自编码器形状问题

Question

我在使用自编码器的时候遇到了一个问题，输出的形状设置得不对。目前自编码器的代码是这样的。

我遇到了这个错误：

ValueError: 维度必须相等，但'{{node mean_absolute_error/sub}} = Sub[T=DT_FLOAT](sequential_8/sequential_7/conv1d_transpose_14/BiasAdd, IteratorGetNext:1)'的输入形状是：[?,2000,3]和[?,3750,3]，它们的维度分别是2000和3750。

有没有人能帮我调整一下架构，如果可以的话。我好像忘记了最初为了调整这个问题所做的修改。

import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv1D, MaxPooling1D, UpSampling1D, concatenate
from tensorflow.keras.callbacks import EarlyStopping

# Provided encoder
encoder = tf.keras.models.Sequential([
    tf.keras.layers.Reshape([3750, 3], input_shape=[3750, 3]),
    tf.keras.layers.Conv1D(32, kernel_size=5, padding="same", activation="relu"),
    tf.keras.layers.MaxPool1D(pool_size=2),
    tf.keras.layers.Conv1D(64, kernel_size=5, padding="same", activation="relu"),
    tf.keras.layers.MaxPool1D(pool_size=2),
    tf.keras.layers.Conv1D(128, kernel_size=5, padding="same", activation="relu"),
    tf.keras.layers.MaxPool1D(pool_size=2),
    tf.keras.layers.Conv1D(256, kernel_size=5, padding="same", activation="relu"),
    tf.keras.layers.MaxPool1D(pool_size=2),
    tf.keras.layers.Conv1D(512, kernel_size=5, padding="same", activation="relu"),
    tf.keras.layers.MaxPool1D(pool_size=2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(512)
])

#latent space

decoder = tf.keras.models.Sequential([
    tf.keras.layers.Dense(512 * 125, input_shape=[512]),
    tf.keras.layers.Reshape([125, 512]),
    tf.keras.layers.Conv1DTranspose(512, kernel_size=5, strides=1, padding="same", activation="relu"),
    tf.keras.layers.UpSampling1D(size=2),
    tf.keras.layers.Conv1DTranspose(256, kernel_size=5, strides=1, padding="same", activation="relu"),
    tf.keras.layers.UpSampling1D(size=2),
    tf.keras.layers.Conv1DTranspose(128, kernel_size=5, strides=1, padding="same", activation="relu"),
    tf.keras.layers.UpSampling1D(size=2),
    tf.keras.layers.Conv1DTranspose(64, kernel_size=5, strides=1, padding="same", activation="relu"),
    tf.keras.layers.UpSampling1D(size=2),
    # Adjust the kernel size and padding to match the input shape
    tf.keras.layers.Conv1DTranspose(3, kernel_size=5, strides=1, padding="same", activation="linear")
])

# Add more layers with larger kernel sizes to both encoder and decoder.
ae = tf.keras.models.Sequential([encoder, decoder])

ae.compile(
    loss="mean_squared_error", 
    optimizer=tf.keras.optimizers.Adam(learning_rate=0.00001)
)
# Define the early stopping criteria
early_stopping = EarlyStopping(monitor='val_loss', patience=30, mode='min')

history = ae.fit(X_train, X_train, batch_size=8, epochs=150, validation_data=(X_val, X_val), callbacks=[early_stopping])    ```

错误处理数据预处理深度学习神经网络自编码器形状问题维度不匹配结构调整

自编码器形状问题

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