如何通过Lambda函数将CNN输出横向拼接后输入单个LSTM？

Got it, let's break this down clearly. You're currently routing each CNN filter's feature map to its own LSTM, but want to instead concatenate all those feature maps horizontally first, then feed the combined tensor into one single LSTM—using a Lambda layer to handle the concatenation step. Here's exactly how to implement this:

Step 1: Understand the Tensor Shape Transformation #

First, let's assume your CNN layer outputs a tensor with shape (batch_size, height, width, num_filters)—this is standard for CNNs processing 2D data. Your goal is to take all num_filters feature maps (each of size height × width) and stitch them together horizontally, resulting in a single tensor of shape (batch_size, height, width × num_filters).

Step 2: Implement the Horizontal Concatenation with Lambda #

We'll use TensorFlow operations inside a Lambda layer to unstack the filters and concatenate them along the width axis. Here's the code snippet (assuming you're using Keras/TensorFlow):

import tensorflow as tf
from tensorflow.keras.layers import Lambda, LSTM

# Assume `cnn_output` is the output tensor from your CNN layers
# Shape: (batch_size, height, width, num_filters)

# Lambda layer to horizontally concatenate all feature maps
horizontal_concat = Lambda(
    lambda x: tf.concat(tf.unstack(x, axis=-1), axis=-2)
)(cnn_output)

# Let's verify the shape: after concat, it becomes (batch_size, height, width * num_filters)

What's happening here?

• tf.unstack(x, axis=-1): Takes the CNN output tensor and splits it along the last dimension (the filters), creating num_filters separate tensors each with shape (batch_size, height, width).
• tf.concat(..., axis=-2): Stitches these separate feature maps together along the width axis (second-last dimension), resulting in one combined feature map where all original filters are laid out side-by-side horizontally.

Step 3: Feed the Concatenated Tensor to a Single LSTM #

Now you can directly pass this concatenated tensor to your LSTM layer. Keep in mind that LSTMs expect input in the shape (batch_size, timesteps, features):

• If you want to treat each row of the concatenated feature map as a timestep, the current shape (batch_size, height, width × num_filters) works perfectly (height = timesteps, width×num_filters = features per timestep).
• If you need to swap timesteps and features (e.g., use width as timesteps), add an extra Lambda layer to transpose:

# Optional: Transpose if you want width as timesteps instead of height
lstm_input = Lambda(lambda x: tf.transpose(x, perm=[0, 2, 1]))(horizontal_concat)
# New shape: (batch_size, width × num_filters, height)

# Add your single LSTM layer
lstm_layer = LSTM(units=128)(lstm_input)  # Or use horizontal_concat directly if shape fits

Step 4: Compare to Your Original Implementation #

Just to clarify the difference:

• Your old code likely looped over each filter (or used a TimeDistributed wrapper with multiple LSTMs) to route each feature map to its own LSTM.
• This new approach combines all feature maps into one continuous tensor first, so you only need one LSTM to process the entire combined sequence.

This should solve your problem—let me know if you need to adjust for specific tensor shapes or edge cases!

内容的提问来源于stack exchange，提问作者rjpg