基于python的Lasagne包对minist的代码解读

本例是基于python的Lasagne包中对minist数据集的卷积神经网络的代码的一个解读,其中英文部分因为怕自己理解错误，所以保留了下来；由于该部分代码主要还是基于theano进行开发，所以里面的很多函数都是借鉴或直接用了theano中的函数，要想真正理解还是需要学习一下theano,包括其中对于变量和函数设计的理念，其实都是很有意味的！
theano: http://deeplearning.net/software/theano/#
lasagne下载地址 https://github.com/Lasagne/Lasagne

#!/usr/bin/env python"""Usage example employing Lasagne for digit recognition using the MNIST dataset.This example is deliberately structured as a long flat file, focusing on howto use Lasagne, instead of focusing on writing maximally modular and reusablecode. It is used as the foundation for the introductory Lasagne tutorial:http://lasagne.readthedocs.org/en/latest/user/tutorial.htmlMore in-depth examples and reproductions of paper results are maintained ina separate repository: https://github.com/Lasagne/Recipes"""from __future__ import print_functionimport sysimport osimport timeimport numpy as npimport theanoimport theano.tensor as Timport lasagne# 导入该导入的模块# ################## Download and prepare the MNIST dataset ################### This is just some way of getting the MNIST dataset from an online location# and loading it into numpy arrays. It doesn't involve Lasagne at all.def load_dataset():    # We first define a download function, supporting both Python 2 and 3.    if sys.version_info[0] == 2:        from urllib import urlretrieve    else:        from urllib.request import urlretrieve    def download(filename, source='http://yann.lecun.com/exdb/mnist/'):        print("Downloading %s" % filename)        urlretrieve(source + filename, filename)    # We then define functions for loading MNIST images and labels.    # For convenience, they also download the requested files if needed.    import gzip    def load_mnist_images(filename):        if not os.path.exists(filename):            download(filename)        # Read the inputs in Yann LeCun's binary format.        with gzip.open(filename, 'rb') as f:            data = np.frombuffer(f.read(), np.uint8, offset=16)        # The inputs are vectors now, we reshape them to monochrome 2D images,        # following the shape convention: (examples, channels, rows, columns)        data = data.reshape(-1, 1, 28, 28)        # The inputs come as bytes, we convert them to float32 in range [0,1].        # (Actually to range [0, 255/256], for compatibility to the version        # provided at http://deeplearning.net/data/mnist/mnist.pkl.gz.)        return data / np.float32(256)    def load_mnist_labels(filename):        if not os.path.exists(filename):            download(filename)        # Read the labels in Yann LeCun's binary format.        with gzip.open(filename, 'rb') as f:            data = np.frombuffer(f.read(), np.uint8, offset=8)        # The labels are vectors of integers now, that's exactly what we want.        return data    # We can now download and read the training and test set images and labels.    X_train = load_mnist_images('train-images-idx3-ubyte.gz')    y_train = load_mnist_labels('train-labels-idx1-ubyte.gz')    X_test = load_mnist_images('t10k-images-idx3-ubyte.gz')    y_test = load_mnist_labels('t10k-labels-idx1-ubyte.gz')    # We reserve the last 10000 training examples for validation.    X_train, X_val = X_train[:-10000], X_train[-10000:]    y_train, y_val = y_train[:-10000], y_train[-10000:]    # We just return all the arrays in order, as expected in main().    # (It doesn't matter how we do this as long as we can read them again.)    return X_train, y_train, X_val, y_val, X_test, y_test# 读取数据集，该处使用前10000维作为训练，后10000维作为验证集# ##################### Build the neural network model ######################## This script supports three types of models. For each one, we define a# function that takes a Theano variable representing the input and returns# the output layer of a neural network model built in Lasagne.#下面定义了三种网络结构：多层感知机、自定义多层感知机、卷积神经网络def build_mlp(input_var=None):    # 定义了含有800个元件的两个隐含层，一个10个元件的softmax，输入层dropout为0.2，    # 隐含层为0.5；    # 输入层的shape中：    # None表示batch size是可以变的，没有被特别指定；    # 接下来三个参数为输入数据维度，然后dropout掉0.2    # 隐藏层使用神经元max(0,x);输出层为softmax    # This creates an MLP of two hidden layers of 800 units each, followed by    # a softmax output layer of 10 units. It applies 20% dropout to the input    # data and 50% dropout to the hidden layers.    # Input layer, specifying the expected input shape of the network    # (unspecified batchsize, 1 channel, 28 rows and 28 columns) and    # linking it to the given Theano variable `input_var`, if any:    l_in = lasagne.layers.InputLayer(shape=(None, 1, 28, 28),                                     input_var=input_var)    # Apply 20% dropout to the input data:    l_in_drop = lasagne.layers.DropoutLayer(l_in, p=0.2)    # Add a fully-connected layer of 800 units, using the linear rectifier, and    # initializing weights with Glorot's scheme (which is the default anyway):    l_hid1 = lasagne.layers.DenseLayer(            l_in_drop, num_units=800,            nonlinearity=lasagne.nonlinearities.rectify,            W=lasagne.init.GlorotUniform())    #    # We'll now add dropout of 50%:    l_hid1_drop = lasagne.layers.DropoutLayer(l_hid1, p=0.5)    # Another 800-unit layer:    l_hid2 = lasagne.layers.DenseLayer(            l_hid1_drop, num_units=800,            nonlinearity=lasagne.nonlinearities.rectify)    # 50% dropout again:    l_hid2_drop = lasagne.layers.DropoutLayer(l_hid2, p=0.5)    # Finally, we'll add the fully-connected output layer, of 10 softmax units:    l_out = lasagne.layers.DenseLayer(            l_hid2_drop, num_units=10,            nonlinearity=lasagne.nonlinearities.softmax)    # Each layer is linked to its incoming layer(s), so we only need to pass    # the output layer to give access to a network in Lasagne:    return l_outdef build_custom_mlp(input_var=None, depth=2, width=800, drop_input=.2,                     drop_hidden=.5):    # 定义了含有800个元件的两个隐含层，一个10个元件的softmax，输入层dropout为0.2，    # 隐含层为0.5；    # 输入层的shape中：    # None表示batch size是可以变的，没有被特别指定；    # 接下来三个参数为输入数据维度，然后dropout:0.2    # 隐藏层使用线性整流，神经元max(0,x);输出层为softmax    # 该步基本用的都是上面mlp的函数，其中隐含层的层数由depth决定，隐含层每层神经元个数    # 有width决定    # By default, this creates the same network as `build_mlp`, but it can be    # customized with respect to the number and size of hidden layers. This    # mostly showcases how creating a network in Python code can be a lot more    # flexible than a configuration file. Note that to make the code easier,    # all the layers are just called `network` -- there is no need to give them    # different names if all we return is the last one we created anyway; we    # just used different names above for clarity.    # Input layer and dropout (with shortcut `dropout` for `DropoutLayer`):    network = lasagne.layers.InputLayer(shape=(None, 1, 28, 28),                                        input_var=input_var)    if drop_input:        network = lasagne.layers.dropout(network, p=drop_input)    # Hidden layers and dropout:    nonlin = lasagne.nonlinearities.rectify    for _ in range(depth):        network = lasagne.layers.DenseLayer(                network, width, nonlinearity=nonlin)        if drop_hidden:            network = lasagne.layers.dropout(network, p=drop_hidden)    # Output layer:    softmax = lasagne.nonlinearities.softmax    network = lasagne.layers.DenseLayer(network, 10, nonlinearity=softmax)    return networkdef build_cnn(input_var=None):    # 接下来定义了卷积神经网络：    # 输入层跟上面相同    # 卷积层使用32个卷积核，每个大小5x5，依然适用ReLU神经元，W初始化采用GlorotUniform（）    # 函数（也就是uniform分布）赋值    # pooling层使用max_pooling的2x2    # 以上两层重复两次，之后接全连接层    # 全连接层依然采用DenseLayer函数定义，神经元用ReLU，采用0.5的dropout，一共用了256个神经元    # 输出层采用softmax，用了10个神经元，我理解为输出的类一共有10个，每一个类都有一个[0,1]的值，    # 代表属于该类的一个概率，    # 最后输出的是一个和为1的向量，同样对于其输入也有0.5的进行dropout    # As a third model, we'll create a CNN of two convolution + pooling stages    # and a fully-connected hidden layer in front of the output layer.    # Input layer, as usual:    network = lasagne.layers.InputLayer(shape=(None, 1, 28, 28),                                        input_var=input_var)    # This time we do not apply input dropout, as it tends to work less well    # for convolutional layers.    # Convolutional layer with 32 kernels of size 5x5. Strided and padded    # convolutions are supported as well; see the docstring.    network = lasagne.layers.Conv2DLayer(            network, num_filters=32, filter_size=(5, 5),            nonlinearity=lasagne.nonlinearities.rectify,            W=lasagne.init.GlorotUniform())    # Expert note: Lasagne provides alternative convolutional layers that    # override Theano's choice of which implementation to use; for details    # please see http://lasagne.readthedocs.org/en/latest/user/tutorial.html.    # Max-pooling layer of factor 2 in both dimensions:    network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))    # Another convolution with 32 5x5 kernels, and another 2x2 pooling:    network = lasagne.layers.Conv2DLayer(            network, num_filters=32, filter_size=(5, 5),            nonlinearity=lasagne.nonlinearities.rectify)    network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))    # A fully-connected layer of 256 units with 50% dropout on its inputs:    network = lasagne.layers.DenseLayer(            lasagne.layers.dropout(network, p=.5),            num_units=256,            nonlinearity=lasagne.nonlinearities.rectify)    # And, finally, the 10-unit output layer with 50% dropout on its inputs:    network = lasagne.layers.DenseLayer(            lasagne.layers.dropout(network, p=.5),            num_units=10,            nonlinearity=lasagne.nonlinearities.softmax)    return network# ############################# Batch iterator ################################ This is just a simple helper function iterating over training data in# mini-batches of a particular size, optionally in random order. It assumes# data is available as numpy arrays. For big datasets, you could load numpy# arrays as memory-mapped files (np.load(..., mmap_mode='r')), or write your# own custom data iteration function. For small datasets, you can also copy# them to GPU at once for slightly improved performance. This would involve# several changes in the main program, though, and is not demonstrated here.# Notice that this function returns only mini-batches of size `batchsize`.# If the size of the data is not a multiple of `batchsize`, it will not# return the last (remaining) mini-batch.# 对于采用mini_batch进行迭代的过程，需要输入输入数据集，输入数据的标签，batch size的大小，# 以及是否重新洗牌# 如果索引将会被打乱，然后按照batch size分批次进行迭代；def iterate_minibatches(inputs, targets, batchsize, shuffle=False):    assert len(inputs) == len(targets)    if shuffle:        indices = np.arange(len(inputs))        np.random.shuffle(indices)    for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):        if shuffle:            excerpt = indices[start_idx:start_idx + batchsize]        else:            excerpt = slice(start_idx, start_idx + batchsize)        yield inputs[excerpt], targets[excerpt]# ############################## Main program ################################# Everything else will be handled in our main program now. We could pull out# more functions to better separate the code, but it wouldn't make it any# easier to read.# 主函数：首先确定模型种类，然后根据种类确定相应的参数mlp没什么好说的，都是上面的参数；自定义# mlp需要输入深度隐含层个数depth、每一层神经元个数width以及输入层dropout率和隐含层dropout率；# 个人觉得这里是淘汰率；因为根据Hinton的dropout的文章保留率应该是0.5~1# 对于cnn也与上面相同通过已经构造出来的网络得到预测值向量prediction，然后计算绝对交叉熵计算损失值，# coding_list是# softmax输出结果，而target_list是其结果的标签，即t_{i,j}，因此其数学公式为math:: # L_i = - \\sum_j{t_{i,j} \\log(p_{i,j})}# 取其样本正确结果预测值的对数就是损失值# 更新权重的方法，本次代码默认采用nesterov_momentum，其中超参learning_rate=0.01, momentum=0.9# 最后，认为预测值最大值就是其判断值，如果它与真标签一致就返回1，否则返回0，然后算出平均值，就是其准确度# 如果是训练阶段，抛出损失值进入迭代更新；如果是测试集，就直接输出准确度和损失值# 进入迭代阶段，本次一共进行500次迭代，首先是训练阶段一共迭代产生500batch，每个batch都要计算损失值，# 然后迭代更新权重，之后再用500个# batch进行验证，求出平均准确度并输出；这样进行500次迭代# 进行完500次训练和验证之后，再产生500个测试集的batch进行测试，输出最后的500个准确度的平均值就是# Final resultsdef main(model='mlp', num_epochs=500):    # Load the dataset    print("Loading data...")    X_train, y_train, X_val, y_val, X_test, y_test = load_dataset()    # Prepare Theano variables for inputs and targets    #byte: bscalar, bvector, bmatrix, brow, bcol, btensor3, btensor4    #16-bit integers: wscalar, wvector, wmatrix, wrow, wcol, wtensor3, wtensor4    #32-bit integers: iscalar, ivector, imatrix, irow, icol, itensor3, itensor4    #64-bit integers: lscalar, lvector, lmatrix, lrow, lcol, ltensor3, ltensor4    #float: fscalar, fvector, fmatrix, frow, fcol, ftensor3, ftensor4    #double: dscalar, dvector, dmatrix, drow, dcol, dtensor3, dtensor4    #complex: cscalar, cvector, cmatrix, crow, ccol, ctensor3, ctensor4    # 此处创建了一个以'inputs'为名的四维张量对象和一个以‘targets’为名的dtype=int32的一维向量    input_var = T.tensor4('inputs')    target_var = T.ivector('targets')    # Create neural network model (depending on first command line parameter)    print("Building model and compiling functions...")    if model == 'mlp':        network = build_mlp(input_var)    elif model.startswith('custom_mlp:'):        depth, width, drop_in, drop_hid = model.split(':', 1)[1].split(',')        network = build_custom_mlp(input_var, int(depth), int(width),                                   float(drop_in), float(drop_hid))    elif model == 'cnn':        network = build_cnn(input_var)    else:        print("Unrecognized model type %r." % model)        return    # Create a loss expression for training, i.e., a scalar objective we want    # to minimize (for our multi-class problem, it is the cross-entropy loss):    prediction = lasagne.layers.get_output(network)    loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)    loss = loss.mean()    # We could add some weight decay as well here, see lasagne.regularization.    # Create update expressions for training, i.e., how to modify the    # parameters at each training step. Here, we'll use Stochastic Gradient    # Descent (SGD) with Nesterov momentum, but Lasagne offers plenty more.    params = lasagne.layers.get_all_params(network, trainable=True)    updates = lasagne.updates.nesterov_momentum(            loss, params, learning_rate=0.01, momentum=0.9)    # Create a loss expression for validation/testing. The crucial difference    # here is that we do a deterministic forward pass through the network,    # disabling dropout layers.    test_prediction = lasagne.layers.get_output(network, deterministic=True)    test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,                                                            target_var)    test_loss = test_loss.mean()    # As a bonus, also create an expression for the classification accuracy:    test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var),                      dtype=theano.config.floatX)    # Compile a function performing a training step on a mini-batch (by giving    # the updates dictionary) and returning the corresponding training loss:    train_fn = theano.function([input_var, target_var], loss, updates=updates)    # Compile a second function computing the validation loss and accuracy:    val_fn = theano.function([input_var, target_var], [test_loss, test_acc])    # Finally, launch the training loop.    print("Starting training...")    # We iterate over epochs:    for epoch in range(num_epochs):        # In each epoch, we do a full pass over the training data:        train_err = 0        train_batches = 0        start_time = time.time()        for batch in iterate_minibatches(X_train, y_train, 500, shuffle=True):            inputs, targets = batch            train_err += train_fn(inputs, targets)            train_batches += 1        # And a full pass over the validation data:        val_err = 0        val_acc = 0        val_batches = 0        for batch in iterate_minibatches(X_val, y_val, 500, shuffle=False):            inputs, targets = batch            err, acc = val_fn(inputs, targets)            val_err += err            val_acc += acc            val_batches += 1        # Then we print the results for this epoch:        print("Epoch {} of {} took {:.3f}s".format(            epoch + 1, num_epochs, time.time() - start_time))        print("  training loss:\t\t{:.6f}".format(train_err / train_batches))        print("  validation loss:\t\t{:.6f}".format(val_err / val_batches))        print("  validation accuracy:\t\t{:.2f} %".format(            val_acc / val_batches * 100))    # After training, we compute and print the test error:    test_err = 0    test_acc = 0    test_batches = 0    for batch in iterate_minibatches(X_test, y_test, 500, shuffle=False):        inputs, targets = batch        err, acc = val_fn(inputs, targets)        test_err += err        test_acc += acc        test_batches += 1    print("Final results:")    print("  test loss:\t\t\t{:.6f}".format(test_err / test_batches))    print("  test accuracy:\t\t{:.2f} %".format(        test_acc / test_batches * 100))    # Optionally, you could now dump the network weights to a file like this:    # np.savez('model.npz', *lasagne.layers.get_all_param_values(network))    #    # And load them again later on like this:    # with np.load('model.npz') as f:    #     param_values = [f['arr_%d' % i] for i in range(len(f.files))]    # lasagne.layers.set_all_param_values(network, param_values)# 主函数部分是需要接受两个参数，一个是模型，一个是迭代次数，如果model='custom_mlp'需要# 给出'custom_mlp:DEPTH,WIDTH,DROP_IN,DROP_HID'迭代次数默认500if __name__ == '__main__':    if ('--help' in sys.argv) or ('-h' in sys.argv):        print("Trains a neural network on MNIST using Lasagne.")        print("Usage: %s [MODEL [EPOCHS]]" % sys.argv[0])        print()        print("MODEL: 'mlp' for a simple Multi-Layer Perceptron (MLP),")        print("       'custom_mlp:DEPTH,WIDTH,DROP_IN,DROP_HID' for an MLP")        print("       with DEPTH hidden layers of WIDTH units, DROP_IN")        print("       input dropout and DROP_HID hidden dropout,")        print("       'cnn' for a simple Convolutional Neural Network (CNN).")        print("EPOCHS: number of training epochs to perform (default: 500)")    else:        kwargs = {}        if len(sys.argv) > 1:            kwargs['model'] = sys.argv[1]        if len(sys.argv) > 2:            kwargs['num_epochs'] = int(sys.argv[2])        main(**kwargs)

这里解释一下theano 的基本工作原理
Theano Graphs：
写theano代码的第一步就是用symbol来表示所有的数学公式或函数。
这些操作在theano中称为ops.
op ：表示的就是一个计算单元。
在Theano内部会把theano的代码表示成图形，组成theano图的基本元件包括：
变量节点 ：包括input、output node
操作节点：op nodes
应用节点：apply nodes : 每一个op 会需要有一个apply node，这个op是绑定在这个apply上，通过apply node把op需要的输入和输出以及op本身组合在一起，示意图：

以上关于theano的部分建议看一下：http://blog.sina.com.cn/s/blog_80ce3a550102veyz.html
或者参考论文：http://arxiv.org/pdf/1211.5590v1.pdf