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import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import keras
from keras.layers import Input, Dropout, Dense
from keras.models import Model
from keras_vggface.vggface import VGGFace
def get_model(image_shape, num_classes, model_weights, unfreeze_layers=-3, drop_rate=0.5):
input_layer = Input(shape=image_shape)
vgg_base_model = VGGFace(include_top = False, input_shape = image_shape, pooling='avg')
# Freeze all the layers till unfreeze layers
for layer in vgg_base_model.layers[:unfreeze_layers]:
layer.trainable = False
for layer in vgg_base_model.layers[unfreeze_layers:]:
layer.trainable = True
x = vgg_base_model(input_layer)
x = Dropout(drop_rate)(x)
output = Dense(num_classes, activation='softmax')(x)
model = Model(inputs=[input_layer], outputs=[output], name="Expression_Classifier")
model.load_weights(model_weights)
return model
if __name__ == "__main__":
model_path = "vgg_face_weights2.h5"
model = get_model(
image_shape = (224, 224, 3),
num_classes = 6,
model_weights = model_path
)
print(model.summary()) |