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from keras.models import Model
from keras.layers import Input
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Concatenate
from keras.utils import plot_model
import tensorflow as tf
from keras.layers import GlobalAveragePooling2D, GlobalMaxPooling2D, Reshape, Dense, Input, Flatten, Dropout
from keras.layers import Activation, Concatenate, Conv2D, Multiply
from keras.applications.resnet import ResNet50
import keras
from keras.applications.inception_v3 import preprocess_input
from updated_resnet_1 import ResNet50
def build_regression_model():
base_model_carpal = ResNet50(input_shape = (224, 224, 3))
base_model_carpal.load_weights("https://drive.google.com/file/d/1pnPeSYukdn_a-GtQwjcyQxKVujOYSAUy/view?usp=sharing")
base_model_metacarpal = ResNet50(input_shape = (224, 224, 3))
base_model_metacarpal.load_weights("https://drive.google.com/file/d/19N2bxesImdU3sfCFWAgUiSfq55RRZBPR/view?usp=sharing")
gender_input = Input(shape=(1,), name='gender_input')
for layer in base_model_carpal.layers:
layer._name = 'carpal_' + layer.name
for layer in base_model_metacarpal.layers:
layer._name = 'metacarpal_' + layer.name
for layer in base_model_carpal.layers[:-9]:
layer.trainable = False
for layer in base_model_metacarpal.layers[:-9]:
layer.trainable = False
x1 = base_model_carpal.layers[-4].output
x2 = base_model_metacarpal.layers[-4].output
# gender_weight = tf.Variable(initial_value=1.0, trainable=True, name='gender_weight')
# gender_input_weighted = tf.multiply(gender_input, gender_weight)
x = Concatenate()([x1, x2, gender_input])
x = Dense(128, activation='relu')(x)
x = Dense(64, activation='relu')(x)
x = Dense(32, activation='relu')(x)
predictions = Dense(1)(x)
model = Model(inputs=(base_model_carpal.input, base_model_metacarpal.input, gender_input), outputs=predictions)
return model |