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