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卷积神经网络实践

数据处理

  • dataset处理成四维的，label仍然作为one-hot encoding
def reformat(dataset, labels, image_size, num_labels, num_channels):
    dataset = dataset.reshape(
        (-1, image_size, image_size, num_channels)).astype(np.float32)
    labels = (np.arange(num_labels) == labels[:, None]).astype(np.float32)
    return dataset, labels
  • 将lesson2的dnn转为cnn很简单，只要把WX+b改为conv2d(X)+b即可
  • 关键在于conv2d

tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, data_format=None, name=None) {#conv2d}

给定四维的input和filter tensor，计算一个二维卷积

Args:
  • input: A Tensor. type必须是以下几种类型之一: half, float32, float64.
  • filter: A Tensor. type和input必须相同
  • strides: A list of ints.一维，长度4， 在input上切片采样时，每个方向上的滑窗步长，必须和format指定的维度同阶
  • padding: A string from: "SAME", "VALID". padding 算法的类型
  • use_cudnn_on_gpu: An optional bool. Defaults to True.
  • data_format: An optional string from: "NHWC", "NCHW"， 默认为"NHWC"。 指定输入输出数据格式，默认格式为"NHWC", 数据按这样的顺序存储： [batch, in_height, in_width, in_channels] 也可以用这种方式："NCHW", 数据按这样的顺序存储： [batch, in_channels, in_height, in_width]
  • name: 操作名，可选.
Returns:

A Tensor. type与input相同

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels]

conv2d实际上执行了以下操作：

  1. 将filter转为二维矩阵，shape为 [filter_height * filter_width * in_channels, output_channels].
  2. 从input tensor中提取image patches，每个patch是一个virtual tensor，shape[batch, out_height, out_width, filter_height * filter_width * in_channels].
  3. 将每个filter矩阵和image patch向量相乘

具体来讲，当data_format为NHWC时：

output[b, i, j, k] =
    sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] *
                    filter[di, dj, q, k]

input 中的每个patch都作用于filter，每个patch都能获得其他patch对filter的训练 需要满足strides[0] = strides[3] = 1. 大多数水平步长和垂直步长相同的情况下：strides = [1, stride, stride, 1].

  • 然后再接一个WX+b连Relu连WX+b的全连接神经网络即可

Max Pooling

在tf.nn.conv2d后面接tf.nn.max_pool，将卷积层输出减小，从而减少要调整的参数

tf.nn.max_pool(value, ksize, strides, padding, data_format='NHWC', name=None) {#max_pool}

Performs the max pooling on the input.

Args:
  • value: A 4-D Tensor with shape [batch, height, width, channels] and type tf.float32.
  • ksize: A list of ints that has length >= 4. 要执行取最值的切片在各个维度上的尺寸
  • strides: A list of ints that has length >= 4. 取切片的步长
  • padding: A string, either 'VALID' or 'SAME'. padding算法
  • data_format: A string. 'NHWC' and 'NCHW' are supported.
  • name: 操作名，可选
Returns:

A Tensor with type tf.float32. The max pooled output tensor.

优化

仿照lesson2，添加learning rate decay 和 drop out，可以将准确率提高到90.6%

补充

  • 最近在用GPU版本的TensorFlow，发现，如果import tensorflow放在代码第一行，运行会报段错误（pycharm debug模式下不会），因此最好在import tensorflow前import numpy或者其他的module

参考链接