我有这个麻烦：检查目标时出错：预期的decoded_output具有形状（50，50），但形状为（50，1）的数组使用此代码，具有CONV1D和两个输出的自动编码器，但是麻烦是重建输出（decode_output）：
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
TAM_VECTOR = X_train.shape[1]
input_tweet = Input(shape=(TAM_VECTOR,X_train.shape[2]))
encoded = Conv1D(64, kernel_size=1, activation='relu')(input_tweet)
encoded = Conv1D(32, kernel_size=1, activation='relu')(encoded)
decoded = Conv1D(32, kernel_size=1, activation='relu')(encoded)
decoded = Conv1D(64, kernel_size=1, activation='relu')(decoded)
decoded = Conv1D(TAM_VECTOR, kernel_size=1, activation='relu', name='decode_output')(decoded)
encoded = Flatten()(encoded)
second_output = Dense(1, activation='linear', name='second_output')(encoded)
autoencoder = Model(inputs=input_tweet, outputs=[decoded, second_output])
autoencoder.compile(optimizer="adam",
loss={'decode_output': 'mse', 'second_output': 'mse'},
loss_weights={'decode_output': 0.001, 'second_output': 0.999},
metrics=["mae"])
autoencoder.fit([X_train], [X_train, y_train], epochs=10, batch_size=32)
输入（X）的形状为（50000,50），但由于Conv1D接收3D输入，因此我将其重塑为：
X = np.reshape(X, (X.shape[0], X.shape[1], -1))
（50000,50,1）
而y（第二个输出）是
y.shape
（50000,1）
这里是模型摘要
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_43 (InputLayer) (None, 50, 1) 0
__________________________________________________________________________________________________
conv1d_169 (Conv1D) (None, 50, 64) 128 input_43[0][0]
__________________________________________________________________________________________________
conv1d_170 (Conv1D) (None, 50, 32) 2080 conv1d_169[0][0]
__________________________________________________________________________________________________
conv1d_171 (Conv1D) (None, 50, 32) 1056 conv1d_170[0][0]
__________________________________________________________________________________________________
conv1d_172 (Conv1D) (None, 50, 64) 2112 conv1d_171[0][0]
__________________________________________________________________________________________________
flatten_62 (Flatten) (None, 1600) 0 conv1d_170[0][0]
__________________________________________________________________________________________________
decode_output (Conv1D) (None, 50, 50) 3250 conv1d_172[0][0]
__________________________________________________________________________________________________
pib_output (Dense) (None, 1) 1601 flatten_62[0][0]
==================================================================================================
Total params: 10,227
Trainable params: 10,227
Non-trainable params: 0
答案 0 :(得分：0)
TAM_VECTOR应该在下面的行中替换为1。
替换
decoded = Conv1D(TAM_VECTOR, kernel_size=1, activation='relu', name='decode_output')(decoded)
使用
decoded = Conv1D(1, kernel_size=1, activation='relu', name='decode_output')(decoded)
解码后的输出形状应与自动编码器（50,1）的输入形状匹配。
from keras.layers import Conv1D, Flatten, Dense, Input
from keras.models import Model
import numpy as np
TAM_VECTOR = 50
input_tweet = Input(shape=(TAM_VECTOR,1))
encoded = Conv1D(64, kernel_size=1, activation='relu')(input_tweet)
encoded = Conv1D(32, kernel_size=1, activation='relu')(encoded)
decoded = Conv1D(32, kernel_size=1, activation='relu')(encoded)
decoded = Conv1D(64, kernel_size=1, activation='relu')(decoded)
decoded = Conv1D(1, kernel_size=1, activation='relu', name='decode_output')(decoded)
encoded = Flatten()(encoded)
second_output = Dense(1, activation='linear', name='second_output')(encoded)
autoencoder = Model(inputs=input_tweet, outputs=[decoded, second_output])
autoencoder.compile(optimizer="adam",
loss={'decode_output': 'mse', 'second_output': 'mse'},
loss_weights={'decode_output': 0.001, 'second_output': 0.999},
metrics=["mae"])
autoencoder.fit(np.ones((1,50,1)), [np.ones((1,50,1)), np.ones((1,1))])
1/1 [==============================]-0s 407ms / step-损失：0.9112-encode_output_loss： 0.9549-第二输出损失：0.9111-解码输出平均误差：0.9772-第二输出平均误差：0.9545
答案 1 :(得分：0)
这是错误： 错误1）：
InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: You must feed a value for placeholder tensor 'decode_output_sample_weights_32' with dtype float and shape [?]
[[{{node decode_output_sample_weights_32}}]]
[[loss_2/second_output_loss/Mean_3/_3217]]
(1) Invalid argument: You must feed a value for placeholder tensor 'decode_output_sample_weights_32' with dtype float and shape [?]
[[{{node decode_output_sample_weights_32}}]]
0 successful operations.
0 derived errors ignored.
错误2）：
InvalidArgumentError: You must feed a value for placeholder tensor 'decode_output_target_17' with dtype float and shape [?,?,?] [[{{node decode_output_target_17}}]]
错误3）：
UnknownError: 2 root error(s) found.
(0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[{{node conv1d_1/convolution}}]]
[[loss/add/_157]]
(1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[{{node conv1d_1/convolution}}]]
0 successful operations.
0 derived errors ignored.
错误4）：
UnknownError: 2 root error(s) found.
(0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[{{node conv1d_25/convolution}}]]
[[loss_6/second_output_loss/Mean_3/_1025]]
(1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[{{node conv1d_25/convolution}}]]
0 successful operations.
0 derived errors ignored.