tensorflow實現1維卷積
官方參考文檔：https://www.tensorflow.org/api_docs/python/tf/nn/conv1d
參考網頁：
http://www.riptutorial.com/tensorflow/example/19385/basic-example
http://www.riptutorial.com/tensorflow/example/30750/math-behind-1d-convolution-with-advanced-examples-in-tf
tensorflow從版本r0.11起，開始支持1維卷積操作:tf.nn.conv1d.
input的維度設置：[batch_size, 每個input的元素個數, input的通道數]
卷積核(filter)的維度設置: [卷積核長度, input的通道數，輸出通道數]
最簡單的1維卷積法：使用tf的conv1d函數，設置padding=0,stride=1，舉例說明：
input = [1, 0, 2, 3, 0, 1, 1] and kernel = [2, 1, 3] the result of the convolution is [8, 11, 7, 9, 4],
which is calculated in the following way:
- 8 = 1 * 2 + 0 * 1 + 2 * 3
- 11 = 0 * 2 + 2 * 1 + 3 * 3
- 7 = 2 * 2 + 3 * 1 + 0 * 3
- 9 = 3 * 2 + 0 * 1 + 1 * 3
- 4 = 0 * 2 + 1 * 1 + 1 * 3
tensorflow支持2種擴展模式(padding)，第一種是‘VALID‘，第二種是‘SAME‘
‘VALID‘方式表示不進行擴展; ‘SAME‘表示添加0；比如，對input進行‘SAME‘的擴展時,得到的是：input = [0, 1, 0, 2, 3, 0, 1, 1, 0], 卷積後的output= [1, 8, 11, 7, 9, 4, 3]
設置卷積核每一次滑動2次：
res = tf.squeeze(tf.nn.conv1d(data, kernel, 2, ‘SAME‘)) with tf.Session() as sess: print sess.run(res)
tensorflow實現1維卷積