build_weighted_model.py

from common import GENRES
from keras.models import Model
from keras.optimizers import RMSprop
from keras import backend as K
from keras.layers import Input, Dense, Lambda, Dropout, Activation, LSTM, \
    TimeDistributed, Conv1D, MaxPooling1D

SEED = 42
N_LAYERS = 3
FILTER_LENGTH = 5
CONV_FILTER_COUNT = 256
LSTM_COUNT = 256
BATCH_SIZE = 32
EPOCH_COUNT = 80


def build_weighted_model(weights_filepath):
    print('Building model...')

    n_features = 128
    input_shape = (None, n_features)
    model_input = Input(input_shape, name='input')

    model = crnn(model_input)
    model.load_weights(weights_filepath)
    return model

def crnn(model_input):
    layer = model_input
    for i in range(N_LAYERS):
        # Convolutional layer names are used by extract_filters.py
        layer = Conv1D(
            nb_filter=CONV_FILTER_COUNT,
            filter_length=FILTER_LENGTH,
            name='convolution_' + str(i + 1))(layer)
        layer = Activation('relu')(layer)
        layer = MaxPooling1D(2)(layer)

    layer = Dropout(0.5)(layer)
    layer = LSTM(LSTM_COUNT, return_sequences=True)(layer)
    layer = Dropout(0.5)(layer)
    layer = TimeDistributed(Dense(len(GENRES)))(layer)
    layer = Activation('softmax', name='output_realtime')(layer)
    time_distributed_merge_layer = Lambda(
        function=lambda x: K.mean(x, axis=1),
        output_shape=lambda shape: (shape[0],) + shape[2:],
        name='output_merged')
    model_output = time_distributed_merge_layer(layer)
    model = Model(model_input, model_output)
    opt = RMSprop(lr=0.00001)  # Optimizer
    model.compile(
        loss='categorical_crossentropy',
        optimizer=opt,
        metrics=['accuracy'])

    return model