我是Tensorflow的新手。我正在尝试使用Google ML Engine上的Estimator建立并提供模型。但是，在尝试几种方法后，我不确定如何保存该模型以便投放。
我已经成功地以可接受的精度训练了模型。当我尝试保存要投放的模型时，我四处搜寻并找到了几种方法。但是，我仍然遇到许多问题...
我根据发布的其他一些问题的建议尝试了3种导出方式：
1）获取序列化示例作为输入-我遇到了一个错误“ TypeError：字节类型的对象不可JSON序列化的对象”。另外，我找不到找到有效服务的序列化示例的好方法。当我使用ML Engine进行投放时，使用JSON输入似乎会更容易。
2）通过“基本”预处理获取JSON作为输入-我能够成功导出模型。将模型加载到ML Engine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存在问题，因此我尝试了第三种方法...
3）JSON输入具有“相同”的预处理-我无法理解，但是我认为可能需要执行与模型训练期间处理数据完全相同的预处理。但是，由于服务输入函数使用了tf.placeholders，所以我不知道如何复制相同的预处理以使导出的模型正常工作。
（请原谅我糟糕的编码风格...）
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)
出口审判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
)
出口审判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
)
出口审判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
)
应如何构造服务输入功能，以便输入JSON文件进行预测？非常感谢您的见解！
答案 0 :(得分：0)
仅提供更新-我仍然无法完成导出。然后，我使用Keras重建了训练模型，并成功导出了服务模型（重建模型可能花费了我较少的时间来弄清楚如何导出我的情况下的估算器模型...）