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+from __future__ import division, print_function, absolute_import%0A%0Aimport tflearn%0A%0A# Residual blocks%0A# 32 layers: n=5, 56 layers: n=9, 110 layers: n=18%0An = 5%0A%0A# Data loading%0Afrom tflearn.datasets import cifar10%0A(X, Y), (testX, testY) = cifar10.load_data()%0AY = tflearn.data_utils.to_categorical(Y, 10)%0AtestY = tflearn.data_utils.to_categorical(testY, 10)%0A%0A# Real-time data preprocessing%0Aimg_prep = tflearn.ImagePreprocessing()%0Aimg_prep.add_featurewise_zero_center(per_channel=True)%0A%0A# Real-time data augmentation%0Aimg_aug = tflearn.ImageAugmentation()%0Aimg_aug.add_random_flip_leftright()%0Aimg_aug.add_random_crop(%5B32, 32%5D, padding=4)%0A%0A# Building Residual Network%0Anet = tflearn.input_data(shape=%5BNone, 32, 32, 3%5D,%0A                         data_preprocessing=img_prep,%0A                         data_augmentation=img_aug)%0Anet = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)%0Anet = tflearn.residual_block(net, n, 16)%0Anet = tflearn.residual_block(net, 1, 32, downsample=True)%0Anet = tflearn.residual_block(net, n-1, 32)%0Anet = tflearn.residual_block(net, 1, 64, downsample=True)%0Anet = tflearn.residual_block(net, n-1, 64)%0Anet = tflearn.batch_normalization(net)%0Anet = tflearn.activation(net, 'relu')%0Anet = tflearn.global_avg_pool(net)%0A# Regression%0Anet = tflearn.fully_connected(net, 10, activation='softmax')%0Amom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)%0Anet = tflearn.regression(net, optimizer=mom,%0A                         loss='categorical_crossentropy')%0A# Training%0Amodel = tflearn.DNN(net, checkpoint_path='model_resnet_cifar10',%0A                    max_checkpoints=10, tensorboard_verbose=0,%0A                    clip_gradients=0.)%0A%0Amodel.fit(X, Y, n_epoch=200, validation_set=(testX, testY),%0A          snapshot_epoch=False, snapshot_step=500,%0A          show_metric=True, batch_size=128, shuffle=True,%0A          run_id='resnet_cifar10')%0A

Create Resnet.py