我有这个麻烦： 检查目标时出错： 预期decoded_output具有形状 (50， 50( 但得到具有形状 (50， 1( 的数组 使用此代码，带有 CONV1D 和两个输出的自动编码器，但问题是重建输出 (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，我将形状调整为：
X = np.reshape(X, (X.shape[0], X.shape[1], -1))
(50000,50,1(
y(第二个输出(为
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
TAM_VECTOR应在下一行中替换为 1。
取代
decoded = Conv1D(TAM_VECTOR, kernel_size=1, activation='relu', name='decode_output')(decoded)
跟
decoded = Conv1D(1, kernel_size=1, activation='relu', name='decode_output')(decoded)
解码的输出形状应与自动编码器的输入形状匹配 (50,1(。
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 [=================================================================================================================================================================================second_output_mean_absolute_error decode_output_mean_absolute_error second_output_loss decode_output_loss=
============================================================================================================================================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.