add

models/cnn_net.py

@@ -1,24 +1,5 @@
-'''
 
 
-
-conv_1 = slim.conv2d(images, 64, [3, 3], 1, padding='SAME', scope='conv1')
-# (inputs,num_outputs,[卷积核个数] kernel_size,[卷积核的高度，卷积核的宽]stride=1,padding='SAME',)
-max_pool_1 = slim.max_pool2d(conv_1, [2, 2], [2, 2], padding='SAME')
-conv_2 = slim.conv2d(max_pool_1, 128, [3, 3], padding='SAME', scope='conv2')
-max_pool_2 = slim.max_pool2d(conv_2, [2, 2], [2, 2], padding='SAME')
-conv_3 = slim.conv2d(max_pool_2, 256, [3, 3], padding='SAME', scope='conv3')
-max_pool_3 = slim.max_pool2d(conv_3, [2, 2], [2, 2], padding='SAME')
-
-flatten = slim.flatten(max_pool_3)
-fc1 = slim.fully_connected(tf.nn.dropout(flatten, keep_prob), 1024, activation_fn=tf.nn.tanh, scope='fc1')
-logits = slim.fully_connected(tf.nn.dropout(fc1, keep_prob), FLAGS.charset_size, activation_fn=None, scope='fc2')
-# logits = slim.fully_connected(flatten, FLAGS.charset_size, activation_fn=None, reuse=reuse, scope='fc')
-loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels))
-# y表示的是实际类别，y_表示预测结果，这实际上面是把原来的神经网络输出层的softmax和cross_entrop何在一起计算，为了追求速度
-accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits, 1), labels), tf.float32))
-'''
-
 import tensorflow as tf
 from tensorflow.keras import layers
 
@@ -54,6 +35,23 @@ def build_net_002(input_shape, n_classes):
     return model
 
 
+# this model is converge in terms of chinese characters classification
+# so simply is effective sometimes, adding a dense maybe model will be better?
+def build_net_003(input_shape, n_classes):
+    model = tf.keras.Sequential([
+        layers.Conv2D(input_shape=input_shape, filters=32, kernel_size=(3, 3), strides=(1, 1),
+                      padding='same', activation='relu'),
+        layers.MaxPool2D(pool_size=(2, 2), padding='same'),
+        layers.Conv2D(filters=64, kernel_size=(3, 3), padding='same'),
+        layers.MaxPool2D(pool_size=(2, 2), padding='same'),
+
+        layers.Flatten(),
+        # layers.Dense(1024, activation='relu'),
+        layers.Dense(n_classes, activation='softmax')
+    ])
+    return model
+
+
 # some models wrapped into tf.keras.Model
 class CNNNet(tf.keras.Model):