Hey there, let's work through this issue together—setting up EfficientNetB0 for 4-class classification should be smooth, but there are a few common missteps that could be causing this error. Here are the key areas to check:

1. Verify the Top Classification Layer Matches 4 Classes #

Since you set include_top=False in your EfficientNetB0 initialization, you need to manually add a top layer that outputs 4 classes (matching your task). If this layer is missing or configured for the wrong number of classes, training will fail. Here's how to properly build the full model:

from tensorflow.keras import models, layers

# Your existing conv base
conv_base = EfficientNetB0(weights="imagenet", include_top=False, input_shape=(224, 224, 3))

# Freeze the conv base if you're doing transfer learning (optional but recommended for initial training)
conv_base.trainable = False

# Build the full model
model = models.Sequential([
    conv_base,
    layers.GlobalAveragePooling2D(),  # Reduces spatial dimensions to a single vector
    layers.Dense(256, activation='relu'),  # Optional hidden layer for feature refinement
    layers.Dense(4, activation='softmax')  # Critical: outputs 4 probabilities for your classes
])

2. Check Data Generator Configuration #

Your train_generator and validation_generator need to be set up correctly for multi-class classification:

• Ensure class_mode='categorical' (not 'binary', which is for 2-class tasks)
• Confirm your dataset directory structure has exactly 4 subfolders (one per class) if using flow_from_directory
• Match target_size=(224, 224) to your model's input shape

Example generator setup:

from tensorflow.keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
    'path/to/train_data',
    target_size=(224, 224),
    batch_size=20,
    class_mode='categorical'  # Must use this for 4-class classification
)

3. Use the Correct Loss Function #

When compiling your model, the loss function must align with multi-class classification. Use categorical_crossentropy instead of binary_crossentropy:

model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',  # Critical for multi-class tasks
    metrics=['accuracy']
)

4. Confirm Input Shape and Generator Output Match #

Double-check that your generator's target_size is exactly (224, 224) to match the input_shape defined in EfficientNetB0. Mismatched dimensions will cause model input errors.

5. Verify Pre-trained Weights Loaded Successfully #

In rare cases, the imagenet weights might fail to download (e.g., offline environments). If you're working offline, you can download the weights manually and specify the local path in weights=, but this is less likely to be the issue here.

Start with the first three checks—they're the most common causes of this error. Let me know if you still run into issues after verifying these!

内容的提问来源于stack exchange，提问作者Muhammad Rifqi