def run():
client = serving_grpc_client.GRPCClient('localhost:50051')
# 读取 PTB 数据集
print("Loading ptb data...")
train_data, valid_data, test_data, _ = reader.ptb_raw_data(FLAGS.data_path)
# 这里 PTB 的测试数据集的前 10 个单词来假设收到了一个单词序列，来预测第 11 个单词。
state = {}
logits = None
for i in range(10):
inputs = {
'input': tf.contrib.util.make_tensor_proto(test_data[i], shape=[1,1])
}
# 对于序列的第一个单词，采用模型初始状态。
# 对于非第一个单词，使用模型上一时刻的状态。
if i > 0:
for key in state:
inputs[key] = tf.contrib.util.make_tensor_proto(state[key])
outputs = client.call_predict(inputs)
# 模型的输出列表中，除了 logits 外，其他都是模型的状态。
for key in outputs:
if key == "logits":
logits = tf.contrib.util.make_ndarray(outputs[key])
else:
state[key] = tf.contrib.util.make_ndarray(outputs[key])
print('logits: {0}'.format(logits))
def run():
client = serving_grpc_client.GRPCClient('localhost:50051')
input_data = mnist.test.images[0]
input_data_shape = [1, mnist.test.images[0].size]
inputs = {
'image':
tf.contrib.util.make_tensor_proto(input_data, shape=input_data_shape),
}
outputs = client.call_predict(inputs)
result = tf.contrib.util.make_ndarray(outputs["logits"])
print('logits: {0}'.format(result))
def run():
client = serving_grpc_client.GRPCClient('localhost:50051')
feature_dict = {
k: _float_feature(prediction_set[k].values[0])
for k in FEATURES
}
example = tf.train.Example(features=tf.train.Features(
feature=feature_dict))
serialized = example.SerializeToString()
inputs = {
'inputs': tf.contrib.util.make_tensor_proto(serialized, shape=[1]),
}
outputs = client.call_predict(inputs)
# print(outputs)
result = tf.contrib.util.make_ndarray(outputs['outputs'])
print('Outputs: {}'.format(result))
def run():
client = serving_grpc_client.GRPCClient('localhost:50051')
print("""
#######################################
Case1: request.output_filter 为空，计算所有所有输出。
Request: x = 10
Expect: 10*0.5 + 2 = 7
#######################################""")
feature_dict = {'x': _float_feature(value=1.0)}
example = tf.train.Example(features=tf.train.Features(
feature=feature_dict))
serialized = example.SerializeToString()
inputs1 = {
'inputs': tf.contrib.util.make_tensor_proto(serialized, shape=[1]),
}
outputs1 = client.call_predict(inputs1)
print(outputs1)
result1 = tf.contrib.util.make_ndarray(outputs1['outputs'])
print('Response: y = {}'.format(result1))
def run():
client = serving_grpc_client.GRPCClient('localhost:50051')
print("""
#######################################
Case1: request.output_filter 为空，计算所有所有输出。
Request: x = 10
Expect: 10*0.5 + 2 = 7
#######################################""")
inputs1 = {
'x': tf.contrib.util.make_tensor_proto(10, shape=[]),
}
outputs1 = client.call_predict(inputs1)
result1 = tf.contrib.util.make_ndarray(outputs1['y'])
print('Response: y = {}'.format(result1))
print("""
#######################################
Case2: request.output_filter 包含正确的输出别名 y。
Request: x = 5
Expect: 5*0.5 + 2 = 4.5
#######################################""")
inputs2 = {
'x': tf.contrib.util.make_tensor_proto(5, shape=[]),
}
outputs2 = client.call_predict(inputs2, output_filter=['y'])
result2 = tf.contrib.util.make_ndarray(outputs2['y'])
print('Response: y = {}'.format(result2))
print("""
#######################################
Case3: request.output_filter 包含模型没有输出 y1，gRPC 服务端会抛异常。
#######################################""")
inputs3 = {
'x': tf.contrib.util.make_tensor_proto(20, shape=[]),
}
outputs3 = client.call_predict(inputs3, ['y', 'y1'])
result3 = tf.contrib.util.make_ndarray(outputs3['y'])
print('Response: y = {}'.format(result3))
def run():
client = serving_grpc_client.GRPCClient('localhost:50051')
print("""
模型计算三个线性函数：
y1 = 0.5*x1 + 2
y2 = 2*x1 + 3
y3 = 4*x2 + 5
其中，包含两个输入x1和x2，三个输出y1、y2和y3。
""")
print("""
#######################################
Case1:
输入：request.inputs={x1:10, x2=20}
限制：request.output_filter 为空，表示计算所有输出y1、y2和y3。
预计： response.outputs={y1: 7, y2: 23, y3: 85}
""")
inputs1 = {
'x1': tf.contrib.util.make_tensor_proto(10, shape=[]),
'x2': tf.contrib.util.make_tensor_proto(20, shape=[]),
}
outputs1 = client.call_predict(inputs1)
print("计算结果：")
print("得到输出个数： {0}".format(len(outputs1)))
for alias_name in outputs1:
print("{0}: {1}".format(alias_name, make_ndarray(outputs1[alias_name])))
print("""
#######################################
Case2:
输入：request.inputs={x1:10}
限制：request.output_filter=[y1]，表示只要求计算输出y1即可。
预计：response.outputs={y1: 7}
""")
inputs2 = {
'x1': tf.contrib.util.make_tensor_proto(10, shape=[]),
}
outputs2 = client.call_predict(inputs2, output_filter=['y1'])
print("计算结果：")
print("得到输出个数：{0}".format(len(outputs2)))
for alias_name in outputs2:
print("{0}: {1}".format(alias_name, make_ndarray(outputs2[alias_name])))
print("""
#######################################
Case3:
输入：request.inputs={x1:10, x2:20}
限制：request.output_filter=[y1, y3]，表示只要求计算输出y1和y3即可。
预计：response.outputs={y1: 7, y3: 85}
""")
inputs3 = {
'x1': tf.contrib.util.make_tensor_proto(10, shape=[]),
'x2': tf.contrib.util.make_tensor_proto(20, shape=[]),
}
outputs3 = client.call_predict(inputs3, output_filter=['y1', 'y3'])
print("计算结果：")
print("得到输出个数：{0}".format(len(outputs3)))
for alias_name in outputs3:
print("{0}: {1}".format(alias_name, make_ndarray(outputs3[alias_name])))