to submit cnn related stuff

cnn_for_license_plate_recognition/area_model/checkpoint

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+model_checkpoint_path: "area"
+all_model_checkpoint_paths: "area"

cnn_for_license_plate_recognition/cnn_model.py

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+import tensorflow as tf
+from process_data import DataProcessor
+
+batch_size = 128
+learning_rate = 1e-4
+aspect = 'province'
+data_processor = DataProcessor(aspect)
+dir = './'
+model_name = aspect
+
+input_x = tf.placeholder(dtype=tf.float32, shape=[None, 20, 20], name='input_x')
+input_y = tf.placeholder(dtype=tf.float32, shape=[None, 34], name='input_y')
+
+with tf.name_scope('conv1'):
+    W_C1 = tf.Variable(tf.truncated_normal(shape=[3, 3, 1, 32], stddev=0.1))
+    b_C1 = tf.Variable(tf.constant(0.1, tf.float32, shape=[32]))
+    X = tf.reshape(input_x, [-1, 20, 20, 1])
+    featureMap_C1 = tf.nn.relu(tf.nn.conv2d(X, W_C1, strides=[1, 1, 1, 1], padding='SAME') + b_C1)
+
+with tf.name_scope('conv2'):
+    W_C2 = tf.Variable(tf.truncated_normal(shape=[3, 3, 32, 64], stddev=0.1))
+    b_C2 = tf.Variable(tf.constant(0.1, tf.float32, shape=[64]))
+    featureMap_C2 = tf.nn.relu(tf.nn.conv2d(featureMap_C1, W_C2, strides=[1, 1, 1, 1], padding='SAME') + b_C2)
+
+with tf.name_scope('pooling2'):
+    featureMap_S2 = tf.nn.max_pool(featureMap_C2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
+
+with tf.name_scope('conv3'):
+    W_C3 = tf.Variable(tf.truncated_normal(shape=[3, 3, 64, 8], stddev=0.1))
+    b_C3 = tf.Variable(tf.constant(0.1, shape=[8], dtype=tf.float32))
+    featureMap_C3 = tf.nn.relu(tf.nn.conv2d(featureMap_S2, filter=W_C3, strides=[1, 1, 1, 1], padding='SAME') + b_C3)
+
+with tf.name_scope('pooling3'):
+    featureMap_S3 = tf.nn.max_pool(featureMap_C3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
+
+with tf.name_scope('fulnet'):
+    featureMap_flatten = tf.reshape(featureMap_S3, [-1, 5*5*8])
+    W_F4 = tf.Variable(tf.truncated_normal(shape=[5*5*8, 512], stddev=0.1))
+    b_F4 = tf.Variable(tf.constant(0.1, shape=[512], dtype=tf.float32))
+    out_F4 = tf.nn.relu(tf.matmul(featureMap_flatten, W_F4) + b_F4)
+    out_F4 = tf.nn.dropout(out_F4, keep_prob=0.5)
+
+with tf.name_scope('output'):
+    W_OUTPUT = tf.Variable(tf.truncated_normal(shape=[512, 34], stddev=0.1))
+    b_OUTPUT = tf.Variable(tf.constant(0.1, shape=[34], dtype=tf.float32))
+    logits = tf.matmul(out_F4, W_OUTPUT)+b_OUTPUT
+
+loss = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(labels=input_y, logits=logits))
+train_op = tf.train.AdamOptimizer(learning_rate).minimize(loss)
+predictY = tf.nn.softmax(logits)
+y_pred = tf.arg_max(predictY, 1)
+bool_pred = tf.equal(tf.arg_max(input_y, 1), y_pred)
+right_rate = tf.reduce_mean(tf.to_float(bool_pred))
+
+saver = tf.train.Saver()
+
+
+def load_model(sess, dir, model_name):
+    ckpt = tf.train.get_checkpoint_state(dir)
+    if ckpt and ckpt.model_checkpoint_path:
+        print('*'*30)
+        print('load latest model......')
+        saver.restore(sess, dir+'.\\'+model_name)
+        print('*'*30)
+
+
+def save_model(sess, dir, model_name):
+    saver.save(sess, dir+model_name)
+
+
+with tf.Session() as sess:
+    sess.run(tf.initialize_all_variables())
+    step = 1
+    display_interval = 200
+    max_epoch = 500
+    epoch = 1
+    acc = 0
+    load_model(sess, dir=dir, model_name=model_name)
+    while True:
+        if step % display_interval == 0:
+            image_batch, label_batch, epoch = data_processor.next_valid_batch(batch_size)
+            acc = sess.run(right_rate, feed_dict={input_x: image_batch, input_y: label_batch})
+            print({str(epoch)+':'+str(step): acc})
+        image_batch, label_batch, epoch = data_processor.next_train_batch(batch_size)
+        sess.run([loss, train_op], {input_x: image_batch, input_y: label_batch})
+        if epoch > max_epoch:
+            break
+        step += 1
+    while True:
+        test_img, test_lab, test_epoch = data_processor.next_test_batch(batch_size)
+        test_acc = sess.run(right_rate, {input_x: test_img, input_y: test_lab})
+        acc = test_acc * 0.8 + acc * 0.2
+        if test_epoch != epoch:
+            print({'Test Over..... acc:': acc})
+            break
+    save_model(sess, dir, model_name)

cnn_for_license_plate_recognition/dataset/map.py

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+maps={
+  "province":
+  {
+    "0":"皖",
+    "1":"沪",
+    "2":"津",
+	"3":"渝",
+	"4":"冀",
+	"5":"晋",
+	"6":"蒙",
+	"7":"辽",
+	"8":"吉",
+	"9":"黑",
+	"10":"苏",
+	"11":"浙",
+	"12":"京",
+	"13":"闽",
+	"14":"赣",
+	"15":"鲁",
+	"16":"豫",
+	"17":"鄂",
+	"18":"湘",
+	"19":"粤",
+	"20":"桂",
+	"21":"琼",
+	"22":"川",
+	"23":"贵",
+	"24":"云",
+	"25":"藏",
+	"26":"陕",
+	"27":"甘",
+	"28":"青",
+	"29":"宁",
+	"30":"新"
+  },
+  "area":
+  {
+	"0":"A",
+    "1":"B",
+    "2":"C",
+	"3":"D",
+	"4":"E",
+	"5":"F",
+	"6":"G",
+	"7":"H",
+	"8":"I",
+	"9":"J",
+	"10":"K",
+	"11":"L",
+	"12":"M",
+	"13":"N",
+	"14":"O",
+	"15":"P",
+	"16":"Q",
+	"17":"R",
+	"18":"S",
+	"19":"T",
+	"20":"U",
+	"21":"V",
+	"22":"W",
+	"23":"X",
+	"24":"Y",
+	"25":"Z"
+  },
+  "letter":
+  {
+    "0":"0",
+    "1":"1",
+    "2":"2",
+	"3":"3",
+	"4":"4",
+	"5":"5",
+	"6":"6",
+	"7":"7",
+	"8":"8",
+	"9":"9",
+	"10":"A",
+	"11":"B",
+	"12":"C",
+	"13":"D",
+	"14":"E",
+	"15":"F",
+	"16":"G",
+	"17":"H",
+	"18":"J",
+	"19":"K",
+	"20":"L",
+	"21":"M",
+	"22":"N",
+	"23":"P",
+	"24":"Q",
+	"25":"R",
+	"26":"S",
+	"27":"T",
+	"28":"U",
+	"29":"V",
+	"30":"W",
+	"31":"X",
+	"32":"Y",
+	"33":"Z"
+  }      
+}