def conv_block8(feat_maps_out, prev): prev = BatchNormalization(axis=CHANNEL_AXIS, name='BN8_1', freeze=False)(prev) # Specifying the axis and mode allows for later merging prev = layers.LeakyReLU(name='Activation8_1')(prev) prev = layers.Conv2D(feat_maps_out, (3,3), padding = 'same', kernel_initializer = 'he_normal', name='Conv8_1')(prev) prev = BatchNormalization(axis=CHANNEL_AXIS, name='BN8_2', freeze=False)(prev) # Specifying the axis and mode allows for later merging prev = layers.LeakyReLU(name='Activation8_2')(prev) prev = layers.Conv2D(feat_maps_out, (3,3), padding = 'same', kernel_initializer = 'he_normal', name='Conv8_2')(prev) return prev
def f(input): y = Conv2D(filters, 3, strides=stride, padding="same", kernel_initializer="he_normal", kernel_regularizer=l2(1e-4))(input) y = BatchNormalization(axis=3, freeze=False)(y) y = LeakyReLU()(y) y = Conv2D(filters, 3, strides=1, padding="same", kernel_initializer="he_normal", kernel_regularizer=l2(1e-4))(y) y = BatchNormalization(axis=3, freeze=False)(y) if block == 0: shortcut = Conv2D(filters, 1, strides=stride, padding="same", kernel_initializer="he_normal", kernel_regularizer=l2(1e-4))(input) shortcut = BatchNormalization(axis=3, freeze=False)(shortcut) else: shortcut = input y = Add()([y, shortcut]) y = LeakyReLU()(y) return y
def f(input): x = Conv2D(48, 3, padding='same', use_bias=False, kernel_initializer='he_normal', kernel_regularizer=l2(weight_decay))(input) x = BatchNormalization(axis=3, freeze=False)(x) x = LeakyReLU()(x) return x
def initial_conv_block1(input, weight_decay=5e-4): x = layers.Conv2D(32, (3, 3), padding='same', use_bias=False, kernel_initializer='he_normal', kernel_regularizer=l2(weight_decay), name='conv1_1')(input) x = BatchNormalization(axis=CHANNEL_AXIS, name='BN1_1', freeze=True)(x) x = layers.LeakyReLU(name='Activation1_1')(x) return x
def f(input): b = Conv1D(filters=filters, kernel_size=1, strides=1, padding="same", #dilation_rate=dilationrate1D, kernel_initializer="he_normal", kernel_regularizer=l2(1e-4))(input) b = BatchNormalization(axis=3, freeze=False)(b) b = LeakyReLU()(b) b_res = b b = Conv1D(filters=filters, kernel_size=1, strides=1, padding="same", kernel_initializer="he_normal", kernel_regularizer=l2(1e-4))(b) b = BatchNormalization(axis=3, freeze=False)(b) b_attention = Activation("softmax")(b) b = Conv2D(filters=filters, kernel_size=3, strides=(1,1), padding="same", #dilation_rate=dilationrate2D, kernel_initializer="he_normal", kernel_regularizer=l2(1e-4))(b) b = BatchNormalization(axis=3, freeze=False)(b) b = Multiply()([b, b_attention]) b = LeakyReLU()(b) b = Conv1D(filters=filters, kernel_size=1, strides=1, padding="same", kernel_initializer="he_normal", kernel_regularizer=l2(1e-4))(b) b = BatchNormalization(axis=3, freeze=False)(b) b = Add()([b, b_res]) b = LeakyReLU()(b) return b
def _bn_relu(input): """Helper to build a BN -> relu block """ norm = BatchNormalization(axis=CHANNEL_AXIS, freeze=False)(input) return Activation("relu")(norm)