将Caffe训练的性别检测模型转为Keras H5并微调的技术咨询

Got it, let's walk through how to get this sorted out. You've converted the Caffe gender.caffemodel to a Keras H5 file, but since the tool only handles weights, you need to first build a matching Keras model structure, load those weights, then set up your dataset for fine-tuning. Here's the step-by-step process:

1. Build a Keras Model Matching the Caffe Model Structure #

The original model is based on VGG16 (pre-trained on ImageNet) with a custom top for gender classification (binary or 2-class categorical). You need to replicate this structure exactly to load the converted weights correctly.

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.applications.vgg16 import VGG16

# Load VGG16 without the original ImageNet top layers
base_model = VGG16(weights=None, include_top=False, input_shape=(224, 224, 3))

# Add the custom gender classification top (match your Caffe model's deploy.prototxt)
x = base_model.output
x = Flatten(name="flatten")(x)
x = Dense(4096, activation="relu", name="fc1")(x)
x = Dense(4096, activation="relu", name="fc2")(x)
# Use Dense(1, activation="sigmoid") if your model uses binary classification instead
predictions = Dense(2, activation="softmax", name="predictions")(x)

# Assemble the full model
model = Model(inputs=base_model.input, outputs=predictions)

Note: Double-check your original Caffe model's deploy.prototxt to confirm the top layer architecture (neuron counts, activation functions, output classes). If the input shape isn't 224x224, adjust the input_shape parameter accordingly.

2. Load the Converted H5 Weights #

Once the model structure is in place, load your converted weights. If you run into name mismatches between Caffe and Keras layer names, you'll need to adjust the weight keys:

Load Weights Directly (if names match) #

model.load_weights("gender_converted.h5")

Fix Weight Name Mismatches (if needed) #

Caffe and Keras use different naming conventions (e.g., Caffe's conv1_1_w vs Keras's block1_conv1/kernel). To fix this, you can rename the weight keys in the H5 file:

import h5py

with h5py.File("gender_converted.h5", "r+") as f:
    # List all existing weight keys to map them
    print("Original weight keys:", list(f["model_weights"].keys()))
    
    # Example mapping (adjust based on your actual key names)
    key_mapping = {
        "conv1_1_w": "block1_conv1/kernel",
        "conv1_1_b": "block1_conv1/bias",
        # Add mappings for all layers
    }
    
    for old_key, new_key in key_mapping.items():
        # Move the old key to the new path
        f["model_weights"][new_key.split("/")[0]][new_key.split("/")[1]] = f["model_weights"].pop(old_key)

3. Prepare Your Custom Dataset for Fine-Tuning #

Align your data preprocessing with the original model's requirements (Caffe's VGG16 uses mean subtraction, which matches Keras's preprocess_input for VGG16):

from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Data augmentation for training (adjust as needed)
train_datagen = ImageDataGenerator(
    preprocessing_function=preprocess_input,
    rotation_range=15,
    width_shift_range=0.1,
    height_shift_range=0.1,
    horizontal_flip=True
)

# No augmentation for validation
val_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)

# Load datasets (assuming folder structure: train/male, train/female; val/male, val/female)
train_generator = train_datagen.flow_from_directory(
    "path/to/your/train_data",
    target_size=(224, 224),
    batch_size=32,
    class_mode="categorical"  # Use "binary" if using sigmoid output
)

val_generator = val_datagen.flow_from_directory(
    "path/to/your/val_data",
    target_size=(224, 224),
    batch_size=32,
    class_mode="categorical"
)

4. Fine-Tune the Model #

Choose a strategy based on your dataset size:

• Small dataset: Freeze most of the VGG16 base and only train the top layers.
• Large dataset: Unfreeze some or all base layers for full fine-tuning.

Step 1: Train the Top Layers First #

# Freeze all base model layers
for layer in base_model.layers:
    layer.trainable = False

# Compile the model
model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])

# Train the top layers
model.fit(
    train_generator,
    epochs=10,
    validation_data=val_generator
)

Step 2: Unfreeze and Fine-Tune Layers #

# Unfreeze the last few VGG16 layers (e.g., starting from block5)
for layer in base_model.layers[15:]:
    layer.trainable = True

# Recompile with a smaller learning rate to avoid overwriting pre-trained weights
from tensorflow.keras.optimizers import Adam
model.compile(optimizer=Adam(learning_rate=1e-5), loss="categorical_crossentropy", metrics=["accuracy"])

# Continue fine-tuning
model.fit(
    train_generator,
    epochs=20,
    validation_data=val_generator
)

5. Troubleshooting Tips #

• Weight loading errors: Verify that your Keras model's layer names, input shape, and output classes exactly match the original Caffe model.
• Low accuracy: Ensure your data preprocessing matches the original model (mean subtraction, input size). Check for label errors in your dataset and adjust learning rates/augmentation.
• Overfitting: Add dropout layers to the top (if missing from the original model) or increase data augmentation.

内容的提问来源于stack exchange，提问作者L1nu5