theano中的concolutional_mlp.py学习

缺乏、安全感 2021-07-16 19:16 443阅读 0赞

(1) evaluate \_lenet5中的导入数据部分

1     # 导入数据集，该函数定义在logistic_sgd中，返回的是一个list
     2     datasets = load_data(dataset)
     3 
     4     # 从list中提取三个元素，每个元素都是一个tuple（每个tuple含有2个元素，分别为images数据和label数据）
     5     train_set_x, train_set_y = datasets[0] #训练集
     6     valid_set_x, valid_set_y = datasets[1] #校验集
     7     test_set_x, test_set_y = datasets[2]   #测试集
     8 
     9 
    10     # 训练集、校验集、测试集分别含有的样本个数
    11     n_train_batches = train_set_x.get_value(borrow=True).shape[0]
    12     n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
    13     n_test_batches = test_set_x.get_value(borrow=True).shape[0]
    14     # 训练集、校验集、测试集中包含的minibatch个数（每个iter，只给一个minibatch，而不是整个数据集）
    15     n_train_batches /= batch_size
    16     n_valid_batches /= batch_size
    17     n_test_batches /= batch_size

![736761-20160206031806585-982182128.jpg][]

（2）evaluate \_lenet5中的building model部分

1     # 首先，定义一些building model用到的符号变量
      2     index = T.lscalar()  # 用于指定具体哪个minibatch的指标
      3 
      4     # start-snippet-1
      5     x = T.matrix('x')   # 存储图像的像素数据
      6     y = T.ivector('y')  # 存储每幅图像对应的label
      7 
      8     # 开始build model
      9     print '... building the model'
     10 
     11     # 将输入的数据(batch_size, 28 * 28)reshape为4D tensor（batch_size是每个mini-batch包含的image个数）
     12     layer0_input = x.reshape((batch_size, 1, 28, 28))
     13 
     14     # 构造第一个卷积层
     15     # （1）卷积核大小为5*5、个数为nkerns[0]、striding =1，padding=0
     16     #     输出的feature map大小为：(28-5+1 , 28-5+1) = (24, 24)
     17     # （2）含有max-pooling，pooling的大小为2*2、striding =1，padding=0
     18     #     输出的map大小为：(24/2, 24/2) = (12, 12)
     19     # （3）综上，第一个卷积层输出的feature map为一个4D tensor，形状为：(batch_size, nkerns[0], 12, 12)
     20     layer0 = LeNetConvPoolLayer(
     21         rng,
     22         input=layer0_input,
     23         image_shape=(batch_size, 1, 28, 28),
     24         filter_shape=(nkerns[0], 1, 5, 5),
     25         poolsize=(2, 2)
     26     )
     27 
     28     # 构造第二个卷积层，卷积核大小为5*5
     29     # （1）卷积核大小为5*5、个数为nkerns[0]、striding =1，padding=0
     30     #     输出的feature map大小为：(12-5+1, 12-5+1) = (8, 8)
     31     # （2）含有max-pooling，pooling的大小为2*2、striding =1，padding=0
     32     #     输出的map大小为：(8/2, 8/2) = (4, 4)
     33     # （3）综上，第二个卷积层输出的feature map为一个4D tensor，形状为：(batch_size, nkerns[1], 4, 4)
     34     layer1 = LeNetConvPoolLayer(
     35         rng,
     36         input=layer0.output,
     37         image_shape=(batch_size, nkerns[0], 12, 12),
     38         filter_shape=(nkerns[1], nkerns[0], 5, 5),
     39         poolsize=(2, 2)
     40     )
     41 
     42     # 将第二个卷积层的输出map（形状为(batch_size, nkerns[1], 4, 4)）转化为一个matrix的形式
     43     # 该矩阵的形状为：(batch_size, nkerns[1] * 4 * 4)，每一行为一个图形对应的feature map
     44     layer2_input = layer1.output.flatten(2)
     45 
     46     # 第一个全链接层
     47     # （1）输入的大小固定，即第二个卷积层的输出
     48     # （2）输出大小自己选的，这里选定为500
     49     # （3）sigmoid函数为tan函数
     50     layer2 = HiddenLayer(
     51         rng,
     52         input=layer2_input,
     53         n_in=nkerns[1] * 4 * 4,
     54         n_out=500,
     55         activation=T.tanh
     56     )
     57 
     58     # 输出层，即逻辑回归层
     59     layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
     60 
     61     # 代价函数的计算
     62     cost = layer3.negative_log_likelihood(y)
     63 
     64     # 测试model，输入为具体要测试的test集中的某个mini-batch
     65     # 输出为训练得到的model在该mini-batch上的error
     66     test_model = theano.function(
     67         [index],
     68         layer3.errors(y),
     69         givens={
     70             x: test_set_x[index * batch_size: (index + 1) * batch_size],
     71             y: test_set_y[index * batch_size: (index + 1) * batch_size]
     72         }
     73     )
     74 
     75     # 校验model，输入为具体要测试的校验集中的某个mini-batch
     76     # 输出为训练得到的model在该mini-batch上的error
     77     validate_model = theano.function(
     78         [index],
     79         layer3.errors(y),
     80         givens={
     81             x: valid_set_x[index * batch_size: (index + 1) * batch_size],
     82             y: valid_set_y[index * batch_size: (index + 1) * batch_size]
     83         }
     84     )
     85 
     86     # 创建一个list，该list存放的是该CNN网络的所有待利用梯度下降法优化的参数
     87     params = layer3.params + layer2.params + layer1.params + layer0.params
     88 
     89     # 创建一个list，该list存放的是代价函数对该CNN网络的所有待利用梯度下降法优化的参数的梯度
     90     grads = T.grad(cost, params)
     91 
     92     # 为train模型创建更新规则，即创建一个list，自动更新params、grads中每一组值
     93     updates = [
     94         (param_i, param_i - learning_rate * grad_i)
     95         for param_i, grad_i in zip(params, grads)
     96     ]
     97 
     98     # 训练model，输入为具体要训练集中的某个mini-batch
     99     # 输出为训练得到的model在该mini-batch上的error
    100     train_model = theano.function(
    101         [index],
    102         cost,
    103         updates=updates,
    104         givens={
    105             x: train_set_x[index * batch_size: (index + 1) * batch_size],
    106             y: train_set_y[index * batch_size: (index + 1) * batch_size]
    107         }
    108     )

（3）Lenet-5中的training model部分

1     # 开始训练模型
     2     print '... training'
     3 
     4     # 定义一些进行early-stopping的相关参数
     5     # look as this many examples regardless
     6     patience = 10000
     7     # wait this much longer when a new best is found
     8     patience_increase = 2
     9     # a relative improvement of this much is considered significant
    10     improvement_threshold = 0.995
    11     # go through this many minibatche before checking the network on the validation set; in this case we check every epoch
    12     validation_frequency = min(n_train_batches, patience / 2)
    13 
    14     # 训练过程中需要的其他参数
    15     best_validation_loss = numpy.inf
    16     best_iter = 0
    17     test_score = 0.
    18     start_time = timeit.default_timer()
    19 
    20     epoch = 0
    21     done_looping = False
    22 
    23     while (epoch < n_epochs) and (not done_looping):
    24 
    25         #epoch次数增加1，每轮epoch，利用所有组mini-batch进行一次模型训练
    26         # 每轮epoch，整体的迭代次数iter增加n_train_batches次
    27         epoch = epoch + 1
    28 
    29         # 对于整个训练集中的第minibatch_index 个mini-batch
    30         # minibatch_index=0,1,...,n_train_batches-1
    31         for minibatch_index in xrange(n_train_batches):
    32 
    33             # 总的iter次数（每一轮epoch，iter个数都增加n_train_batches）
    34             # 即每一个iter，只利用一个mini-batch进行训练
    35             # 而每一个epoch，利用了所有的mini-batch进行训练
    36             iter = (epoch - 1) * n_train_batches + minibatch_index
    37 
    38             # 整体的迭代次数可以被100整除时，显示一次迭代次数
    39             if iter % 100 == 0:
    40                 print 'training @ iter = ', iter
    41 
    42             # 利用第minibatch_index个mini-batch训练model，得到model的代价函数
    43             cost_ij = train_model(minibatch_index)
    44 
    45             # 如果整体的迭代次数满足需要进行校验的条件，则对该次iter对应的model进行校验
    46             if (iter + 1) % validation_frequency == 0:
    47 
    48                 # 计算该model在校验集上的loss函数值
    49                 validation_losses = [validate_model(i) for i
    50                                      in xrange(n_valid_batches)]
    51                 this_validation_loss = numpy.mean(validation_losses)
    52                 print('epoch %i, minibatch %i/%i, validation error %f %%' %
    53                       (epoch, minibatch_index + 1, n_train_batches,
    54                        this_validation_loss * 100.))
    55 
    56                 # 如果该model在校验集的loss值小于之前的值
    57                 if this_validation_loss < best_validation_loss:
    58 
    59                     # 增加patience的值，目的是为了进行更多次的iter
    60                     # 也就是说，如果在测试集上的性能不如之前好，证明模型开始恶化，那么，不再进行那么多次的training了
    61                     if this_validation_loss < best_validation_loss *  \
    62                        improvement_threshold:
    63                         patience = max(patience, iter * patience_increase)
    64 
    65                     # save best validation score and iteration number
    66                     best_validation_loss = this_validation_loss
    67                     best_iter = iter
    68 
    69                     # 利用测试集测试该模型
    70                     test_losses = [
    71                         test_model(i)
    72                         for i in xrange(n_test_batches)
    73                     ]
    74                     # 计算测试集的loss值
    75                     test_score = numpy.mean(test_losses)
    76                     print(('     epoch %i, minibatch %i/%i, test error of '
    77                            'best model %f %%') %
    78                           (epoch, minibatch_index + 1, n_train_batches,
    79                            test_score * 100.))
    80 
    81             if patience <= iter:
    82                 done_looping = True
    83                 break
    84                 
    85     # 整个训练过程结束，记录training时间
    86     end_time = timeit.default_timer()
    87     print('Optimization complete.')
    88     print('Best validation score of %f %% obtained at iteration %i, '
    89           'with test performance %f %%' %
    90           (best_validation_loss * 100., best_iter + 1, test_score * 100.))
    91     print >> sys.stderr, ('The code for file ' +
    92                           os.path.split(__file__)[1] +
    93                           ' ran for %.2fm' % ((end_time - start_time) / 60.))

![736761-20160206021606397-18169317.jpg][]

（4）真个convolutional\_mlp的原始代码

![ContractedBlock.gif][] ![ExpandedBlockStart.gif][]

1 """This tutorial introduces the LeNet5 neural network architecture
      2 using Theano.  LeNet5 is a convolutional neural network, good for
      3 classifying images. This tutorial shows how to build the architecture,
      4 and comes with all the hyper-parameters you need to reproduce the
      5 paper's MNIST results.
      6 
      7 
      8 
      9 This implementation simplifies the model in the following ways:
     10 
     11  - LeNetConvPool doesn't implement location-specific gain and bias parameters
     12  - LeNetConvPool doesn't implement pooling by average, it implements pooling
     13    by max.
     14  - Digit classification is implemented with a logistic regression rather than
     15    an RBF network
     16  - LeNet5 was not fully-connected convolutions at second layer
     17 
     18 References:
     19  - Y. LeCun, L. Bottou, Y. Bengio and P. Haffner:
     20    Gradient-Based Learning Applied to Document
     21    Recognition, Proceedings of the IEEE, 86(11):2278-2324, November 1998.
     22    http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf
     23 
     24 """
     25 import os
     26 import sys
     27 import timeit
     28 
     29 import numpy
     30 
     31 import theano
     32 import theano.tensor as T
     33 from theano.tensor.signal import downsample
     34 from theano.tensor.nnet import conv
     35 
     36 from logistic_sgd import LogisticRegression, load_data
     37 from mlp import HiddenLayer
     38 
     39 
     40 class LeNetConvPoolLayer(object):
     41     """Pool Layer of a convolutional network """
     42 
     43     def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2, 2)):
     44         """
     45         Allocate a LeNetConvPoolLayer with shared variable internal parameters.
     46 
     47         :type rng: numpy.random.RandomState
     48         :param rng: a random number generator used to initialize weights
     49 
     50         :type input: theano.tensor.dtensor4
     51         :param input: symbolic image tensor, of shape image_shape
     52 
     53         :type filter_shape: tuple or list of length 4
     54         :param filter_shape: (number of filters, num input feature maps,
     55                               filter height, filter width)
     56 
     57         :type image_shape: tuple or list of length 4
     58         :param image_shape: (batch size, num input feature maps,
     59                              image height, image width)
     60 
     61         :type poolsize: tuple or list of length 2
     62         :param poolsize: the downsampling (pooling) factor (#rows, #cols)
     63         """
     64 
     65         assert image_shape[1] == filter_shape[1]
     66         self.input = input
     67 
     68         # there are "num input feature maps * filter height * filter width"
     69         # inputs to each hidden unit
     70         fan_in = numpy.prod(filter_shape[1:])
     71         # each unit in the lower layer receives a gradient from:
     72         # "num output feature maps * filter height * filter width" /
     73         #   pooling size
     74         fan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]) /
     75                    numpy.prod(poolsize))
     76         # initialize weights with random weights
     77         W_bound = numpy.sqrt(6. / (fan_in + fan_out))
     78         self.W = theano.shared(
     79             numpy.asarray(
     80                 rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
     81                 dtype=theano.config.floatX
     82             ),
     83             borrow=True
     84         )
     85 
     86         # the bias is a 1D tensor -- one bias per output feature map
     87         b_values = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)
     88         self.b = theano.shared(value=b_values, borrow=True)
     89 
     90         # convolve input feature maps with filters
     91         conv_out = conv.conv2d(
     92             input=input,
     93             filters=self.W,
     94             filter_shape=filter_shape,
     95             image_shape=image_shape
     96         )
     97 
     98         # downsample each feature map individually, using maxpooling
     99         pooled_out = downsample.max_pool_2d(
    100             input=conv_out,
    101             ds=poolsize,
    102             ignore_border=True
    103         )
    104 
    105         # add the bias term. Since the bias is a vector (1D array), we first
    106         # reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
    107         # thus be broadcasted across mini-batches and feature map
    108         # width & height
    109         self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
    110 
    111         # store parameters of this layer
    112         self.params = [self.W, self.b]
    113 
    114         # keep track of model input
    115         self.input = input
    116 
    117 
    118 def evaluate_lenet5(learning_rate=0.1, n_epochs=200,
    119                     dataset='mnist.pkl.gz',
    120                     nkerns=[20, 50], batch_size=500):
    121     """ Demonstrates lenet on MNIST dataset
    122 
    123     :type learning_rate: float
    124     :param learning_rate: learning rate used (factor for the stochastic
    125                           gradient)
    126 
    127     :type n_epochs: int
    128     :param n_epochs: maximal number of epochs to run the optimizer
    129 
    130     :type dataset: string
    131     :param dataset: path to the dataset used for training /testing (MNIST here)
    132 
    133     :type nkerns: list of ints
    134     :param nkerns: number of kernels on each layer
    135     """
    136 
    137     rng = numpy.random.RandomState(23455)
    138 
    139     datasets = load_data(dataset)
    140 
    141     train_set_x, train_set_y = datasets[0]
    142     valid_set_x, valid_set_y = datasets[1]
    143     test_set_x, test_set_y = datasets[2]
    144 
    145     # compute number of minibatches for training, validation and testing
    146     n_train_batches = train_set_x.get_value(borrow=True).shape[0]
    147     n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
    148     n_test_batches = test_set_x.get_value(borrow=True).shape[0]
    149     n_train_batches /= batch_size
    150     n_valid_batches /= batch_size
    151     n_test_batches /= batch_size
    152 
    153     # allocate symbolic variables for the data
    154     index = T.lscalar()  # index to a [mini]batch
    155 
    156     # start-snippet-1
    157     x = T.matrix('x')   # the data is presented as rasterized images
    158     y = T.ivector('y')  # the labels are presented as 1D vector of
    159                         # [int] labels
    160 
    161     ######################
    162     # BUILD ACTUAL MODEL #
    163     ######################
    164     print '... building the model'
    165 
    166     # Reshape matrix of rasterized images of shape (batch_size, 28 * 28)
    167     # to a 4D tensor, compatible with our LeNetConvPoolLayer
    168     # (28, 28) is the size of MNIST images.
    169     layer0_input = x.reshape((batch_size, 1, 28, 28))
    170 
    171     # Construct the first convolutional pooling layer:
    172     # filtering reduces the image size to (28-5+1 , 28-5+1) = (24, 24)
    173     # maxpooling reduces this further to (24/2, 24/2) = (12, 12)
    174     # 4D output tensor is thus of shape (batch_size, nkerns[0], 12, 12)
    175     layer0 = LeNetConvPoolLayer(
    176         rng,
    177         input=layer0_input,
    178         image_shape=(batch_size, 1, 28, 28),
    179         filter_shape=(nkerns[0], 1, 5, 5),
    180         poolsize=(2, 2)
    181     )
    182 
    183     # Construct the second convolutional pooling layer
    184     # filtering reduces the image size to (12-5+1, 12-5+1) = (8, 8)
    185     # maxpooling reduces this further to (8/2, 8/2) = (4, 4)
    186     # 4D output tensor is thus of shape (batch_size, nkerns[1], 4, 4)
    187     layer1 = LeNetConvPoolLayer(
    188         rng,
    189         input=layer0.output,
    190         image_shape=(batch_size, nkerns[0], 12, 12),
    191         filter_shape=(nkerns[1], nkerns[0], 5, 5),
    192         poolsize=(2, 2)
    193     )
    194 
    195     # the HiddenLayer being fully-connected, it operates on 2D matrices of
    196     # shape (batch_size, num_pixels) (i.e matrix of rasterized images).
    197     # This will generate a matrix of shape (batch_size, nkerns[1] * 4 * 4),
    198     # or (500, 50 * 4 * 4) = (500, 800) with the default values.
    199     layer2_input = layer1.output.flatten(2)
    200 
    201     # construct a fully-connected sigmoidal layer
    202     layer2 = HiddenLayer(
    203         rng,
    204         input=layer2_input,
    205         n_in=nkerns[1] * 4 * 4,
    206         n_out=500,
    207         activation=T.tanh
    208     )
    209 
    210     # classify the values of the fully-connected sigmoidal layer
    211     layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
    212 
    213     # the cost we minimize during training is the NLL of the model
    214     cost = layer3.negative_log_likelihood(y)
    215 
    216     # create a function to compute the mistakes that are made by the model
    217     test_model = theano.function(
    218         [index],
    219         layer3.errors(y),
    220         givens={
    221             x: test_set_x[index * batch_size: (index + 1) * batch_size],
    222             y: test_set_y[index * batch_size: (index + 1) * batch_size]
    223         }
    224     )
    225 
    226     validate_model = theano.function(
    227         [index],
    228         layer3.errors(y),
    229         givens={
    230             x: valid_set_x[index * batch_size: (index + 1) * batch_size],
    231             y: valid_set_y[index * batch_size: (index + 1) * batch_size]
    232         }
    233     )
    234 
    235     # create a list of all model parameters to be fit by gradient descent
    236     params = layer3.params + layer2.params + layer1.params + layer0.params
    237 
    238     # create a list of gradients for all model parameters
    239     grads = T.grad(cost, params)
    240 
    241     # train_model is a function that updates the model parameters by
    242     # SGD Since this model has many parameters, it would be tedious to
    243     # manually create an update rule for each model parameter. We thus
    244     # create the updates list by automatically looping over all
    245     # (params[i], grads[i]) pairs.
    246     updates = [
    247         (param_i, param_i - learning_rate * grad_i)
    248         for param_i, grad_i in zip(params, grads)
    249     ]
    250 
    251     train_model = theano.function(
    252         [index],
    253         cost,
    254         updates=updates,
    255         givens={
    256             x: train_set_x[index * batch_size: (index + 1) * batch_size],
    257             y: train_set_y[index * batch_size: (index + 1) * batch_size]
    258         }
    259     )
    260     # end-snippet-1
    261 
    262     ###############
    263     # TRAIN MODEL #
    264     ###############
    265     print '... training'
    266     # early-stopping parameters
    267     patience = 10000  # look as this many examples regardless
    268     patience_increase = 2  # wait this much longer when a new best is
    269                            # found
    270     improvement_threshold = 0.995  # a relative improvement of this much is
    271                                    # considered significant
    272     validation_frequency = min(n_train_batches, patience / 2)
    273                                   # go through this many
    274                                   # minibatche before checking the network
    275                                   # on the validation set; in this case we
    276                                   # check every epoch
    277 
    278     best_validation_loss = numpy.inf
    279     best_iter = 0
    280     test_score = 0.
    281     start_time = timeit.default_timer()
    282 
    283     epoch = 0
    284     done_looping = False
    285 
    286     while (epoch < n_epochs) and (not done_looping):
    287         epoch = epoch + 1
    288         for minibatch_index in xrange(n_train_batches):
    289 
    290             iter = (epoch - 1) * n_train_batches + minibatch_index
    291 
    292             if iter % 100 == 0:
    293                 print 'training @ iter = ', iter
    294             cost_ij = train_model(minibatch_index)
    295 
    296             if (iter + 1) % validation_frequency == 0:
    297 
    298                 # compute zero-one loss on validation set
    299                 validation_losses = [validate_model(i) for i
    300                                      in xrange(n_valid_batches)]
    301                 this_validation_loss = numpy.mean(validation_losses)
    302                 print('epoch %i, minibatch %i/%i, validation error %f %%' %
    303                       (epoch, minibatch_index + 1, n_train_batches,
    304                        this_validation_loss * 100.))
    305 
    306                 # if we got the best validation score until now
    307                 if this_validation_loss < best_validation_loss:
    308 
    309                     #improve patience if loss improvement is good enough
    310                     if this_validation_loss < best_validation_loss *  \
    311                        improvement_threshold:
    312                         patience = max(patience, iter * patience_increase)
    313 
    314                     # save best validation score and iteration number
    315                     best_validation_loss = this_validation_loss
    316                     best_iter = iter
    317 
    318                     # test it on the test set
    319                     test_losses = [
    320                         test_model(i)
    321                         for i in xrange(n_test_batches)
    322                     ]
    323                     test_score = numpy.mean(test_losses)
    324                     print(('     epoch %i, minibatch %i/%i, test error of '
    325                            'best model %f %%') %
    326                           (epoch, minibatch_index + 1, n_train_batches,
    327                            test_score * 100.))
    328 
    329             if patience <= iter:
    330                 done_looping = True
    331                 break
    332 
    333     end_time = timeit.default_timer()
    334     print('Optimization complete.')
    335     print('Best validation score of %f %% obtained at iteration %i, '
    336           'with test performance %f %%' %
    337           (best_validation_loss * 100., best_iter + 1, test_score * 100.))
    338     print >> sys.stderr, ('The code for file ' +
    339                           os.path.split(__file__)[1] +
    340                           ' ran for %.2fm' % ((end_time - start_time) / 60.))
    341 
    342 if __name__ == '__main__':
    343     evaluate_lenet5()
    344 
    345 
    346 def experiment(state, channel):
    347     evaluate_lenet5(state.learning_rate, dataset=state.dataset)

[736761-20160206031806585-982182128.jpg]: /images/20210716/ca09f4c4ae1e4e8490dcd62dcb7eabfc.png
[736761-20160206021606397-18169317.jpg]: /images/20210716/0e357b1636224d06a5aff35678a3be60.png
[ContractedBlock.gif]: http://images.cnblogs.com/OutliningIndicators/ContractedBlock.gif
[ExpandedBlockStart.gif]: /images/20210716/1cd8a68915db480ebcd8c4587e115dc6.png