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models.py
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models.py
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import keras.backend as K
from keras import regularizers
from keras.layers import Lambda
from keras.layers.convolutional import Conv1D, MaxPooling1D
from keras.layers.core import Activation, Dense
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
import os
DATA_AUDIO_DIR = './audio'
TARGET_SR = 8000
OUTPUT_DIR = './output'
OUTPUT_DIR_TRAIN = os.path.join(OUTPUT_DIR, 'train')
OUTPUT_DIR_TEST = os.path.join(OUTPUT_DIR, 'test')
AUDIO_LENGTH = 32000
def m3(num_classes=10):
print('Using Model M3')
m = Sequential()
m.add(Conv1D(256,
input_shape=[AUDIO_LENGTH, 1],
kernel_size=80,
strides=4,
padding='same',
kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(l=0.0001)))
m.add(BatchNormalization())
m.add(Activation('relu'))
m.add(MaxPooling1D(pool_size=4, strides=None))
m.add(Conv1D(256,
kernel_size=3,
strides=1,
padding='same',
kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(l=0.0001)))
m.add(BatchNormalization())
m.add(Activation('relu'))
m.add(MaxPooling1D(pool_size=4, strides=None))
m.add(Lambda(lambda x: K.mean(x, axis=1))) # Same as GAP for 1D Conv Layer
m.add(Dense(num_classes, activation='softmax'))
return m
def m5(num_classes=10):
print('Using Model M5')
m = Sequential()
m.add(Conv1D(128,
input_shape=[AUDIO_LENGTH, 1],
kernel_size=80,
strides=4,
padding='same',
kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(l=0.0001)))
m.add(BatchNormalization())
m.add(Activation('relu'))
m.add(MaxPooling1D(pool_size=4, strides=None))
m.add(Conv1D(128,
kernel_size=3,
strides=1,
padding='same',
kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(l=0.0001)))
m.add(BatchNormalization())
m.add(Activation('relu'))
m.add(MaxPooling1D(pool_size=4, strides=None))
m.add(Conv1D(256,
kernel_size=3,
strides=1,
padding='same',
kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(l=0.0001)))
m.add(BatchNormalization())
m.add(Activation('relu'))
m.add(MaxPooling1D(pool_size=4, strides=None))
m.add(Conv1D(512,
kernel_size=3,
strides=1,
padding='same',
kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(l=0.0001)))
m.add(BatchNormalization())
m.add(Activation('relu'))
m.add(MaxPooling1D(pool_size=4, strides=None))
m.add(Lambda(lambda x: K.mean(x, axis=1))) # Same as GAP for 1D Conv Layer
m.add(Dense(num_classes, activation='softmax'))
return m
def m11(num_classes=10):
print('Using Model M11')
m = Sequential()
m.add(Conv1D(64,
input_shape=[AUDIO_LENGTH, 1],
kernel_size=80,
strides=4,
padding='same',
kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(l=0.0001)))
m.add(BatchNormalization())
m.add(Activation('relu'))
m.add(MaxPooling1D(pool_size=4, strides=None))
for i in range(2):
m.add(Conv1D(64,
kernel_size=3,
strides=1,
padding='same',
kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(l=0.0001)))
m.add(BatchNormalization())
m.add(Activation('relu'))
m.add(MaxPooling1D(pool_size=4, strides=None))
for i in range(2):
m.add(Conv1D(128,
kernel_size=3,
strides=1,
padding='same',
kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(l=0.0001)))
m.add(BatchNormalization())
m.add(Activation('relu'))
m.add(MaxPooling1D(pool_size=4, strides=None))
for i in range(3):
m.add(Conv1D(256,
kernel_size=3,
strides=1,
padding='same',
kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(l=0.0001)))
m.add(BatchNormalization())
m.add(Activation('relu'))
m.add(MaxPooling1D(pool_size=4, strides=None))
for i in range(2):
m.add(Conv1D(512,
kernel_size=3,
strides=1,
padding='same',
kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(l=0.0001)))
m.add(BatchNormalization())
m.add(Activation('relu'))
m.add(Lambda(lambda x: K.mean(x, axis=1))) # Same as GAP for 1D Conv Layer
m.add(Dense(num_classes, activation='softmax'))
return m
def m_rec(num_classes=10):
from keras.layers.recurrent import LSTM
print('Using Model LSTM 1')
m = Sequential()
m.add(Conv1D(64,
input_shape=[AUDIO_LENGTH, 1],
kernel_size=80,
strides=4,
padding='same',
kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(l=0.0001)))
m.add(BatchNormalization())
m.add(Activation('relu'))
m.add(MaxPooling1D(pool_size=4, strides=None))
m.add(LSTM(32,
kernel_regularizer=regularizers.l2(l=0.0001),
return_sequences=True,
dropout=0.2))
m.add(LSTM(32,
kernel_regularizer=regularizers.l2(l=0.0001),
return_sequences=False,
dropout=0.2))
m.add(Dense(32))
m.add(Dense(num_classes, activation='softmax'))
return m
def m18(num_classes=10):
print('Using Model M18')
m = Sequential()
m.add(Conv1D(64,
input_shape=[AUDIO_LENGTH, 1],
kernel_size=80,
strides=4,
padding='same',
kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(l=0.0001)))
m.add(BatchNormalization())
m.add(Activation('relu'))
m.add(MaxPooling1D(pool_size=4, strides=None))
for i in range(4):
m.add(Conv1D(64,
kernel_size=3,
strides=1,
padding='same',
kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(l=0.0001)))
m.add(BatchNormalization())
m.add(Activation('relu'))
m.add(MaxPooling1D(pool_size=4, strides=None))
for i in range(4):
m.add(Conv1D(128,
kernel_size=3,
strides=1,
padding='same',
kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(l=0.0001)))
m.add(BatchNormalization())
m.add(Activation('relu'))
m.add(MaxPooling1D(pool_size=4, strides=None))
for i in range(4):
m.add(Conv1D(256,
kernel_size=3,
strides=1,
padding='same',
kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(l=0.0001)))
m.add(BatchNormalization())
m.add(Activation('relu'))
m.add(MaxPooling1D(pool_size=4, strides=None))
for i in range(4):
m.add(Conv1D(512,
kernel_size=3,
strides=1,
padding='same',
kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(l=0.0001)))
m.add(BatchNormalization())
m.add(Activation('relu'))
m.add(Lambda(lambda x: K.mean(x, axis=1))) # Same as GAP for 1D Conv Layer
m.add(Dense(num_classes, activation='softmax'))
return m