如果你想要推理，但不使用 tensorflow-serving，你可以使用tf.estimator.Estimator predict 方法。
但是，如果您想手动执行（这样运行速度更快），您需要一种解决方法。我不确定我所做的是否是最好的方法，但它确实有效。这是我的解决方案。
1）让我们进行导入并创建变量和假数据：
import os
import numpy as np
from functools import partial
import pickle
import tensorflow as tf
N = 10000
EPOCHS = 1000
BATCH_SIZE = 2
X_data = np.random.random((N, 10))
y_data = (np.random.random((N, 1)) >= 0.5).astype(int)
my_dir = os.getcwd() + "/"
2) 定义一个 input_fn，您将使用tf.data.Dataset。将张量名称保存在字典（“input_tensor_map”）中，它将输入键映射到张量名称。
def my_input_fn(X, y=None, is_training=False):
def internal_input_fn(X, y=None, is_training=False):
if (not isinstance(X, dict)):
X = {"x": X}
if (y is None):
dataset = tf.data.Dataset.from_tensor_slices(X)
else:
dataset = tf.data.Dataset.from_tensor_slices((X, y))
if (is_training):
dataset = dataset.repeat().shuffle(100)
batch_size = BATCH_SIZE
else:
batch_size = 1
dataset = dataset.batch(batch_size)
dataset_iter = dataset.make_initializable_iterator()
if (y is None):
features = dataset_iter.get_next()
labels = None
else:
features, labels = dataset_iter.get_next()
input_tensor_map = dict()
for input_name, tensor in features.items():
input_tensor_map[input_name] = tensor.name
with open(os.path.join(my_dir, 'input_tensor_map.pickle'), 'wb') as f:
pickle.dump(input_tensor_map, f, protocol=pickle.HIGHEST_PROTOCOL)
tf.add_to_collection(tf.GraphKeys.TABLE_INITIALIZERS, dataset_iter.initializer)
return (features, labels) if (not labels is None) else features
return partial(internal_input_fn, X=X, y=y, is_training=is_training)
3）定义您的模型，以在您的tf.estimator.Estimator中使用。例如：
def my_model_fn(features, labels, mode):
output = tf.layers.dense(inputs=features["x"], units=1, activation=None)
logits = tf.identity(output, name="logits")
prediction = tf.nn.sigmoid(logits, name="predictions")
classes = tf.to_int64(tf.greater(logits, 0.0), name="classes")
predictions_dict = {
"class": classes,
"probabilities": prediction
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions_dict)
one_hot_labels = tf.squeeze(tf.one_hot(tf.cast(labels, dtype=tf.int32), 2))
loss = tf.losses.sigmoid_cross_entropy(multi_class_labels=one_hot_labels, logits=logits)
tf.summary.scalar("loss", loss)
accuracy = tf.reduce_mean(tf.to_float(tf.equal(labels, classes)))
tf.summary.scalar("accuracy", accuracy)
# Configure the Training Op (for TRAIN mode)
if (mode == tf.estimator.ModeKeys.TRAIN):
train_op = tf.train.AdamOptimizer().minimize(loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
return tf.estimator.EstimatorSpec(mode=mode, loss=loss)
4）训练和冻结你的模型。freeze 方法来自TensorFlow：How to freeze a model and serve it with a python API，我添加了一个微小的修改。
def freeze_graph(output_node_names):
"""Extract the sub graph defined by the output nodes and convert
all its variables into constant
Args:
model_dir: the root folder containing the checkpoint state file
output_node_names: a string, containing all the output node's names,
comma separated
"""
if (output_node_names is None):
output_node_names = 'loss'
if not tf.gfile.Exists(my_dir):
raise AssertionError(
"Export directory doesn't exists. Please specify an export "
"directory: %s" % my_dir)
if not output_node_names:
print("You need to supply the name of a node to --output_node_names.")
return -1
# We retrieve our checkpoint fullpath
checkpoint = tf.train.get_checkpoint_state(my_dir)
input_checkpoint = checkpoint.model_checkpoint_path
# We precise the file fullname of our freezed graph
absolute_model_dir = "/".join(input_checkpoint.split('/')[:-1])
output_graph = absolute_model_dir + "/frozen_model.pb"
# We clear devices to allow TensorFlow to control on which device it will load operations
clear_devices = True
# We start a session using a temporary fresh Graph
with tf.Session(graph=tf.Graph()) as sess:
# We import the meta graph in the current default Graph
saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=clear_devices)
# We restore the weights
saver.restore(sess, input_checkpoint)
# We use a built-in TF helper to export variables to constants
output_graph_def = tf.graph_util.convert_variables_to_constants(
sess, # The session is used to retrieve the weights
tf.get_default_graph().as_graph_def(), # The graph_def is used to retrieve the nodes
output_node_names.split(",") # The output node names are used to select the usefull nodes
)
# Finally we serialize and dump the output graph to the filesystem
with tf.gfile.GFile(output_graph, "wb") as f:
f.write(output_graph_def.SerializeToString())
print("%d ops in the final graph." % len(output_graph_def.node))
return output_graph_def
# *****************************************************************************
tf.logging.set_verbosity(tf.logging.INFO)
estimator = tf.estimator.Estimator(model_fn=my_model_fn, model_dir=my_dir)
if (estimator.latest_checkpoint() is None):
estimator.train(input_fn=my_input_fn(X=X_data, y=y_data, is_training=True), steps=EPOCHS)
freeze_graph("predictions,classes")
tf.logging.set_verbosity(tf.logging.INFO)
estimator = tf.estimator.Estimator(model_fn=my_model_fn, model_dir=my_dir)
if (estimator.latest_checkpoint() is None):
estimator.train(input_fn=my_input_fn(X=X_data, y=y_data, is_training=True), steps=EPOCHS)
freeze_graph("predictions,classes")
5）最后，您可以使用冻结图进行推理，输入张量名称在您保存的字典中。同样，从TensorFlow 加载冻结模型的方法：如何冻结模型并使用 python API 提供服务。
def load_frozen_graph(prefix="frozen_graph"):
frozen_graph_filename = os.path.join(my_dir, "frozen_model.pb")
# We load the protobuf file from the disk and parse it to retrieve the
# unserialized graph_def
with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
# Then, we import the graph_def into a new Graph and returns it
with tf.Graph().as_default() as graph:
# The name var will prefix every op/nodes in your graph
# Since we load everything in a new graph, this is not needed
tf.import_graph_def(graph_def, name=prefix)
return graph
# *****************************************************************************
X_test = {"x": np.random.random((int(N/2), 10))}
prefix = "frozen_graph"
graph = load_frozen_graph(prefix)
for op in graph.get_operations():
print(op.name)
with open(os.path.join(my_dir, 'input_tensor_map.pickle'), 'rb') as f:
input_tensor_map = pickle.load(f)
with tf.Session(graph=graph) as sess:
input_feed = dict()
for key, tensor_name in input_tensor_map.items():
tensor = graph.get_tensor_by_name(prefix + "/" + tensor_name)
input_feed[tensor] = X_test[key]
logits = graph.get_operation_by_name(prefix + "/logits").outputs[0]
probabilities = graph.get_operation_by_name(prefix + "/predictions").outputs[0]
classes = graph.get_operation_by_name(prefix + "/classes").outputs[0]
logits_values, probabilities_values, classes_values = sess.run([logits, probabilities, classes], feed_dict=input_feed)