theano-xnor-net代码注释 cifar10_train.py
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# *_*coding:utf-8 *_*import sys, os, timeimport lasagneimport numpy as npimport theanoimport theano.tensor as Timport cPickleimport xnor_netimport cnn_utilsfrom external import bnn_utilsimport gzipfrom collections import OrderedDictdef construct_cifar10_net(input_var, alpha, eps): ################################################## # # 结构: # # 输入层 # | # 卷积层1 + BN层 # | # 卷积层2 + 最大值混合层2 + BN层 # | # 卷积层3 + BN层 # | # 卷积层4 + 最大值混合层4 + BN层 # | # 卷积层5 + BN层 # | # 卷积层6 + 最大值混合层6 + BN层 # | # 全连接层1 + BN层 # | # 全连接层2 + BN层 # | # 全连接层3 # | # 输出 # ################################################## # input layer (输入层) # Input conv layer is not binary. As the paper states, the computational savings are very less # when the input channels to the conv layer are less # 第一层卷积：根据论文所述，由于加速与激活图层深度与卷积大小关系密切，输入层有RGB三层，所以不使用xnor-conv cnn = lasagne.layers.InputLayer(shape=(None, 3, 32, 32), input_var=input_var) # 卷积层1 cnn = xnor_net.Conv2DLayer( cnn, # 输入层 xnor=False, # 输入层是否是XNor层 (如果不是，会对输入层进行二值化) num_filters=128, # 滤波器个数 filter_size=(3, 3), # 滤波器大小 pad=1, # 边界填充方式 (lasagne.layers.Conv2DLayer的参数) nonlinearity=lasagne.nonlinearities.identity) # 激活函数 (lasagne.layers.Conv2DLayer的参数) # Batch Normalization层 cnn = lasagne.layers.BatchNormLayer( cnn, epsilon=eps, # todo: BN层参数 alpha=alpha) # 卷积层2 cnn = xnor_net.Conv2DLayer( cnn, xnor=True, num_filters=128, filter_size=(3, 3), pad=1, nonlinearity=lasagne.nonlinearities.identity) # 最大值混合层2 cnn = lasagne.layers.MaxPool2DLayer(cnn, pool_size=(2, 2)) # Batch Normalization层 cnn = lasagne.layers.BatchNormLayer( cnn, epsilon=eps, alpha=alpha) # 卷积层3 cnn = xnor_net.Conv2DLayer( cnn, xnor=True, num_filters=256, filter_size=(3, 3), pad=1, nonlinearity=lasagne.nonlinearities.identity) # Batch Normalization层 cnn = lasagne.layers.BatchNormLayer( cnn, epsilon=eps, alpha=alpha) # 卷积层4 cnn = xnor_net.Conv2DLayer( cnn, xnor=True, num_filters=256, filter_size=(3, 3), pad=1, nonlinearity=lasagne.nonlinearities.identity) # 最大值混合层4 cnn = lasagne.layers.MaxPool2DLayer(cnn, pool_size=(2, 2)) # Batch Normalization层 cnn = lasagne.layers.BatchNormLayer( cnn, epsilon=eps, alpha=alpha) # 卷积层5 cnn = xnor_net.Conv2DLayer( cnn, xnor=True, num_filters=512, filter_size=(3, 3), pad=1, nonlinearity=lasagne.nonlinearities.identity) # Batch Normalization层 cnn = lasagne.layers.BatchNormLayer( cnn, epsilon=eps, alpha=alpha) # 卷积层6 cnn = xnor_net.Conv2DLayer( cnn, xnor=True, num_filters=512, filter_size=(3, 3), pad=1, nonlinearity=lasagne.nonlinearities.identity) # 最大值混合层6 cnn = lasagne.layers.MaxPool2DLayer(cnn, pool_size=(2, 2)) # Batch Normalization层 cnn = lasagne.layers.BatchNormLayer( cnn, epsilon=eps, alpha=alpha) # 全连接层1 cnn = xnor_net.DenseLayer( cnn, xnor=True, nonlinearity=lasagne.nonlinearities.identity, num_units=1024) # Batch Normalization层 cnn = lasagne.layers.BatchNormLayer( cnn, epsilon=eps, alpha=alpha) # 全连接层2 cnn = xnor_net.DenseLayer( cnn, xnor=True, nonlinearity=lasagne.nonlinearities.identity, num_units=1024) # Batch Normalization层 cnn = lasagne.layers.BatchNormLayer( cnn, epsilon=eps, alpha=alpha) # 全连接层3 cnn = xnor_net.DenseLayer( cnn, xnor=False, nonlinearity=lasagne.nonlinearities.softmax, num_units=10) return cnnif __name__ == '__main__': # theano.function(inputs, outputs=None, mode=None, updates=None, givens=None): # 根据输入inputs计算数据outputs的函数，其中: # inputs: 列表类型，用来保存输入量 # outputs: 列表或字典类型，用来保存输出量。输入量与输出量的映射关系通常在输出量的定义中体现。 # updates: 一组可迭代更新的量(shared_variable, new_expression), # 对其中的shared_variable输入用new_expression表达式更新 # # lasagne.updates.adam(loss_or_grads, params, learning_rate): 用于参数更新， # 其中: loss_or_grads: 误差或梯度 # params: 要更新的参数 # learning_rate: 更新速率(学习速率) # # lasagne.layers.get_output(layer): 对指定网络，计算网络输出; # lasagne.objectives.categorical_crossentropy(predictions, targets): 计算分类结果与目标的交叉熵(误差); # lasagne.layers.get_all_params(layer): 返回一个列表，包含该层参数的theano共享变量或表达式 ? # bnn_utils.compute_grads(loss, network): 计算梯度; # bnn_utilsclipping_scaling(updates, network): 该函数在参数更新后规范化; # OrderedDict: 有序字典类; # dict.items(): 返回字典的键值; # tensor.nep(): 相当于"a != b"; # tensor.argmax(): 返回沿指定轴取得最大值的下标; # This is XNOR net (设定XNOR-神经网络标识) xnor = True # Model file name (设定模型文件名) model_file = 'xnor_net_cifar10_nonxnor_first_lyr.npz' # hyper parameters (设定超参数) batch_size = 50 # 小批量数据大小 alpha = 0.1 # (BN层参数) eps = 1e-4 # (BN层参数) no_epochs = 200 # 迭代期次数 # learning rate (设定学习速率) # similar setting # s as in BinaryNet LR_start = 0.001 # 学习速率-起点 LR_end = 0.0000003 # 学习速率-终点 LR_decay = (LR_end/LR_start)**(1./no_epochs) # 平台值后，LR的衰减倍数 print('LR_start = {:f}\tLR_end = {:f}\tLR_decay = {:f}'.format(LR_start, LR_end, LR_decay)) # input data, target and learning rate as theano symbolic var (建立输入数据，目标和学习速率变量) input_vars = T.tensor4('input') # 输入数据，四维tensor张量 targets = T.fmatrix('target') # 目标，tensor矩阵 LR = T.scalar('LR', dtype=theano.config.floatX) # 学习速率，tensor标量 # construct deep network (构建深度网络) print('Constructing the network...') net = construct_cifar10_net(input_vars, alpha, eps) # 建立网络，这里调用了construct_cifar10_net()函数 # Load data (加载数据) print('Loading the data...') train_x, val_x, test_x, train_y, val_y, test_y = cnn_utils.load_data('cifar10') # 加载数据 # network output (计算网络输出) # 注：用于建立训练数据的误差计算及参数更新函数 train_pred = lasagne.layers.get_output(net, deterministic=False) print('Constructed symbolic output') # loss (定义误差计算函数) # 注：用于建立训练数据的误差计算及参数更新函数 # As per paper it is -ve log-liklihood on softmax output loss = lasagne.objectives.categorical_crossentropy(train_pred, targets) # mean loss across all images in the batch # 计算小批量数据中所有图片误差的平均值 loss = T.mean(loss) print('Constructed symbolic training loss') # define the update process (定义参数更新函数) # 注：用于建立训练数据的误差计算及参数更新函数 # No need of weight cliping as in BinaryNet # 在二值神经网络中无需进行权重重置。 print('Defining the update process...') if xnor: # W updates (权重更新) W = lasagne.layers.get_all_params(net, xnor=True) # 取得网络权重 W_grads = bnn_utils.compute_grads(loss, net) # 计算梯度 updates = lasagne.updates.adam(loss_or_grads=W_grads, params=W, learning_rate=LR) #是的 updates = bnn_utils.clipping_scaling(updates, net) # other parameters updates (其它参数更新) params = lasagne.layers.get_all_params(net, trainable=True, xnor=False) updates = OrderedDict(updates.items() + lasagne.updates.adam(loss_or_grads=loss, params=params, learning_rate=LR).items()) # 将其它参数的更新添加到updates中 else: # (参数更新) params = lasagne.layers.get_all_params(net, trainable=True) updates = lasagne.updates.adam(loss_or_grads=loss, params=params, learning_rate=LR) # test prediction and loss expressions (测试数据和误差表示) # 注：用于建立测试数据的误差计算函数 print('Creating test prediction, loss and error expressions...') test_pred = lasagne.layers.get_output(net, deterministic=True) #多分类交叉熵损失函数，一般用作最后一层为sigmoid分类 test_loss = T.mean(lasagne.objectives.categorical_crossentropy(test_pred, targets)) test_err = T.mean(T.neq(T.argmax(test_pred, axis=1), T.argmax(targets, axis=1)), dtype=theano.config.floatX) # construct theano function train, validation/testing process (建立训练数据和测试数据的theano函数) train_fn = theano.function([input_vars, targets, LR], loss, updates=updates) # 建立训练数据的误差计算及参数更新函数 # test_fn = theano.function([input_vars, targets], test_loss) test_fn = theano.function([input_vars, targets], [test_loss, test_err]) # 建立测试数据的误差计算函数 print('Created theano functions for training and validation...') # 训练数据 print('Training...') # new_loss = train_fn(train_x[0:50], train_y[0:50], LR_start) print('Trainset shape = ', train_x.shape, train_y.shape) print('Valset shape = ', val_x.shape, val_y.shape) print('Testset shape = ', test_x.shape, test_y.shape) # new_loss, new_err = test_fn(val_x[0:50], val_y[0:50]) bnn_utils.train( train_fn, test_fn, # 训练和测试数据的theano函数 net, # 神经网络 batch_size, # 小批量数据大小 LR_start, LR_decay, # 学习速率 no_epochs, # 迭代期次数 train_x, train_y, # 训练数据 val_x, val_y, # 验证数据 test_x, test_y, # 测试数据 save_path=model_file, # 模型存放路径 shuffle_parts=1)# This should produce at most 13.89% test error rate (这应该产生至多13.89%的测试错误率。)阅读全文
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- theano-xnor-net代码注释 cifar10_train.py
- theano-xnor-net代码注释2 cnn_utils.py
- theano-xnor-net代码注释3 xnor_net.py
- theano-xnor-net代码注释4 bnn_utils.py
- theano-xnor-net代码注释5 cifar10_test.py
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