TF模型在Google平台上的模型错误：“预期序列化为标量或向量，得到形状：[1，12]”
TF模型在Google平台上的模型错误：“预期序列化为标量或向量，得到形状：[1，12]”
提问于 2020-05-29 03:53:17
作为tfx培训师和pusher输出的一部分，已通过以下库在Google平台作业和模型中执行保存的模型/预培训模型。
from tfx.extensions.google_cloud_ai_platform.pusher import executor as ai_platform_pusher_executor
from tfx.extensions.google_cloud_ai_platform.trainer import executor as ai_platform_trainer_executor然而，当我在Google平台上测试服务模型时，我收到了以下错误消息，请您帮助我理解它的含义吗？我应该如何转换输入数据以被模型接受？
错误：
{
"error": "Prediction failed: Error during model execution: <_MultiThreadedRendezvous of RPC that terminated with:\n\tstatus = StatusCode.INVALID_ARGUMENT\n\tdetails = \"Expected serialized to be a scalar or vector, got shape: [1,12]\n\t [[{{node ParseExample/ParseExampleV2}}]]\"\n\tdebug_error_string = \"{\"created\":\"@1590723484.491366234\",\"description\":\"Error received from peer ipv4:127.0.0.1:8081\",\"file\":\"src/core/lib/surface/call.cc\",\"file_line\":1056,\"grpc_message\":\"Expected serialized to be a scalar or vector, got shape: [1,12]\\n\\t [[{{node ParseExample/ParseExampleV2}}]]\",\"grpc_status\":3}\"\n>"
}Google平台模型中的示例输入：
{
"instances": [
["A", "B", "C", "D","E", "F", "G", "H", "I", "J", "K", "L"]
]
}，这是训练师的代码.
def get_model(show_summary=True):
#one-hot categorical features
num_A = 4
num_B = 3
num_C = 2
num_D = 8
num_E = 12
num_F = 4
num_G = 16
num_H = 26
num_I = 11
num_J = 2
num_K = 11
num_L = 2
input_A = tf.keras.Input(shape=(num_A,), name="A_xf")
input_B = tf.keras.Input(shape=(num_B,), name="B_xf")
input_C = tf.keras.Input(shape=(num_C,), name="C_xf")
input_D = tf.keras.Input(shape=(num_D,), name="D_xf")
input_E = tf.keras.Input(shape=(num_E,), name="E_xf")
input_F = tf.keras.Input(shape=(num_F,), name="F_xf")
input_G = tf.keras.Input(shape=(num_G,), name="G_xf")
input_H = tf.keras.Input(shape=(num_H,), name="H_xf")
input_I = tf.keras.Input(shape=(num_I,), name="I_xf")
input_J = tf.keras.Input(shape=(num_J,), name="J_xf")
input_K = tf.keras.Input(shape=(num_K,), name="K_xf")
input_L = tf.keras.Input(shape=(num_L,), name="L_xf")
inputs_con = tf.keras.layers.concatenate([
input_A,
input_B,
input_C,
input_D,
input_E,
input_F,
input_G,
input_H
input_I,
input_J,
input_K,
input_L])
dense_1 = tf.keras.layers.Dense(50, activation = 'relu')(inputs_con)
dense_2 = tf keras.layers.Dense(25, activation = "rely") (dense_1)
output = tf.keras.laters.Dense(1, activation = "sigmoid") (dense_2)
model = keras.Model(inputs=inputs, outputs=outputs)
_inputs = [
input_A,
input_B,
input_C,
input_D,
input_E,
input_F,
input_G,
input_H,
input_I,
input_J,
input_K,
input_L]
model = tf.keras.models.Model(_inputs, output)
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy'])
if show_summary:
model.summary()
return model回答 1
Stack Overflow用户
发布于 2020-12-11 04:25:29
LLTeng，不确定您对该模型的意图是什么，但基于实例，这将起作用：
inputs = tf.keras.Input((12,))
dense_1 = tf.keras.layers.Dense(50, activation = 'relu')(inputs)
dense_2 = tf keras.layers.Dense(25, activation = "rely") (dense_1)
output = tf.keras.laters.Dense(1, activation = "sigmoid") (dense_2)
model = tf.keras.Model(inputs=inputs, outputs=outputs)页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/62078419
复制相关文章
相似问题
腾讯云开发者
