inputs=tf.placeholder(tf.float32,shape=[None,None,None,3])conv1=slim.conv2d(inputs,num_outputs=20,kernel_size=3,stride=4)de_weight=tf.get_variable('de_weight',shape=[3,3,3,20])deconv1=tf.nn.conv2d_transpose(conv1,filter=de_weight,output_shape=tf.shape(inputs),strides=[1,3,3,...
TensorFlow已经实现了卷积(tf.nn.conv2d卷积函数),反卷积(tf.nn.conv2d_transpose反卷积函数)以及空洞卷积(tf.nn.atrous_conv2d空洞卷积(dilated convolution)),这三个函数的参数理解,可参考网上。比较难的是计算维度,这里提供三种方式封装卷积、反卷积和空洞卷积的方法,方面调用: 一、卷积 输入图片大小W×W Filter...
tf.nn.conv2d中的filter参数,是[filter_height, filter_width, in_channels, out_channels]的形式,而tf.nn.conv2d_transpose中的filter参数,是[filter_height, filter_width, out_channels,in_channels]的形式,注意in_channels和out_channels反过来了!因为两者互为反向,所以输入输出要调换位置 既然y2是卷积操作的...
tf.nn.conv2d_transpose(value, filter, output_shape, strides, padding='SAME', name=None) 参数的设置和conv2d(卷积还是有一定区别的),比如第二个参数:先写output_channels,再写in_channels 这里的filter与conv2d有一点区别,反卷积【height,width,output_channels,in_channels】;卷积【height,width,in_channels...
tf.shape(y) 1. 2. 3. 4. 结果如下: (6)进行conv2d的转置 tf.nn.conv2d_transpose(value, filter, output_shape, strides, padding=‘SAME’, data_format=‘NHWC’, name=None) output_shape: 一维的张量,表示反卷积运算后输出的形状。
然而, 如果设计的模型中, 有转置卷积网络, 其中用到了tf.nn.conv2d_transpose()函数, 那么该函数中的output_shape, 需要按照如下形式进行设置: x = tf.nn.conv2d_transpose(x,w_t3,output_shape=tf.stack([tf.shape(x)[0],z_dim[0],z_dim[1],z_dim[2]]),strides=(1,8,8,1),padding='SAME...
tf.nn.conv2d_transpose也就是转置卷积(反卷积)。 tf.nn.conv1d类似于二维卷积,用来计算给定三维输入和过滤器情况下的一维卷积。 tf.nn.conv3d与二维卷积类似,用来计算给定五维输入和过滤器的情况下的三维卷积. 具体的测试函数如下: # - * - coding: utf - 8 -*-importtensorflowastfimportosimportnumpyasnpo...
nn.conv2d_transpose(inp, w, out_shape, strides=[1, 1, 1, 1], padding="SAME") var = tf.nn.bias_add(var, b) if not dropout_prob is None: var = tf.nn.relu(var) var = tf.nn.dropout(var, dropout_prob) return var weights = { "conv1": tf.Variable(tf.random_normal([3, ...
Intf.nn, there are 4 closely related 2d conv functions: tf.nn.conv2d tf.nn.conv2d_backprop_filter tf.nn.conv2d_backprop_input tf.nn.conv2d_transpose defconv2d(input, filter, strides, padding, use_cudnn_on_gpu=True, data_format="NHWC", name=None): ...
https://github.com/vdumoulin/conv_arithmeticgithub.com/vdumoulin/conv_arithmetic Tensorflow 中反卷积的实现: tf.nn.conv2d_transpose( value, filter, output_shape, strides, padding='SAME', data_format='NHWC', name=None ) 参数: value:做反卷积的输入数据,要求为一个四维浮点型张量,其shape为[...