本文主要介紹前兩個函式tf.nn.conv2d和tf.contrib.layers.conv2d
因為tf.contrib.layers.conv2d 和 tf.contrib.slim.conv2d用法是相似的，都是高階api，只是slim是一個更高級別的庫，用slim中的repeat函式，可以用幾行就寫出一個vgg16網路。但是tf.nn和tf.contrib.layers是基本ops，最常見也最重要。

首先說tf.nn.conv2d()的引數

tf.nn.conv2d(
    input,
    filter,
    strides,
    padding,
    use_cudnn_on_gpu=True
 
,
    data_format='NHWC',
    dilations=[1, 1, 1, 1],
    name=None
)

input的格式由data_format引數決定，預設都是‘NHWC’，也就是batch size；feature map height；feature map width；channels。
filter是單獨定義的, 可以用tf.get_variable()或者tf.Variable()來定義weight，關於這兩種定義方法，可以參見我的另一篇博文
以tf.Variable和tf.nn.conv2d為例子來建立一個卷積層：

def create_weights
 
(shape):
    return tf.Variable(tf.truncated_normal(shape,stddev=0.1))

def create_bias(num_filters):
    return tf.Variable(tf.constant(0.05,shape=num_filters))

def create_conv_layer(input,num_channels,filtr_size,num_filters):
    weights=create_weights(shape=[input,num_channels,filtr_size,num_filter])
    bias=create_bias(num_filters)
    layer=tf.nn.conv2d(input=input,filter=weights,stride=[1
 
,1,1,1],padding='same')
    layer+=bias
    layer=tf.nn.max_pool(layer,ksize=[1,2,2,1],strides=[1,2,2,1],padding='same')
    return layer

以上是建立卷積層最基礎的方式。
接下來來看用tf.contrib.layers.conv2d建立卷積層。

tf.contrib.layers.conv2d(
    inputs, #四維tensor ‘NHDC’
    num_outputs, #filter的數量
    kernel_size,#[k,k]
    stride=1,#預設為[1,1]
    padding='SAME',#valid or same 
    data_format=None,#NHDC
    rate=1,#dilation rate
    activation_fn=tf.nn.relu,#預設是relu，可選tf.nn.leaky_relu
    normalizer_fn=None,
    normalizer_params=None,
    weights_initializer=tf.contrib.layers.xavier_initializer(),
    weights_regularizer=None,
    biases_initializer=tf.zeros_initializer(),
    biases_regularizer=None,
    reuse=None,
    variables_collections=None,
    outputs_collections=None,
    trainable=True,
    scope=None
)

這裡提一下tf.contrib.layers.xavier_initializer()
這個比 tf.truncated_normal_initializer好一點(不過有人做實驗顯示兩者效果差不多）。
xavier initializer is designed to keep the scale of the gradients roughly the same in all layers.
In uniform distribution, tf.contrib.layers.xavier_initializer(uniform=True) this ends up being the range: x = sqrt(6. / (in + out)); [-x, x]
In normal distribution, tf.contrib.layers.xavier_initializer(uniform=False) a standard deviation of sqrt(3. / (in + out)) is used.
有以下幾篇介紹xavier initialier及其他initializer的文章很詳細
1 why xavier and He initializer
2 聊一聊weights initializer
3 這篇也不錯
tf.contrib.layers.conv2d()簡單的example是：

def mynet(input,reuse=False):
    with tf.variable_scope("conv1") as scope:
        net=tf.contrib.layers.conv2d(input,32,[5,5],padding='SAME',weights_initializer=tf.contrib.layers.xavier_initializer(uniform=False),scope=scope,reuse=reuse)
        net=tf.contrib.layers.max_pool2d(net,[2,2],padding='SAME')
    with tf.variable_scope("conv2") as scope:
        ...
    net=tf.contrib.layers.flatten(net)
    return net