conv2d函数原型：
tf.nn.conv2d ( input, filter, strides, padding, use_cudnn_on_gpu=None, name=None )
参数说明：
input：输入图像，shape为 [ batch, in_height, in_width, in_channels ]，例如 [ 1, 28, 28, 1 ]
filter：卷积核，shape为 [ filter_height, filter_width, in_channels, out_channels ]，例如 [ 5, 5, 1, 1 ]
strides：步长，一维向量，长度为 4，例如 [ 1, 1, 1, 1 ]
padding：string类型，取值为‘SAME’或‘VALID’，例如‘SAME’
第一种padding方式：‘SAME’
1、卷积后的Feature map尺寸的计算方式为：
out_height = ceil ( float ( in_height ) / float ( strides [1] ) ) out_width = ceil ( float ( in_width ) / float ( strides [2] ) )
2、例子：
import tensorflow as tf
x_image = tf.Variable ( tf.random_normal ( [ 1, 28, 28, 1] ) )
kernel = tf.Variable ( tf.random_normal ( [5, 5, 1, 1] ) )
conv = tf.nn.conv2d ( x_image, kernel, strides= [ 1, 1, 1, 1 ], padding='SAME' )
with tf.Session () as sess:
sess.run ( tf.global_variables_initializer () )
result = ( sess.run ( conv ) )
print ( result.shape )
程序运行结果：
====== RESTART: D:\Python_code\ML\test.py ====== ( 1, 28, 28, 1 ) >>>
3、经过卷积后的Feature map尺寸的计算方式说明：
out_height = ceil ( float ( in_height ) / float ( strides[1] ) ) = ceil ( float ( 28 ) / float ( 1 ) ) = 28 out_width = ceil ( float ( in_width ) / float ( strides[2] ) ) = ceil ( float ( 28 ) / float ( 1 ) ) = 28
第二种padding方式：‘VALID’
1、卷积后的Feature map尺寸的计算方式为：
out_height = ceil ( float ( in_height - filter_height + 1 ) / float ( strides[1] ) ) out_width = ceil ( float ( in_width - filter_width + 1 ) / float ( strides[2] ) )
2、例子：
import tensorflow as tf
x_image = tf.Variable ( tf.random_normal ( [ 1, 28, 28, 1] ) )
kernel = tf.Variable ( tf.random_normal ( [5, 5, 1, 1] ) )
conv = tf.nn.conv2d ( x_image, kernel, strides= [ 1, 1, 1, 1 ], padding='VALID' )
with tf.Session () as sess:
sess.run ( tf.global_variables_initializer () )
result = ( sess.run ( conv ) )
print ( result.shape )
程序运行结果：
====== RESTART: D:\Python_code\ML\test.py ====== ( 1, 24, 24, 1 ) >>>
3、经过卷积后的Feature map尺寸的计算方式说明：
out_height = ceil ( float (i n_height - filter_height + 1 ) / float ( strides[1] ) ) = ceil ( float ( 28 - 5+ 1 ) / float ( 1 ) ) = 24 out_width = ceil ( float ( in_width - filter_width + 1 ) / float ( strides[2] ) ) = ceil ( float ( 28 - 5+ 1 ) / float ( 1 ) ) = 24
