sound_models.py

import keras
from keras.layers import Dense, Conv2D, SeparableConv2D, Convolution2D, AveragePooling2D
from keras.layers import MaxPooling2D, GlobalAveragePooling2D, GlobalMaxPooling2D, Activation, Dropout, BatchNormalization, Flatten, Input
from keras.models import Model, Sequential

def model_cnn_alexnet(input_shape, num_classes, time_compress=[2, 1, 1], early_strides=(2,3)):
    model = Sequential()
 
    model.add(Conv2D(48, 11,  input_shape=input_shape, strides=early_strides, activation='relu', padding='same'))
    model.add(MaxPooling2D(3, strides=(1,2)))
    model.add(BatchNormalization())

    model.add(Conv2D(128, 5, strides=early_strides, activation='relu', padding='same'))
    model.add(MaxPooling2D(3, strides=2))
    model.add(BatchNormalization())

    model.add(Conv2D(192, 3, strides=(1, time_compress[0]), activation='relu', padding='same'))
    model.add(Conv2D(192, 3, strides=(1, time_compress[1]), activation='relu', padding='same'))
    model.add(Conv2D(128, 3, strides=(1, time_compress[2]), activation='relu', padding='same', name='last_conv'))
    model.add(MaxPooling2D(3, strides=(1,2)))
    model.add(BatchNormalization())

    model.add(Flatten())
    model.add(Dense(256, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(256, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes, activation='softmax'))
    return model

def create_model(conf, weights=None, show_detail=False):
    print('Model: AlexNet based')
    model = model_cnn_alexnet(conf.dims, conf.num_classes,
                                time_compress=[1, 1, 1], early_strides=(3,2))
    model.compile(loss='categorical_crossentropy',
              optimizer=keras.optimizers.Adam(lr=conf.learning_rate),
              metrics=['accuracy'])
    if weights is not None:
        print('Loading weights:', weights)
        model.load_weights(weights, by_name=True, skip_mismatch=True)
    if show_detail:
        model.summary()
    return model

def freeze_model_layers(model, trainable_after_this=''):
    trainable = False
    for layer in model.layers:
        if layer.name == trainable_after_this:
            trainable = True
        layer.trainable = trainable