这是我对此程序的代码。它工作正常，但突然不起作用，请任何人都可以解决此问题
childClass.getClass == classOf[org.apache.spark.sql.catalyst.plans.logical.Project]
此错误在我的程序中创建。解决我多次搜索却无法解决的问题是什么，我不知道要解决什么问题？
roles_users= db.Table('roles_users',
db.Column('user_id', db.Integer, db.ForeignKey('user.id')),
db.Column('role_id', db.Integer,db.ForeignKey('role.id')))
class role(db.Model,RoleMixin):
__table_args__ = {"schema":"att"}
__tablename__ = 'role'
id= db.Column(db.Integer, primary_key=True)
name = db.Column(db.String(100))
description= db.Column(db.String(255))
class User(db.Model,UserMixin):
__table_args__ = {"schema":"att"}
id= db.Column(db.Integer, primary_key=True)
email = db.Column(db.String(100), unique=True)
password= db.Column(db.String(255))
rad = db.Column(db.String(10))
active = db.Column(db.Boolean)
roles_users = db.relationship('role', secondary=roles_users,
backref='user', lazy=True)
答案 0 :(得分：0)
根据Dr. Snoopy的建议，tf.keras.Model的参数为inputs和outputs，但是您将其传递为input和output分别在custom_model = Model(input=resnet_model.input, output=x)中。
重现该错误的代码-
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
X1 = tf.constant([2, 3, 4, 5, 6, 7])
X2 = tf.constant([2, 3, 4, 5, 6, 7])
yTrain = tf.constant([4, 6, 8, 10, 12, 14])
input1 = keras.Input(shape=(1,))
input2 = keras.Input(shape=(1,))
x = layers.concatenate([input1, input2])
x = layers.Dense(8, activation='relu')(x)
outputs = layers.Dense(2)(x)
mlp = keras.Model(input = [input1, input2], output = outputs)
mlp.summary()
mlp.compile(loss='mean_squared_error',
optimizer='adam', metrics=['accuracy'])
mlp.fit([X1, X2], yTrain, batch_size=1, epochs=10, validation_split=0.2,
shuffle=True)
mlp.evaluate([X1, X2], yTrain)
输出-
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-bec9ebbd1faf> in <module>()
14 x = layers.Dense(8, activation='relu')(x)
15 outputs = layers.Dense(2)(x)
---> 16 mlp = keras.Model(input = [input1, input2], output = outputs)
17
18 mlp.summary()
2 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/generic_utils.py in validate_kwargs(kwargs, allowed_kwargs, error_message)
776 for kwarg in kwargs:
777 if kwarg not in allowed_kwargs:
--> 778 raise TypeError(error_message, kwarg)
779
780
TypeError: ('Keyword argument not understood:', 'input')
要解决该错误，请将参数更改为inputs和outputs。
固定代码-
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
X1 = tf.constant([2, 3, 4, 5, 6, 7])
X2 = tf.constant([2, 3, 4, 5, 6, 7])
yTrain = tf.constant([4, 6, 8, 10, 12, 14])
input1 = keras.Input(shape=(1,))
input2 = keras.Input(shape=(1,))
x = layers.concatenate([input1, input2])
x = layers.Dense(8, activation='relu')(x)
outputs = layers.Dense(2)(x)
mlp = keras.Model(inputs = [input1, input2], outputs = outputs)
mlp.summary()
mlp.compile(loss='mean_squared_error',
optimizer='adam', metrics=['accuracy'])
mlp.fit([X1, X2], yTrain, batch_size=1, epochs=10, validation_split=0.2,
shuffle=True)
mlp.evaluate([X1, X2], yTrain)
输出-
Model: "functional_5"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_6 (InputLayer) [(None, 1)] 0
__________________________________________________________________________________________________
input_7 (InputLayer) [(None, 1)] 0
__________________________________________________________________________________________________
concatenate_34 (Concatenate) (None, 2) 0 input_6[0][0]
input_7[0][0]
__________________________________________________________________________________________________
dense_4 (Dense) (None, 8) 24 concatenate_34[0][0]
__________________________________________________________________________________________________
dense_5 (Dense) (None, 2) 18 dense_4[0][0]
==================================================================================================
Total params: 42
Trainable params: 42
Non-trainable params: 0
__________________________________________________________________________________________________
Epoch 1/10
4/4 [==============================] - 0s 32ms/step - loss: 54.3236 - accuracy: 0.0000e+00 - val_loss: 169.3114 - val_accuracy: 0.0000e+00
Epoch 2/10
4/4 [==============================] - 0s 6ms/step - loss: 53.4965 - accuracy: 0.0000e+00 - val_loss: 167.0008 - val_accuracy: 0.0000e+00
Epoch 3/10
4/4 [==============================] - 0s 6ms/step - loss: 52.7413 - accuracy: 0.0000e+00 - val_loss: 164.6473 - val_accuracy: 0.0000e+00
Epoch 4/10
4/4 [==============================] - 0s 6ms/step - loss: 51.8159 - accuracy: 0.0000e+00 - val_loss: 162.4427 - val_accuracy: 0.0000e+00
Epoch 5/10
4/4 [==============================] - 0s 6ms/step - loss: 51.0917 - accuracy: 0.0000e+00 - val_loss: 160.1798 - val_accuracy: 0.0000e+00
Epoch 6/10
4/4 [==============================] - 0s 6ms/step - loss: 50.4425 - accuracy: 0.0000e+00 - val_loss: 157.8355 - val_accuracy: 0.0000e+00
Epoch 7/10
4/4 [==============================] - 0s 6ms/step - loss: 49.5709 - accuracy: 0.0000e+00 - val_loss: 155.6147 - val_accuracy: 0.0000e+00
Epoch 8/10
4/4 [==============================] - 0s 6ms/step - loss: 48.7816 - accuracy: 0.0000e+00 - val_loss: 153.4298 - val_accuracy: 0.0000e+00
Epoch 9/10
4/4 [==============================] - 0s 6ms/step - loss: 47.9975 - accuracy: 0.0000e+00 - val_loss: 151.2858 - val_accuracy: 0.0000e+00
Epoch 10/10
4/4 [==============================] - 0s 6ms/step - loss: 47.3943 - accuracy: 0.0000e+00 - val_loss: 149.0254 - val_accuracy: 0.0000e+00
1/1 [==============================] - 0s 2ms/step - loss: 80.9333 - accuracy: 0.0000e+00
[80.93333435058594, 0.0]