tenforflow学习笔记（七）：cnn

tenforflow:CNN

常用函数

卷积函数

1.tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, data_format=None, name=None)
2.tf.nn.depthwise_conv2d(input, filter, strides, padding, name=None)
3.tf.nn.separable_conv2d(input, depthwise_filter, pointwise_filter, strides, padding, name=None)
4.tf.nn.atrous_conv2d(value, filters, rate, padding, name=None)

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

def conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, data_format=None, name=None):
#input:shape [batch_size, in_height, in_width, in_channels].channels的意思是就是，如果你输入的是RGB图像，channel就是3
#filter:shape [filter_height, filter_width, in_channels, out_channels], out_channels表示输出几张feature map
#strides:A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.
#stride:stride=[1,h_stride,v_stride,1] ，strides[0]=strides[3]=1!!!!!!第0个是batch中的样本，第三个是channel
#padding:两种形式"VALID"和"SAME","VALID"不会去补0,"SAME"和"VALID"一样运算，不够的时候，会补0，不知为啥tensorflow没有"FULL"
#输出:[batch_size, out_height, out_width, out_channels]

图一：conv2d的输入输出
每三个filter看作一组，每组中的权值不是共享的，组之间也不是共享的

def depthwise_conv2d(input, filter, strides, padding, name=None):
#input:shape [batch_size, in_height, in_width, in_channels]
#filter:shape [filter_height, filter_width, in_channels, channel_multiplier]
#strides:同上
#padding:同上
#return:shape [batch, out_height, out_width, in_channels * channel_multiplier]

图二：depthwise_conv2d的输入输出
各filter之间权值不共享
剩下两个函数暂时还没研究
tf.nn.bias_add(input, biases). 求完卷积后，用这个加上bias

def bias_add(input, biases):
"""Adds `bias` to `value`. This is (mostly) a special case of `tf.add` where `bias` is restricted to 1-D. Broadcasting is supported, so `value` may have any number of dimensions. Unlike `tf.add`, the type of `bias` is allowed to differ from `value` in the case where both types are quantized. Args: value: A `Tensor` with type `float`, `double`, `int64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, or `complex128`. bias: A 1-D `Tensor` with size matching the last dimension of `value`. Must be the same type as `value` unless `value` is a quantized type, in which case a different quantized type may be used. data_format: A string. 'NHWC' and 'NCHW' are supported. name: A name for the operation (optional).

POOLING函数

1.tf.nn.avg_pool(value, ksize, strides, padding, data_format=’NHWC’, name=None)
2.tf.nn.max_pool(value, ksize, strides, padding, data_format=’NHWC’, name=None)
3.tf.nn.max_pool_with_argmax(input, ksize, strides, padding, Targmax=None, name=None)

def avg_pool(value, ksize, strides, padding, data_format='NHWC', name=None):
#value:shape [batch, height, width, channels]
#ksize:A list of ints that has length >= 4. The size of the window for each dimension of the input tensor.
#strides:A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.一般为[1, h_stride, v_stride, 1]
#return: [batch, out__height, out_width, out_channels]

#max_pool与avg_pool相似