Question

我正在培训这种架构的手写识别模型：

{
"network": [
{
"layer_type": "l2_normalize"
},
{
"layer_type": "conv2d",
"num_filters": 16,
"kernel_size": 5,
"stride": 1,
"padding": "same"
},
{
"layer_type": "max_pool2d",
"pool_size": 2,
"stride": 2,
"padding": "same"
},
{
"layer_type": "l2_normalize"
},
{
"layer_type": "dropout",
"keep_prob": 0.5
},
{
"layer_type": "conv2d",
"num_filters": 32,
"kernel_size": 5,
"stride": 1,
"padding": "same"
},
{
"layer_type": "max_pool2d",
"pool_size": 2,
"stride": 2,
"padding": "same"
},
{
"layer_type": "l2_normalize"
},
{
"layer_type": "dropout",
"keep_prob": 0.5
},
{
"layer_type": "conv2d",
"num_filters": 64,
"kernel_size": 5,
"stride": 1,
"padding": "same"
},
{
"layer_type": "max_pool2d",
"pool_size": 2,
"stride": 2,
"padding": "same"
},
{
"layer_type": "l2_normalize"
},
{
"layer_type": "dropout",
"keep_prob": 0.5
},
{
"layer_type": "conv2d",
"num_filters": 128,
"kernel_size": 5,
"stride": 1,
"padding": "same"
},
{
"layer_type": "max_pool2d",
"pool_size": 2,
"stride": 2,
"padding": "same"
},
{
"layer_type": "l2_normalize"
},
{
"layer_type": "dropout",
"keep_prob": 0.5
},
{
"layer_type": "conv2d",
"num_filters": 256,
"kernel_size": 5,
"stride": 1,
"padding": "same"
},
{
"layer_type": "max_pool2d",
"pool_size": 2,
"stride": 2,
"padding": "same"
},
{
"layer_type": "l2_normalize"
},
{
"layer_type": "dropout",
"keep_prob": 0.5
},
{
"layer_type": "collapse_to_rnn_dims"
},
{
"layer_type": "birnn",
"num_hidden": 128,
"cell_type": "LSTM",
"activation": "tanh"
}
],
"output_layer": "ctc_decoder"
}

训练ctc损失在第一个训练时期突然下降，但高原在其余的时期波动。标签错误率不仅波动，而且似乎并没有降低。

我应该提到每个样本的序列长度非常接近最长地面实况的长度（即从1024进入ctc_loss时它变为32，接近最长的地面实况长度为21 ）。

对于图像的预处理，我确保在调整大小时保持纵横比，并右键填充图像~~使其成为正方形~~，以便所有图像都具有宽度和手写的单词将在左侧。我还颠倒了图像的颜色，使得手写字符具有最高像素值（255）和背景，而其余部分具有最低像素值（0）。

预测是这样的。第一部分上随机的字符串~~然后在结尾处有一堆零（这可能是因为填充而预期的）~~。

INFO:tensorflow:outputs = [[59 45 59 45 59 55 59 55 59 45 59 55 59 55 59 55 45 59  8 59 55 45 55  8
  45  8 45 59 45  8 59  8 45 59 45  8 45 19 55 45 55 45 55 59 45 59 45  8
  45  8 45 55  8 45  8 45 59 45 55 59 55 59  8 55 59  8 45  8 45  8 59  8
  59 45 59 45 59 45 59 45 59 45 59 45 19 45 55 45 22 45 55 45 55  8 45  8
  59 45 59 45 59 45 59 55  8 45 59 45 59 45 59 45 19 45 59 45 19 59 55 24
   4 52 54 55]]

这里是我如何将cnn输出折叠到dnn：

def collapse_to_rnn_dims(inputs):
    batch_size, height, width, num_channels = inputs.get_shape().as_list()
    if batch_size is None:
        batch_size = -1
    time_major_inputs = tf.transpose(inputs, (2, 0, 1, 3))
    reshaped_time_major_inputs = tf.reshape(time_major_inputs,
                                            [width, batch_size, height * num_channels]
                                            )
    batch_major_inputs = tf.transpose(reshaped_time_major_inputs, (1, 0, 2))
    return batch_major_inputs

这就是我如何崩溃到ctc dims：

def convert_to_ctc_dims(inputs, num_classes, num_steps, num_outputs):
    outputs = tf.reshape(inputs, [-1, num_outputs])
    logits = slim.fully_connected(outputs, num_classes,
                                  weights_initializer=slim.xavier_initializer())
    logits = slim.fully_connected(logits, num_classes,
                                  weights_initializer=slim.xavier_initializer())
    logits = tf.reshape(logits, [num_steps, -1, num_classes])
    return logits

增加标签错误率（编辑距离）和波动损失？

0 个答案: