仍然无法完成导出。然后，我使用keras重建了培训模型，并成功地导出了服务模型(重建模型可能会更少的时间来弄清楚如何在我的情况下导出估计器模型...(