DeepMusicClassification/model.py at master · nlopez99/DeepMusicClassification · GitHub

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import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.keras.regularizers import l2

def create_model(input_shape):
    model = Sequential()

    model.add(Conv2D(64, (3,3), input_shape=input_shape))
    model.add(Activation('elu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Conv2D(128, (3,3), kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01)))
    model.add(Activation('elu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Conv2D(256, (3,3), kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01)))
    model.add(Activation('elu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Conv2D(512, (3,3), kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01)))
    model.add(Activation('elu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Flatten())

    model.add(Dense(1024))
    model.add(Activation('elu'))
    model.add(Dropout(0.5))

    model.add(Dense(10))
    model.add(Activation('softmax'))

    model.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics = ['accuracy'])

    return model