import keras.backend as K
import tensorflow as tf
from keras.applications.inception_resnet_v2 import InceptionResNetV2
from keras.layers import Input, Dense, concatenate, Lambda
from keras.models import Model
from keras.utils import plot_model
from config import img_size, channel, embedding_size
def build_model():
base_model = InceptionResNetV2(include_top=False, weights='imagenet', input_shape=(img_size, img_size, channel),
pooling='avg')
image_input = base_model.input
x = base_model.layers[-1].output
out = Dense(embedding_size)(x)
image_embedder = Model(image_input, out)
input_a = Input((img_size, img_size, channel), name='anchor')
input_p = Input((img_size, img_size, channel), name='positive')
input_n = Input((img_size, img_size, channel), name='negative')
normalize = Lambda(lambda x: K.l2_normalize(x, axis=-1), name='normalize')
x = image_embedder(input_a)
output_a = normalize(x)
x = image_embedder(input_p)
output_p = normalize(x)
x = image_embedder(input_n)
output_n = normalize(x)
merged_vector = concatenate([output_a, output_p, output_n], axis=-1)
model = Model(inputs=[input_a, input_p, input_n],
outputs=merged_vector)
return model
if __name__ == '__main__':
with tf.device("/cpu:0"):
model = build_model()
print(model.summary())
plot_model(model, to_file='model.svg', show_layer_names=True, show_shapes=True)
K.clear_session()
