def Conv(input_dim, classes):
if len(input_dim) == 1:
input_dim = (input_dim[0], 1)
check_input_dimensions(input_dim)
# Esto dio 99% de accuracy con el conjunto de entrenamiento de los murcielagos, con meanMFCCs
# NO TOCAR
model = Sequential()
model.add(Conv1D(15, 5, padding="same", input_shape=input_dim))
# model.add(Activation("tanh"))
model.add(LeakyReLU(alpha=0.3))
# model.add(MaxPooling1D())
model.add(AveragePooling1D(padding="same"))
model.add(Conv1D(40, 5, padding="same"))
model.add(Activation("tanh"))
model.add(MaxPooling1D())
model.add(Flatten())
# model.add(Dense(100))
# model.add(LeakyReLU(alpha=0.3))
# model.add(Dropout(0.5))
model.add(Dense(600))
model.add(Activation("tanh"))
# model.add(Dropout(0.5))
# model.add(LeakyReLU(alpha=0.3))
model.add(Dense(classes))
model.add(Activation("softmax"))
return model
def Not_Conv(input_dim, classes):
check_input_dimensions(input_dim)
model = Sequential()
model.add(Dense(30, input_shape=input_dim))
# model.add(Activation("tanh"))
model.add(LeakyReLU(alpha=0.3))
model.add(Dense(60))
model.add(Activation("tanh"))
# model.add(Dropout(0.4))
# model.add(Dense(100))
# model.add(LeakyReLU(alpha=0.3))
# model.add(Dropout(0.5))
model.add(Dense(100))
model.add(Activation("tanh"))
# model.add(Dropout(0.5))
# model.add(LeakyReLU(alpha=0.3))
model.add(Dense(classes))
model.add(Activation("softmax"))
return model
def NotConv(input_dim, classes):
check_input_dimensions(input_dim)
model = Sequential()
model.add(Dense(50, input_shape=input_dim))
model.add(Activation("tanh"))
# model.add(LeakyReLU(alpha=0.3))
model.add(Dense(200))
model.add(Activation("tanh"))
model.add(Dropout(0.7))
# for i in range(5):
model.add(Dense(500))
model.add(Activation("tanh"))
model.add(Dropout(0.6))
model.add(Dense(500))
model.add(Activation("tanh"))
# model.add(LeakyReLU(alpha=0.3))
model.add(Dense(classes))
model.add(Activation("softmax"))
return model
def build(input_shape, classes):
check_input_dimensions(input_shape)
model = Sequential()
# CONV => RELU => POOL
model.add(Conv2D(20, (5, 5), padding="same", input_shape=input_shape))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# CONV => RELU => POOL
# for i in range(3):
model.add(Conv2D(50, (7, 7), padding="same"))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# Flatten => RELU layers
model.add(Flatten())
model.add(Dense(500))
model.add(Activation("relu"))
# a softmax classifier
model.add(Dense(classes))
model.add(Activation("softmax"))
return model
def build(input_shape, classes):
check_input_dimensions(input_shape)
# model = Sequential()
# model.add(Conv2D(32, (3, 3), padding='same',
# input_shape=input_shape))
# model.add(Activation('relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))
# model.add(Flatten())
# model.add(Dense(512))
# model.add(Activation('relu'))
# model.add(Dropout(0.5))
# model.add(Dense(classes))
# model.add(Activation('softmax'))
# model.summary()
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same', input_shape=input_shape))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(classes))
model.add(Activation('softmax'))
return model
def Conv2D(input_dim, classes):
check_input_dimensions(input_dim)
model = Sequential()
model.add(Conv2D(15, (5, 5), padding="same", input_shape=input_dim))
# model.add(Activation("tanh"))
model.add(LeakyReLU(alpha=0.3))
# model.add(MaxPooling1D())
model.add(AveragePooling2D(padding="same"))
model.add(Conv2D(40, (5, 5), padding="same"))
model.add(Activation("tanh"))
model.add(MaxPooling2D())
model.add(Flatten())
# model.add(Dense(100))
# model.add(LeakyReLU(alpha=0.3))
# model.add(Dropout(0.5))
model.add(Dense(600))
model.add(Activation("tanh"))
# model.add(Dropout(0.5))
# model.add(LeakyReLU(alpha=0.3))
model.add(Dense(classes))
model.add(Activation("softmax"))
return model