nasnet.py 文件源码

def _normal_A(ip, p, filters, weight_decay=5e-5, id=None):
    '''Adds a Normal cell for NASNet-A (Fig. 4 in the paper)

    # Arguments:
        ip: input tensor `x`
        p: input tensor `p`
        filters: number of output filters
        weight_decay: l2 regularization weight
        id: string id

    # Returns:
        a Keras tensor
    '''
    channel_dim = 1 if K.image_data_format() == 'channels_first' else -1

    with K.name_scope('normal_A_block_%s' % id):
        p = _adjust_block(p, ip, filters, weight_decay, id)

        h = Activation('relu')(ip)
        h = Conv2D(filters, (1, 1), strides=(1, 1), padding='same', name='normal_conv_1_%s' % id,
                   use_bias=False, kernel_initializer='he_normal', kernel_regularizer=l2(weight_decay))(h)
        h = BatchNormalization(axis=channel_dim, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
                               name='normal_bn_1_%s' % id)(h)

        with K.name_scope('block_1'):
            x1_1 = _separable_conv_block(h, filters, kernel_size=(5, 5), weight_decay=weight_decay,
                                         id='normal_left1_%s' % id)
            x1_2 = _separable_conv_block(p, filters, weight_decay=weight_decay, id='normal_right1_%s' % id)
            x1 = add([x1_1, x1_2], name='normal_add_1_%s' % id)

        with K.name_scope('block_2'):
            x2_1 = _separable_conv_block(p, filters, (5, 5), weight_decay=weight_decay, id='normal_left2_%s' % id)
            x2_2 = _separable_conv_block(p, filters, (3, 3), weight_decay=weight_decay, id='normal_right2_%s' % id)
            x2 = add([x2_1, x2_2], name='normal_add_2_%s' % id)

        with K.name_scope('block_3'):
            x3 = AveragePooling2D((3, 3), strides=(1, 1), padding='same', name='normal_left3_%s' % (id))(h)
            x3 = add([x3, p], name='normal_add_3_%s' % id)

        with K.name_scope('block_4'):
            x4_1 = AveragePooling2D((3, 3), strides=(1, 1), padding='same', name='normal_left4_%s' % (id))(p)
            x4_2 = AveragePooling2D((3, 3), strides=(1, 1), padding='same', name='normal_right4_%s' % (id))(p)
            x4 = add([x4_1, x4_2], name='normal_add_4_%s' % id)

        with K.name_scope('block_5'):
            x5 = _separable_conv_block(h, filters, weight_decay=weight_decay, id='normal_left5_%s' % id)
            x5 = add([x5, h], name='normal_add_5_%s' % id)

        x = concatenate([p, x1, x2, x3, x4, x5], axis=channel_dim, name='normal_concat_%s' % id)
    return x, ip