tenforflow学习笔记(七):cnn
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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]
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]
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
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