pretrained_word_embedding_TF_nn.py 文件源码

def max_pool1d_layer(inp, ksize, strides):
    """tf.nn does not have max_pool_1d, so we have to expand the incoming layer
    as if we were dealing with a 2D convolution and then squeeze it again.
    Again, since this is a 1D conv, the size of the window (ksize) and the stride
    of the sliding window must have only one dimension (height) != 1
    """
    x = tf.expand_dims(inp, 3)
    x = tf.nn.max_pool(x, ksize=ksize, strides=strides, padding="VALID")
    x = tf.squeeze(x, [3])
    return x