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from tensorflow.keras.models import Model,Sequential
from tensorflow.keras.layers import Dense, Flatten, Dropout
from tensorflow.keras.applications import VGG16
from tensorflow.keras.regularizers import L2
def get_pretrained_dog_emotion_classifier(weights_path = "dogs_emotion_model_weights.h5"):
# images input shape
MODEL_INPUT_SHAPE = (224,224,3)
# instance the pretrained convolutional blocks
pretrained_vgg_model = VGG16(include_top = False, input_shape = MODEL_INPUT_SHAPE)
# freeze the first four pretrained layer
target_freeze_blocks = ["block1","block2","block3","block4"]
for layer in pretrained_vgg_model.layers:
if layer.name.split("_")[0] in target_freeze_blocks:
layer.trainable = False
# create the model's classifier
classifier = Sequential(
[
Flatten(),
Dense(32,activation = "relu",name = "classifier_dense1"),
Dropout(0.2,name = "classifier_dropout1"),
Dense(64,activation = "relu",kernel_regularizer = L2(),name = "classifier_dense2"),
Dense(64,activation = "relu",name = "classifier_dense3"),
Dropout(0.2,name = "classifier_dropout2"),
Dense(1,activation = "sigmoid",name = "classifier_dense4")
],name = "classifier"
)
# connect the two models
output = classifier(pretrained_vgg_model.layers[-1].output)
model = Model(inputs = pretrained_vgg_model.layers[0].input,outputs = output)
# load the model_weights
model.load_weights(weights_path)
return model
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