define_model.py

from tensorflow import keras
from tensorflow.keras import layers
import tensorflow.compat.v1 as tf
from tensorflow.keras.regularizers import l2

def MEAN_Spot(opt):
    # channel 1
    inputs1 = layers.Input(shape=(42,42,1))
    conv1 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs1)
    bn1 = layers.BatchNormalization()(conv1)
    pool1 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn1)
    do1 = layers.Dropout(0.3)(pool1)
    # channel 2
    inputs2 = layers.Input(shape=(42,42,1))
    conv2 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs2)
    bn2 = layers.BatchNormalization()(conv2)
    pool2 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn2)
    do2 = layers.Dropout(0.3)(pool2)
    # channel 3
    inputs3 = layers.Input(shape=(42,42,1))
    conv3 = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs3)
    bn3 = layers.BatchNormalization()(conv3)
    pool3 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn3)
    do3 = layers.Dropout(0.3)(pool3)
    # merge 1
    merged = layers.Concatenate()([do1, do2, do3])
    # interpretation 1
    merged_conv = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(merged)
    merged_pool = layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2,2))(merged_conv)
    flat = layers.Flatten()(merged_pool)
    flat_do = layers.Dropout(0.2)(flat)
    # outputs 
    outputs = layers.Dense(1, activation='linear', name='spot')(flat_do)
    #Takes input u, v, os
    model = keras.models.Model(inputs=[inputs1, inputs2, inputs3], outputs=[outputs])
    model.compile(
        loss={'spot':'mse'}, 
        optimizer=opt, 
        metrics={'spot':tf.keras.metrics.MeanAbsoluteError()}, 
    )
    return model

# Transfer learning by removing and freeze layers
def MEAN_Recog_TL(model_spot, opt, emotion_class):
    for layer in model_spot.layers:
        layer.trainable = False
    # Until last convolutional later
    merged = model_spot.layers[-6].output 
    merged_conv = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(merged)
    merged_pool = layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2,2))(merged_conv)
    flat = layers.Flatten()(merged_pool)
    flat_do = layers.Dropout(0.2)(flat)
    outputs = layers.Dense(emotion_class, activation = "softmax", name='recog')(flat_do)
    
    model =  keras.models.Model(inputs = model_spot.input, outputs = outputs)
    model.compile(
        loss={'recog':'categorical_crossentropy'}, 
        optimizer=opt, 
        metrics={'recog':tf.keras.metrics.CategoricalAccuracy()}
    )
    return model

def MEAN_Spot_Recog_TL(model_spot, model_recog, opt):
    outputs1 = model_spot.layers[-1].output 
    outputs2 = model_recog.layers[-1].output 
    model = keras.models.Model(inputs = [model_spot.input], outputs = [outputs1, outputs2])
    model.compile(
        loss={'spot':'mse', 'recog':'categorical_crossentropy'}, 
        optimizer=opt, 
        metrics={'spot':tf.keras.metrics.MeanAbsoluteError(), 'recog':tf.keras.metrics.CategoricalAccuracy()}
    )
    return model