def __init__(self, filters=1, kernel_size=80, rank=1, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, activation=None, use_bias=True, fsHz=1000., fc_initializer=initializers.RandomUniform(minval=10, maxval=400), n_order_initializer=initializers.constant(4.), amp_initializer=initializers.constant(10 ** 5), beta_initializer=initializers.RandomNormal(mean=30, stddev=6), bias_initializer='zeros', **kwargs): super(Conv1D_gammatone, self).__init__(**kwargs) self.rank = rank self.filters = filters self.kernel_size_ = kernel_size self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size') self.strides = conv_utils.normalize_tuple(strides, rank, 'strides') self.padding = conv_utils.normalize_padding(padding) self.data_format = conv_utils.normalize_data_format(data_format) self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, rank, 'dilation_rate') self.activation = activations.get(activation) self.use_bias = use_bias self.bias_initializer = initializers.get(bias_initializer) self.fc_initializer = initializers.get(fc_initializer) self.n_order_initializer = initializers.get(n_order_initializer) self.amp_initializer = initializers.get(amp_initializer) self.beta_initializer = initializers.get(beta_initializer) self.input_spec = InputSpec(ndim=self.rank + 2) self.fsHz = fsHz self.t = tf.range(start=0, limit=kernel_size / float(fsHz), delta=1 / float(fsHz), dtype=K.floatx()) self.t = tf.expand_dims(input=self.t, axis=-1)
def __init__(self, hyper_p={}): self.__dict__.update(self.Default, **hyper_p) if K.backend() != 'tensorflow': print('backend is ', K.backend()) self.init_method = initializers.RandomNormal(mean=0.0, stddev=0.05, seed=None) # load piplines of 2 conected models self.recognition = self.encoder() self.generator = self.decoder() # define main network inputs input = Input(shape=(self.inp_shape, ), dtype='float32', name='VAE_input') mask_input = Input(shape=(self.inp_shape, ), dtype='float32', name='mask_input') self.z_mu, self.z_var = self.recognition(input) vae_out = self.generator([self.z_mu, self.z_var]) ## generate vae_model by conecting recognition_model to generator_model self.vae_model = Model(inputs=[input, mask_input], outputs=vae_out, name='VAE') custom_loss = partial(self.custom_loss, mask_input) custom_loss.__name__ = "masked_bce" method = getattr(optimizers, self.optimiz) self.vae_model.compile(optimizer=method(lr=self.lr_rate), loss=custom_loss)
x_test[i] = data.reshape(dim, dim, 1) y_test[i] = object_file['label'] # one_hot_labels = keras.utils.to_categorical(object_file['label'], num_classes=2) # y_test[i]=one_hot_labels training_data = objectnessgenerator.DataGenerator(filelist, mylist, trainingfolder, winubu, dim) model = Sequential() model.add( Conv2D(32, (6, 6), activation='relu', input_shape=(64, 64, 1), kernel_initializer=initializers.RandomNormal(stddev=0.001))) model.add(MaxPooling2D(pool_size=(2, 2), padding='same')) model.add(BatchNormalization()) #model.add(Dropout(0.1)) model.add( Conv2D(64, (3, 3), activation='relu', kernel_initializer=initializers.RandomNormal(stddev=0.001))) model.add(MaxPooling2D(pool_size=(2, 2), padding='same')) model.add(BatchNormalization()) #model.add(Dropout(0.1)) model.add( Conv2D(128, (3, 3), activation='relu', kernel_initializer=initializers.RandomNormal(stddev=0.001)))