densenet_model.py 文件源码

def densenet_block(inputs, layer_num, growth, bc_mode, scope, is_training, keep_prob):
    with tf.variable_scope(scope, 'block1', [inputs]):
        currents = inputs
        for idx in xrange(layer_num):
            if not bc_mode:
                new_feature = slim.conv2d(currents, growth,
                                          [3, 3], scope='conv_{:d}'.format(idx))
                new_feature = slim.dropout(new_feature, keep_prob=keep_prob,
                                           is_training=is_training,
                                           scope='dropout_{:d}'.format(idx))
            else:
                new_feature = slim.conv2d(currents, growth*4,
                                          [1, 1], scope='bottom_{:d}'.format(idx))
                new_feature = slim.dropout(new_feature, keep_prob=keep_prob,
                                           is_training=is_training,
                                           scope='dropout_b_{:d}'.format(idx))
                new_feature = slim.conv2d(new_feature, growth,
                                          [3, 3], scope='conv_{:d}'.format(idx))
                new_feature = slim.dropout(new_feature, keep_prob=keep_prob,
                                           is_training=is_training,
                                           scope='dropout_{:d}'.format(idx))
            currents = tf.concat([currents, new_feature], axis=3)
        return currents