Hey there! Totally get being stuck when you're new to this—let's break this down step by step, since your data setup (gradually changing frames paired with slowly shifting numeric targets) is actually perfect for a few specific Keras approaches.

You're working on a time-series regression task (not classification, since your output is a continuous number). The key here is that your data has two critical components: spatial information (from images) and temporal dependencies (frames in sequence, with labels that change slowly based on pixel movement).

For your scenario, two models stand out as ideal fits:

1. CNN + LSTM Hybrid Model #

This approach first extracts spatial features from each frame using a CNN, then feeds those features into an LSTM to capture the slow, sequential changes in your numeric labels. It's great if you want to separate spatial and temporal processing.

Here's a beginner-friendly code example with explanations:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import (
    TimeDistributed, Conv2D, MaxPooling2D, Flatten,
    LSTM, Dense
)

# Define your input shape: (number of samples, sequence length (frames), image height, image width, channels)
# Example: Each sample is 10 frames of 224x224 RGB images
input_shape = (10, 224, 224, 3)

model = Sequential()

# TimeDistributed wraps the CNN layers to apply them to EVERY frame in the sequence
# This ensures each frame's spatial features are extracted independently while keeping the sequence order
model.add(TimeDistributed(Conv2D(32, (3,3), activation='relu'), input_shape=input_shape))
model.add(TimeDistributed(MaxPooling2D((2,2))))  # Reduce spatial size to save compute
model.add(TimeDistributed(Conv2D(64, (3,3), activation='relu')))
model.add(TimeDistributed(MaxPooling2D((2,2))))
model.add(TimeDistributed(Flatten()))  # Convert 2D features to 1D vectors for the LSTM

# LSTM layer: Captures the temporal relationships between frames
# return_sequences=False because we only need one output per sequence (your single numeric value)
model.add(LSTM(64, return_sequences=False))

# Output layer: Dense(1) for regression (single continuous value), uses linear activation by default
model.add(Dense(1))

# Compile for regression: Use mean squared error (MSE) as loss, Adam as optimizer
model.compile(optimizer='adam', loss='mse')

# Print model structure to understand how data flows through each layer
model.summary()

Key terms explained for beginners:

• TimeDistributed: Lets you reuse CNN layers across every frame in your sequence—no need to build separate CNNs for each frame.
• LSTM: Long Short-Term Memory network, designed to remember patterns in sequential data (perfect for your slowly changing labels, which depend on past frame movements).
• MSE: Mean Squared Error, the standard loss function for regression tasks—it measures how close your predictions are to the true numeric values.

2. ConvLSTM2D Model #

If you want to combine spatial and temporal processing in one step, use ConvLSTM2D. It applies convolution operations across both spatial (image pixels) and temporal (frame sequence) dimensions, which is great for video-like frame sequences.

Simplified example:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import ConvLSTM2D, Flatten, Dense

input_shape = (10, 224, 224, 3)  # Same input shape as before

model = Sequential()

# ConvLSTM2D handles both spatial and temporal features in one layer
model.add(ConvLSTM2D(32, (3,3), activation='relu', input_shape=input_shape))
model.add(Flatten())
model.add(Dense(1))

model.compile(optimizer='adam', loss='mse')
model.summary()

• Normalize your images: Scale pixel values to the range [0, 1] by dividing by 255—this helps the model train faster and more stable.
• Use small batches: Start with batch_size=8 or 16 in model.fit() to avoid memory issues with large image sequences.
• Validate your model: Add validation_split=0.2 to use 20% of your data for checking overfitting (if validation loss keeps going up while training loss goes down, you're overfitting).
• Track meaningful metrics: Add metrics=['mae'] to model.compile()—Mean Absolute Error is easier to interpret than MSE (it tells you the average absolute difference between predictions and true values).

Example training code snippet:

# Assume X_train is your input frame sequences, y_train is your numeric labels
history = model.fit(
    X_train, y_train,
    epochs=20,
    batch_size=8,
    validation_split=0.2,
    metrics=['mae']
)

• Start small: Test your model with a tiny subset of your data (e.g., 100 samples) first—this lets you debug issues without waiting hours for training.
• Check output shapes: Use model.summary() to make sure each layer's output makes sense. For example, after TimeDistributed(Conv2D), you should see a shape like (None, 10, 212, 212, 32) (10 frames, each with 212x212x32 features).
• Don't overcomplicate: Get the basic model working first before adding extra layers (like dropout or more CNN filters). You can always tweak later once you understand what's happening.

内容的提问来源于stack exchange，提问作者Yes