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VGG-19 transfer learning model for emotion recognition.
"""
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import (
Dense, Dropout, GlobalAveragePooling2D, Flatten,
BatchNormalization, Input, Lambda
)
from tensorflow.keras.applications import VGG19
import sys
from pathlib import Path
sys.path.append(str(Path(__file__).parent.parent.parent))
from src.config import IMAGE_SIZE_TRANSFER, NUM_CLASSES, NUM_CHANNELS_RGB
def build_vgg_model(
input_shape: tuple = (*IMAGE_SIZE_TRANSFER, NUM_CHANNELS_RGB),
num_classes: int = NUM_CLASSES,
trainable_layers: int = 4,
dropout_rate: float = 0.5
) -> Model:
"""
Build VGG-19 transfer learning model for emotion recognition.
Args:
input_shape: Input image shape (height, width, channels)
num_classes: Number of emotion classes
trainable_layers: Number of top convolutional layers to make trainable
dropout_rate: Dropout rate for dense layers
Returns:
Keras model
"""
# Load pre-trained VGG19
base_model = VGG19(
weights='imagenet',
include_top=False,
input_shape=input_shape
)
# Freeze all layers initially
for layer in base_model.layers:
layer.trainable = False
# Unfreeze top convolutional layers for fine-tuning
for layer in base_model.layers[-trainable_layers:]:
layer.trainable = True
# Build the model
inputs = Input(shape=input_shape)
# Preprocess input for VGG19 using Rescaling layer
# VGG19 expects inputs scaled to 0-255 range with mean subtraction
x = tf.keras.layers.Rescaling(scale=255.0, offset=-127.5)(inputs)
# Pass through base model
x = base_model(x, training=True)
# Classification head
x = GlobalAveragePooling2D()(x)
x = Dense(512, activation='relu')(x)
x = BatchNormalization()(x)
x = Dropout(dropout_rate)(x)
x = Dense(256, activation='relu')(x)
x = BatchNormalization()(x)
x = Dropout(dropout_rate)(x)
outputs = Dense(num_classes, activation='softmax')(x)
model = Model(inputs=inputs, outputs=outputs, name='vgg19_emotion')
return model
def build_vgg_from_grayscale(
input_shape: tuple = (*IMAGE_SIZE_TRANSFER, 1),
num_classes: int = NUM_CLASSES,
trainable_layers: int = 4,
dropout_rate: float = 0.5
) -> Model:
"""
Build VGG-19 model that accepts grayscale input.
Converts grayscale to RGB internally.
Args:
input_shape: Input shape for grayscale images
num_classes: Number of emotion classes
trainable_layers: Number of top layers to make trainable
dropout_rate: Dropout rate
Returns:
Keras model
"""
# Load pre-trained VGG19
base_model = VGG19(
weights='imagenet',
include_top=False,
input_shape=(*IMAGE_SIZE_TRANSFER, 3)
)
# Freeze base layers
for layer in base_model.layers:
layer.trainable = False
# Unfreeze top layers
for layer in base_model.layers[-trainable_layers:]:
layer.trainable = True
# Input for grayscale image
inputs = Input(shape=input_shape)
# Convert grayscale to RGB by repeating channels
x = tf.keras.layers.Concatenate()([inputs, inputs, inputs])
# Preprocess for VGG19 using Rescaling layer
x = tf.keras.layers.Rescaling(scale=255.0, offset=-127.5)(x)
# Base model
x = base_model(x, training=True)
# Classification head
x = GlobalAveragePooling2D()(x)
x = Dense(512, activation='relu')(x)
x = BatchNormalization()(x)
x = Dropout(dropout_rate)(x)
x = Dense(256, activation='relu')(x)
x = BatchNormalization()(x)
x = Dropout(dropout_rate)(x)
outputs = Dense(num_classes, activation='softmax')(x)
model = Model(inputs=inputs, outputs=outputs, name='vgg19_emotion_grayscale')
return model
def build_vgg_with_flatten(
input_shape: tuple = (*IMAGE_SIZE_TRANSFER, NUM_CHANNELS_RGB),
num_classes: int = NUM_CLASSES,
dropout_rate: float = 0.5
) -> Model:
"""
Alternative VGG-19 architecture using Flatten instead of GAP.
This is closer to the original VGG architecture.
Args:
input_shape: Input image shape
num_classes: Number of emotion classes
dropout_rate: Dropout rate
Returns:
Keras model
"""
base_model = VGG19(
weights='imagenet',
include_top=False,
input_shape=input_shape
)
# Freeze base model
for layer in base_model.layers:
layer.trainable = False
inputs = Input(shape=input_shape)
x = tf.keras.layers.Rescaling(scale=255.0, offset=-127.5)(inputs)
x = base_model(x, training=False)
# VGG-style classification head
x = Flatten()(x)
x = Dense(4096, activation='relu')(x)
x = Dropout(dropout_rate)(x)
x = Dense(4096, activation='relu')(x)
x = Dropout(dropout_rate)(x)
outputs = Dense(num_classes, activation='softmax')(x)
model = Model(inputs=inputs, outputs=outputs, name='vgg19_emotion_flatten')
return model
def freeze_base_model(model: Model) -> Model:
"""
Freeze all layers in the base VGG model.
Args:
model: VGG emotion model
Returns:
Model with frozen base
"""
for layer in model.layers:
if 'vgg' in layer.name.lower():
layer.trainable = False
return model
def unfreeze_top_blocks(model: Model, num_blocks: int = 1) -> Model:
"""
Unfreeze top convolutional blocks of VGG for fine-tuning.
VGG19 has 5 blocks. Block 5 has 4 conv layers.
Args:
model: VGG emotion model
num_blocks: Number of blocks to unfreeze from top
Returns:
Model with partially unfrozen base
"""
# Block layer counts: block1=2, block2=2, block3=4, block4=4, block5=4
block_layers = {5: 4, 4: 4, 3: 4, 2: 2, 1: 2}
layers_to_unfreeze = sum([block_layers[i] for i in range(6 - num_blocks, 6)])
for layer in model.layers:
if 'vgg' in layer.name.lower():
for vgg_layer in layer.layers[-layers_to_unfreeze:]:
if 'conv' in vgg_layer.name:
vgg_layer.trainable = True
return model
def get_model_config() -> dict:
"""
Get the default model configuration.
Returns:
Dictionary with model configuration
"""
return {
"name": "VGG-19",
"input_shape": (*IMAGE_SIZE_TRANSFER, NUM_CHANNELS_RGB),
"num_classes": NUM_CLASSES,
"expected_accuracy": "68-75%",
"training_time": "~60 minutes (GPU)",
"parameters": "~20M",
"base_model": "VGG-19 (ImageNet)"
}
if __name__ == "__main__":
# Build and display model summary
print("Building VGG-19 model...")
model = build_vgg_model()
# Count trainable parameters
trainable = sum([tf.keras.backend.count_params(w) for w in model.trainable_weights])
non_trainable = sum([tf.keras.backend.count_params(w) for w in model.non_trainable_weights])
print(f"\nTotal parameters: {trainable + non_trainable:,}")
print(f"Trainable parameters: {trainable:,}")
print(f"Non-trainable parameters: {non_trainable:,}")
print("\nModel configuration:")
config = get_model_config()
for key, value in config.items():
print(f" {key}: {value}")
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