diff --git a/spp b/spp
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index 0000000000000000000000000000000000000000..6b6b83215329f3835dab8fc31671b146fc65bc59
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+++ b/spp
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+import tensorflow as tf
+class SpatialPyramidPooling(tf.keras.layers.Layer):
+    def __init__(self, pyramid_levels):
+        super(SpatialPyramidPooling, self).__init__()
+        self.pyramid_levels = pyramid_levels
+
+    def call(self, inputs):
+        # 获取输入特征图的尺寸
+        _, h, w, c = inputs.get_shape().as_list()
+        # 初始化一个列表来存储不同金字塔级别的池化结果
+        pooled_features = []
+        for level in self.pyramid_levels:
+            # 计算每个金字塔级别的池化窗口大小
+            ksize = [h // level, w // level]
+            # 对每个级别应用最大池化
+            pooled = tf.nn.max_pool2d(inputs, ksize=ksize, strides=ksize, padding='VALID')
+            pooled_features.append(pooled)
+        # 沿着通道维度拼接所有池化结果
+        return tf.concat(pooled_features, axis=-1)
+class Model():
+    def build_network(self, training_options, image, drop_rate, labels):
+        with tf.variable_scope('CNN'):
+            # 卷积卷积卷积卷积
+            with tf.variable_scope('hidden1'):
+                hidden1 = self.cnn(image, 32, kernel_size=[3, 3])
+            with tf.variable_scope('hidden2'):
+                hidden2 = self.cnn(hidden1, 64, kernel_size=[3, 3])
+            with tf.variable_scope('hidden3'):
+                hidden3 = self.cnn(hidden2, 64, kernel_size=[3, 3])
+            # with tf.variable_scope('hidden4'):
+            #     hidden4 = self.cnn(hidden3, 100, kernel_size=[1, 1])
+            # 更改一下形状，因为全连接神经网络需要2维数据的input
+            #flatten = tf.reshape(hidden3, [-1, 4 * 9 * 64])  # --> 5 * 2 * 160 = 1600
+            # 添加SPP层
+            with tf.variable_scope('spp_layer'):
+                spp = SpatialPyramidPooling(pyramid_levels=[1, 2, 4])
+                spp_out = spp(hidden3)
+
+            # SPP层的输出可能需要扁平化以匹配全连接层的输入要求
+            flatten = tf.reshape(spp_out, [-1, c1 * c2 * c3])  # c1, c2, c3是SPP输出的尺寸
+
+            # 第一层全连接
+            with tf.variable_scope('hidden5'):
+                dense = tf.layers.dense(flatten, 1024, activation=tf.nn.relu)
+                hidden5 = tf.layers.dropout(dense, rate=drop_rate)
+            # 第二层全连接
+            with tf.variable_scope('hidden6'):
+                dense = tf.layers.dense(hidden5, 1024, activation=tf.nn.relu)
+                hidden6 = tf.layers.dropout(dense, rate=drop_rate)
+            # 第一个字符预测输出
+            with tf.variable_scope('digit1'):
+                dense = tf.layers.dense(hidden6, units=36)
+                self.digit1 = dense
+                tf.summary.histogram('digit1', self.digit1)
+            # 第二个
+            with tf.variable_scope('digit2'):
+                dense = tf.layers.dense(hidden6, units=36)
+                self.digit2 = dense
+                tf.summary.histogram('digit2', self.digit2)
+            # 第三个
+            with tf.variable_scope('digit3'):
+                dense = tf.layers.dense(hidden6, units=36)
+                self.digit3 = dense
+                tf.summary.histogram('digit3', self.digit3)
+            # 第四个
+            with tf.variable_scope('digit4'):
+                dense = tf.layers.dense(hidden6, units=36)
+                self.digit4 = dense
+                tf.summary.histogram('digit4', self.digit4)
+
+            layer = dict(
+                digit1=self.digit1,
+                digit2=self.digit2,
+                digit3=self.digit3,
+                digit4=self.digit4
+            )
+            train = self.train(training_options, labels)
+        return layer, train
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