如何在tensorflow中创建初始模块

Question

查看tensorflow页面：https://github.com/tensorflow/models/tree/master/inception

他们展示了他们的建筑形象，特别是他们的开始＆＃39;并行包含的模块：

• 1x1的转化层
• 转换层3x3
• 转换层5x5
• ave pooling + 1x1 conv

随后是一个＆＃39; concat＆＃39;层

如何在tensorflow中创建它？

我认为我可以按照这一点做一些事情来创建并行操作：

start_layer = input_data

filter = tf.Variable(tf.truncated_normal([1,1,channels,filter_count], stddev=0.1)
one_by_one = tf.nn.conv2d(start_layer, filter, strides=[1,1,1,1], padding='SAME')

filter = tf.Variable(tf.truncated_normal([3,3,channels,filter_count], stddev=0.1)
three_by_three = tf.nn.conv2d(start_layer, filter, strides=[1,1,1,1], padding='SAME')

filter = tf.Variable(tf.truncated_normal([5,5,channels,filter_count], stddev=0.1)
five_by_five = tf.nn.conv2d(start_layer, filter, strides=[1,1,1,1], padding='SAME')

filter = tf.Variable(tf.truncated_normal([1,1,channels,filter_count], stddev=0.1)
pooling = tf.nn.avg_pool(start_layer, filter, strides=[1,2,2,1], padding='SAME')

filter = tf.Variable(tf.truncated_normal([1,1,channels,filter_count], stddev=0.1)
pooling = tf.nn.conv2d(pooling, filter, strides=[1,1,1,1], padding='SAME')

#connect one_by_one, three_by_three, five_by_five, pooling into an concat layer

但是如何将4个操作组合成一个concat层呢？

Answer 1

我做了一些与你所做的非常相似的事情，然后用tf.concat()完成了它。注意{4}与我的4d张量匹配的axis=3和第4维度的索引（索引3）。 它的文档是here。

我的最终代码结束了这样的事情：

def inception2d(x, in_channels, filter_count):
    # bias dimension = 3*filter_count and then the extra in_channels for the avg pooling
    bias = tf.Variable(tf.truncated_normal([3*filter_count + in_channels], mu, sigma)),

    # 1x1
    one_filter = tf.Variable(tf.truncated_normal([1, 1, in_channels, filter_count], mu, sigma))
    one_by_one = tf.nn.conv2d(x, one_filter, strides=[1, 1, 1, 1], padding='SAME')

    # 3x3
    three_filter = tf.Variable(tf.truncated_normal([3, 3, in_channels, filter_count], mu, sigma))
    three_by_three = tf.nn.conv2d(x, three_filter, strides=[1, 1, 1, 1], padding='SAME')

    # 5x5
    five_filter = tf.Variable(tf.truncated_normal([5, 5, in_channels, filter_count], mu, sigma))
    five_by_five = tf.nn.conv2d(x, five_filter, strides=[1, 1, 1, 1], padding='SAME')

    # avg pooling
    pooling = tf.nn.avg_pool(x, ksize=[1, 3, 3, 1], strides=[1, 1, 1, 1], padding='SAME')

    x = tf.concat([one_by_one, three_by_three, five_by_five, pooling], axis=3)  # Concat in the 4th dim to stack
    x = tf.nn.bias_add(x, bias)
    return tf.nn.relu(x)