def get_model(width, height, classes=40):# TODO, modify modelnetwork = input_data(shape=[None, width, height, 3])  # if RGB, 224,224,3# Residual blocks  # 32 layers: n=5, 56 layers: n=9, 110 layers: n=18  n = 2net = tflearn.conv_2d(network, 16, 3, regularizer='L2', weight_decay=0.0001)net = tflearn.residual_block(net, n, 16)net = tflearn.residual_block(net, 1, 32, downsample=True)net = tflearn.residual_block(net, n-1, 32)net = tflearn.residual_block(net, 1, 64, downsample=True)net = tflearn.residual_block(net, n-1, 64)net = tflearn.batch_normalization(net)net = tflearn.activation(net, 'relu')net = tflearn.global_avg_pool(net)# Regression  net = tflearn.fully_connected(net, classes, activation='softmax')#mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)mom = tflearn.Momentum(0.01, lr_decay=0.1, decay_step=2000, staircase=True)net = tflearn.regression(net, optimizer=mom,loss='categorical_crossentropy')# Training  model = tflearn.DNN(net, checkpoint_path='model_resnet_cifar10',max_checkpoints=10, tensorboard_verbose=0,clip_gradients=0.)return modeldef  main():trainX, trainY = image_preloader("data/train", image_shape=(width, height, 3), mode='folder', categorical_labels=True, normalize=True)testX, testY = image_preloader("data/test", image_shape=(width, height, 3), mode='folder', categorical_labels=True, normalize=True)#trainX = trainX.reshape([-1, width, height, 1])#testX = testX.reshape([-1, width, height, 1])print("sample data:")print(trainX[0])print(trainY[0])print(testX[-1])print(testY[-1])model = get_model(width, height, classes=3755)filename = 'tflearn_resnet/model.tflearn'# try to load model and resume trainingtry:#model.load(filename)model.load("model_resnet_cifar10-195804")print("Model loaded OK. Resume training!")except:passearly_stopping_cb = EarlyStoppingCallback(val_acc_thresh=0.94)try:      model.fit(trainX, trainY, validation_set=(testX, testY), n_epoch=500, shuffle=True,snapshot_epoch=True, # Snapshot (save & evaluate) model every epoch.show_metric=True, batch_size=1024, callbacks=early_stopping_cb, run_id='cnn_handwrite')except StopIteration as e:print("OK, stop iterate!Good!")model.save(filename)del tf.get_collection_ref(tf.GraphKeys.TRAIN_OPS)[:]filename = 'tflearn_resnet/model-infer.tflearn'model.save(filename)