densenet 中文汉字手写识别，代码如下：

import tensorflow as tf

import os

import random

import math

import tensorflow.contrib.slim as slim

import time

import logging

import numpy as np

import pickle

from PIL import Image

import tensorflow as tf

#from tflearn.layers.conv import global_avg_pool

from tensorflow.contrib.layers import batch_norm, flatten

from tensorflow.contrib.framework import arg_scope

import numpy as np

# Hyperparameter

growth_k = 12

nb_block = 2 # how many (dense block + Transition Layer) ?

init_learning_rate = 1e-4

epsilon = 1e-8 # AdamOptimizer epsilon

dropout_rate = 0.2

# Momentum Optimizer will use

nesterov_momentum = 0.9

weight_decay = 1e-4

# Label & batch_size

class_num = 3755

batch_size = 128

total_epochs = 50

def conv_layer(input, filter, kernel, stride=1, layer_name="conv"):

    with tf.name_scope(layer_name):

        network = tf.layers.conv2d(inputs=input, filters=filter, kernel_size=kernel, strides=stride, padding='SAME')

        return network

def Global_Average_Pooling(x, stride=1):

    #It is global average pooling without tflearn

    width = np.shape(x)[1]

    height = np.shape(x)[2]

    pool_size = [width, height]

    return tf.layers.average_pooling2d(inputs=x, pool_size=pool_size, strides=stride) # The stride value does not matter

    """

    return global_avg_pool(x, name='Global_avg_pooling')

    # But maybe you need to install h5py and curses or not

    """

def Batch_Normalization(x, training, scope):

    with arg_scope([batch_norm],

                   scope=scope,

                   updates_collections=None,

                   decay=0.9,

                   center=True,

                   scale=True,

                   zero_debias_moving_mean=True) :

        return tf.cond(training,

                       lambda : batch_norm(inputs=x, is_training=training, reuse=None),

                       lambda : batch_norm(inputs=x, is_training=training, reuse=True))

def Drop_out(x, rate, training) :

    return tf.layers.dropout(inputs=x, rate=rate, training=training)

def Relu(x):

    return tf.nn.relu(x)

def Average_pooling(x, pool_size=[2,2], stride=2, padding='VALID'):

    return tf.layers.average_pooling2d(inputs=x, pool_size=pool_size, strides=stride, padding=padding)

def Max_Pooling(x, pool_size=[3,3], stride=2, padding='VALID'):

    return tf.layers.max_pooling2d(inputs=x, pool_size=pool_size, strides=stride, padding=padding)

def Concatenation(layers) :

    return tf.concat(layers, axis=3)

def Linear(x) :

    return tf.layers.dense(inputs=x, units=class_num, name='linear')

class DenseNet():

    def __init__(self, x, nb_blocks, filters, training):

        self.nb_blocks = nb_blocks

        self.filters = filters

        self.training = training

        self.model = self.Dense_net(x)

    def bottleneck_layer(self, x, scope):

        # print(x)

        with tf.name_scope(scope):

            x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')

            x = Relu(x)

            x = conv_layer(x, filter=4 * self.filters, kernel=[1,1], layer_name=scope+'_conv1')

            x = Drop_out(x, rate=dropout_rate, training=self.training)

            x = Batch_Normalization(x, training=self.training, scope=scope+'_batch2')

            x = Relu(x)

            x = conv_layer(x, filter=self.filters, kernel=[3,3], layer_name=scope+'_conv2')

            x = Drop_out(x, rate=dropout_rate, training=self.training)

            # print(x)

            return x

    def transition_layer(self, x, scope):

        with tf.name_scope(scope):

            x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')

            x = Relu(x)

            x = conv_layer(x, filter=self.filters, kernel=[1,1], layer_name=scope+'_conv1')

            x = Drop_out(x, rate=dropout_rate, training=self.training)

            x = Average_pooling(x, pool_size=[2,2], stride=2)

            return x

    def dense_block(self, input_x, nb_layers, layer_name):

        with tf.name_scope(layer_name):

            layers_concat = list()

            layers_concat.append(input_x)

            x = self.bottleneck_layer(input_x, scope=layer_name + '_bottleN_' + str(0))

            layers_concat.append(x)

            for i in range(nb_layers - 1):

                x = Concatenation(layers_concat)

                x = self.bottleneck_layer(x, scope=layer_name + '_bottleN_' + str(i + 1))

                layers_concat.append(x)

            x = Concatenation(layers_concat)

            return x

    def Dense_net(self, input_x):

        x = conv_layer(input_x, filter=2 * self.filters, kernel=[7,7], stride=2, layer_name='conv0')

        x = Max_Pooling(x, pool_size=[3,3], stride=2)

        for i in range(self.nb_blocks) :

            # 6 -> 12 -> 48

            x = self.dense_block(input_x=x, nb_layers=4, layer_name='dense_'+str(i))

            x = self.transition_layer(x, scope='trans_'+str(i))

        """

        x = self.dense_block(input_x=x, nb_layers=6, layer_name='dense_1')

        x = self.transition_layer(x, scope='trans_1')

        x = self.dense_block(input_x=x, nb_layers=12, layer_name='dense_2')

        x = self.transition_layer(x, scope='trans_2')

        x = self.dense_block(input_x=x, nb_layers=48, layer_name='dense_3')

        x = self.transition_layer(x, scope='trans_3')

        """

        x = self.dense_block(input_x=x, nb_layers=32, layer_name='dense_final')

        # 100 Layer

        x = Batch_Normalization(x, training=self.training, scope='linear_batch')

        x = Relu(x)

        x = Global_Average_Pooling(x)

        x = flatten(x)

        x = Linear(x)

        # x = tf.reshape(x, [-1, 10])

        return x

def build_graph(top_k):

    # with tf.device('/cpu:0'):

    # keep_prob = tf.placeholder(dtype=tf.float32, shape=[], name='keep_prob')

    images = tf.placeholder(dtype=tf.float32, shape=[None, 64, 64, 1], name='image_batch')

    # label = tf.placeholder(tf.float32, shape=[None, 10])

    labels = tf.placeholder(dtype=tf.int64, shape=[None], name='label_batch')

    training_flag = tf.placeholder(tf.bool)

    logits = DenseNet(x=images, nb_blocks=nb_block, filters=growth_k, training=training_flag).model

    loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels))

    # loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits))

    """

    l2_loss = tf.add_n([tf.nn.l2_loss(var) for var in tf.trainable_variables()])

    optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=nesterov_momentum, use_nesterov=True)

    train = optimizer.minimize(cost + l2_loss * weight_decay)

    In paper, use MomentumOptimizer

    init_learning_rate = 0.1

    but, I'll use AdamOptimizer

    """

    global_step = tf.get_variable("step", [], initializer=tf.constant_initializer(0.0), trainable=False)

    rate = tf.train.exponential_decay(2e-4, global_step, decay_steps=2000, decay_rate=0.97, staircase=True)

    optimizer = tf.train.AdamOptimizer(learning_rate=rate, epsilon=epsilon)

    train_op = optimizer.minimize(loss, global_step=global_step)

    accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits, 1), labels), tf.float32))

    probabilities = logits

    tf.summary.scalar('loss', loss)

    tf.summary.scalar('accuracy', accuracy)

    merged_summary_op = tf.summary.merge_all()

    predicted_val_top_k, predicted_index_top_k = tf.nn.top_k(probabilities, k=top_k)

    accuracy_in_top_k = tf.reduce_mean(tf.cast(tf.nn.in_top_k(probabilities, labels, top_k), tf.float32))

    return {'images': images,

            'labels': labels,

            'training_flag': training_flag,

            'top_k': top_k,

            'global_step': global_step,

            'train_op': train_op,

            'loss': loss,

            'accuracy': accuracy,

            'accuracy_top_k': accuracy_in_top_k,

            'merged_summary_op': merged_summary_op,

            'predicted_distribution': probabilities,

            'predicted_index_top_k': predicted_index_top_k,

            'predicted_val_top_k': predicted_val_top_k} 

logger = logging.getLogger('Training a chinese write char recognition')

logger.setLevel(logging.INFO)

# formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')

ch = logging.StreamHandler()

ch.setLevel(logging.INFO)

logger.addHandler(ch)

run_mode = "train"

charset_size = class_num

max_steps = 122002

save_steps = 1000

cur_test_acc = 0

"""

# for online 3755 words training

checkpoint_dir = '/aiml/dfs/checkpoint_888/'

train_data_dir = '/aiml/data/train/'

test_data_dir = '/aiml/data/test/'

log_dir = '/aiml/dfs/'

"""

checkpoint_dir = './checkpoint_densenet/'

train_data_dir = './data/train/'

test_data_dir = './data/test/'

log_dir = './'

tf.app.flags.DEFINE_string('mode', run_mode, 'Running mode. One of {"train", "valid", "test"}')

tf.app.flags.DEFINE_boolean('random_flip_up_down', True, "Whether to random flip up down")

tf.app.flags.DEFINE_boolean('random_brightness', True, "whether to adjust brightness")

tf.app.flags.DEFINE_boolean('random_contrast', True, "whether to random constrast")

tf.app.flags.DEFINE_integer('charset_size', charset_size, "Choose the first `charset_size` character to conduct our experiment.")

tf.app.flags.DEFINE_integer('image_size', 64, "Needs to provide same value as in training.")

tf.app.flags.DEFINE_boolean('gray', True, "whether to change the rbg to gray")

tf.app.flags.DEFINE_integer('max_steps', max_steps, 'the max training steps ')

tf.app.flags.DEFINE_integer('eval_steps', 50, "the step num to eval")

tf.app.flags.DEFINE_integer('save_steps', save_steps, "the steps to save")

tf.app.flags.DEFINE_string('checkpoint_dir', checkpoint_dir, 'the checkpoint dir')

tf.app.flags.DEFINE_string('train_data_dir', train_data_dir, 'the train dataset dir')

tf.app.flags.DEFINE_string('test_data_dir', test_data_dir, 'the test dataset dir')

tf.app.flags.DEFINE_string('log_dir', log_dir, 'the logging dir')

##############################

# resume training

tf.app.flags.DEFINE_boolean('restore', True, 'whether to restore from checkpoint')

##############################

tf.app.flags.DEFINE_boolean('epoch', 10, 'Number of epoches')

tf.app.flags.DEFINE_boolean('batch_size', 128, 'Validation batch size')

FLAGS = tf.app.flags.FLAGS

class DataIterator:

    def __init__(self, data_dir):

        # Set FLAGS.charset_size to a small value if available computation power is limited.

        truncate_path = data_dir + ('%05d' % FLAGS.charset_size)

        print(truncate_path)

        self.image_names = []

        for root, sub_folder, file_list in os.walk(data_dir):

            if root < truncate_path:

                self.image_names += [os.path.join(root, file_path) for file_path in file_list]

        random.shuffle(self.image_names)

        self.labels = [int(file_name[len(data_dir):].split(os.sep)[0]) for file_name in self.image_names]

    @property

    def size(self):

        return len(self.labels)

    @staticmethod

    def data_augmentation(images):

        if FLAGS.random_flip_up_down:

            # images = tf.image.random_flip_up_down(images)

            images = tf.contrib.image.rotate(images, random.randint(0, 15) * math.pi / 180, interpolation='BILINEAR')

        if FLAGS.random_brightness:

            images = tf.image.random_brightness(images, max_delta=0.3)

        if FLAGS.random_contrast:

            images = tf.image.random_contrast(images, 0.8, 1.2)

        return images

    def input_pipeline(self, batch_size, num_epochs=None, aug=False):

        images_tensor = tf.convert_to_tensor(self.image_names, dtype=tf.string)

        labels_tensor = tf.convert_to_tensor(self.labels, dtype=tf.int64)

        input_queue = tf.train.slice_input_producer([images_tensor, labels_tensor], num_epochs=num_epochs)

        labels = input_queue[1]

        images_content = tf.read_file(input_queue[0])

        images = tf.image.convert_image_dtype(tf.image.decode_png(images_content, channels=1), tf.float32)

        if aug:

            images = self.data_augmentation(images)

        new_size = tf.constant([FLAGS.image_size, FLAGS.image_size], dtype=tf.int32)

        images = tf.image.resize_images(images, new_size)

        image_batch, label_batch = tf.train.shuffle_batch([images, labels], batch_size=batch_size, capacity=50000,

                                                          min_after_dequeue=10000)

        return image_batch, label_batch

def train():

    print('Begin training')

    train_feeder = DataIterator(FLAGS.train_data_dir)

    test_feeder = DataIterator(FLAGS.test_data_dir)

    with tf.Session() as sess:

        train_images, train_labels = train_feeder.input_pipeline(batch_size=FLAGS.batch_size, aug=True)

        test_images, test_labels = test_feeder.input_pipeline(batch_size=FLAGS.batch_size)

        graph = build_graph(top_k=1)

        sess.run(tf.global_variables_initializer())

        coord = tf.train.Coordinator()

        threads = tf.train.start_queue_runners(sess=sess, coord=coord)

        saver = tf.train.Saver()

        train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)

        test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/val')

        start_step = 0

        if FLAGS.restore:

            ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)

            if ckpt:

                saver.restore(sess, ckpt)

                print("restore from the checkpoint {0}".format(ckpt))

                start_step += int(ckpt.split('-')[-1])

        logger.info(':::Training Start:::')

        try:

            while not coord.should_stop():

                start_time = time.time()

                train_images_batch, train_labels_batch = sess.run([train_images, train_labels])

                feed_dict = {graph['images']: train_images_batch,

                             graph['labels']: train_labels_batch,

                             graph['training_flag']: True}

                _, loss_val, train_summary, step = sess.run(

                    [graph['train_op'], graph['loss'], graph['merged_summary_op'], graph['global_step']],

                    feed_dict=feed_dict)

                train_writer.add_summary(train_summary, step)

                end_time = time.time()

                logger.info("the step {0} takes {1} loss {2}".format(step, end_time - start_time, loss_val))

                if step > FLAGS.max_steps:

                    break

                accuracy_test = 0

                if step % FLAGS.eval_steps == 1:

                    test_images_batch, test_labels_batch = sess.run([test_images, test_labels])

                    feed_dict = {graph['images']: test_images_batch,

                                 graph['labels']: test_labels_batch,

                                 graph['training_flag']: False}

                    accuracy_test, test_summary = sess.run(

                        [graph['accuracy'], graph['merged_summary_op']],

                        feed_dict=feed_dict)

                    test_writer.add_summary(test_summary, step)

                    logger.info('===============Eval a batch=======================')

                    logger.info('the step {0} test accuracy: {1}'

                                .format(step, accuracy_test))

                    logger.info('===============Eval a batch=======================')

                if step % FLAGS.save_steps == 1:

                    logger.info('Save the ckpt of {0}'.format(step))

                    saver.save(sess, os.path.join(FLAGS.checkpoint_dir, 'my-model'),

                               global_step=graph['global_step'])

                global cur_test_acc

                cur_test_acc = accuracy_test

        except tf.errors.OutOfRangeError:

            logger.info('==================Train Finished================')

            saver.save(sess, os.path.join(FLAGS.checkpoint_dir, 'my-model'), global_step=graph['global_step'])

        finally:

            coord.request_stop()

        coord.join(threads)

def validation():

    print('validation')

    test_feeder = DataIterator(FLAGS.test_data_dir)

    final_predict_val = []

    final_predict_index = []

    groundtruth = []

    with tf.Session() as sess:

        test_images, test_labels = test_feeder.input_pipeline(batch_size=FLAGS.batch_size, num_epochs=1)

        graph = build_graph(top_k=3)

        sess.run(tf.global_variables_initializer())

        sess.run(tf.local_variables_initializer())  # initialize test_feeder's inside state

        coord = tf.train.Coordinator()

        threads = tf.train.start_queue_runners(sess=sess, coord=coord)

        saver = tf.train.Saver()

        ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)

        if ckpt:

            saver.restore(sess, ckpt)

            print("restore from the checkpoint {0}".format(ckpt))

        print(':::Start validation:::')

        try:

            i = 0

            acc_top_1, acc_top_k = 0.0, 0.0

            while not coord.should_stop():

                i += 1

                start_time = time.time()

                test_images_batch, test_labels_batch = sess.run([test_images, test_labels])

                feed_dict = {graph['images']: test_images_batch,

                             graph['labels']: test_labels_batch,

                             graph['training_flag']: False}

                batch_labels, probs, indices, acc_1, acc_k = sess.run([graph['labels'],

                                                                       graph['predicted_val_top_k'],

                                                                       graph['predicted_index_top_k'],

                                                                       graph['accuracy'],

                                                                       graph['accuracy_top_k']], feed_dict=feed_dict)

                final_predict_val += probs.tolist()

                final_predict_index += indices.tolist()

                groundtruth += batch_labels.tolist()

                acc_top_1 += acc_1

                acc_top_k += acc_k

                end_time = time.time()

                logger.info("the batch {0} takes {1} seconds, accuracy = {2}(top_1) {3}(top_k)"

                            .format(i, end_time - start_time, acc_1, acc_k))

        except tf.errors.OutOfRangeError:

            logger.info('==================Validation Finished================')

            acc_top_1 = acc_top_1 * FLAGS.batch_size / test_feeder.size

            acc_top_k = acc_top_k * FLAGS.batch_size / test_feeder.size

            logger.info('top 1 accuracy {0} top k accuracy {1}'.format(acc_top_1, acc_top_k))

        finally:

            coord.request_stop()

        coord.join(threads)

    return {'prob': final_predict_val, 'indices': final_predict_index, 'groundtruth': groundtruth}

def inference(image):

    print('inference')

    temp_image = Image.open(image).convert('L')

    temp_image = temp_image.resize((FLAGS.image_size, FLAGS.image_size), Image.ANTIALIAS)

    temp_image = np.asarray(temp_image) / 255.0

    temp_image = temp_image.reshape([-1, 64, 64, 1])

    with tf.Session() as sess:

        logger.info('========start inference============')

        # images = tf.placeholder(dtype=tf.float32, shape=[None, 64, 64, 1])

        # Pass a shadow label 0. This label will not affect the computation graph.

        graph = build_graph(top_k=3)

        saver = tf.train.Saver()

        ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)

        if ckpt:

            saver.restore(sess, ckpt)

        predict_val, predict_index = sess.run([graph['predicted_val_top_k'], graph['predicted_index_top_k']],

                                              feed_dict={graph['images']: temp_image, graph['training_flag']: False})

    return predict_val, predict_index

def main(_):

    print(FLAGS.mode)

    if FLAGS.mode == "train":

        train()

    elif FLAGS.mode == 'validation':

        dct = validation()

        result_file = 'result.dict'

        logger.info('Write result into {0}'.format(result_file))

        with open(result_file, 'wb') as f:

            pickle.dump(dct, f)

        logger.info('Write file ends')

    elif FLAGS.mode == 'inference':

        image_path = './data/00098/102544.png'

        final_predict_val, final_predict_index = inference(image_path)

        logger.info('the result info label {0} predict index {1} predict_val {2}'.format(190, final_predict_index,

                                                                                         final_predict_val))

if __name__ == "__main__":

    tf.app.run()

densenet模型参考：https://github.com/taki0112/Densenet-Tensorflow

效果：

===============Eval a batch=======================
the step 34001.0 test accuracy: 0.765625
===============Eval a batch=======================

Compare Structure (CNN, ResNet, DenseNet)

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