gcc 源码阅读---语法树

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c内核源码阅读准备知识（erlang mudos lua） 准备在以后的博客中主要就是写自己对一些源码的学习和理解。那再阅读之前肯定是要准备一些基础的知识的。请看： 1.         c语言 就目前的项目情况来看，无论是erlang还是java还是现在的mudos，虚拟机部分都是用c来实现的，所以呢，对编写人员的c语言掌握要求挺高，不仅需要熟知语法，还要熟悉一些习惯用法和常用技巧，系统函数的调用也必须清楚。 2.         数据结构 阅读详情

gcc 语法树定义在 tree.def中

Line 41: DEFTREECODE (ERROR_MARK, "error_mark", tcc_exceptional, 0)
    Line 47: DEFTREECODE (IDENTIFIER_NODE, "identifier_node", tcc_exceptional, 0)
    Line 54: DEFTREECODE (TREE_LIST, "tree_list", tcc_exceptional, 0)
    Line 57: DEFTREECODE (TREE_VEC, "tree_vec", tcc_exceptional, 0)
    Line 75: DEFTREECODE (BLOCK, "block", tcc_exceptional, 0)
    Line 122: DEFTREECODE (OFFSET_TYPE, "offset_type", tcc_type, 0)
    Line 135: DEFTREECODE (ENUMERAL_TYPE, "enumeral_type", tcc_type, 0)
    Line 139: DEFTREECODE (BOOLEAN_TYPE, "boolean_type", tcc_type, 0)
    Line 145: DEFTREECODE (INTEGER_TYPE, "integer_type", tcc_type, 0)
    Line 152: DEFTREECODE (BITINT_TYPE, "bitint_type", tcc_type, 0)
    Line 156: DEFTREECODE (REAL_TYPE, "real_type", tcc_type, 0)
    Line 165: DEFTREECODE (POINTER_TYPE, "pointer_type", tcc_type, 0)
    Line 169: DEFTREECODE (REFERENCE_TYPE, "reference_type", tcc_type, 0)
    Line 172: DEFTREECODE (NULLPTR_TYPE, "nullptr_type", tcc_type, 0)
    Line 177: DEFTREECODE (FIXED_POINT_TYPE, "fixed_point_type", tcc_type, 0)
    Line 186: DEFTREECODE (COMPLEX_TYPE, "complex_type", tcc_type, 0)
    Line 191: DEFTREECODE (VECTOR_TYPE, "vector_type", tcc_type, 0)
    Line 207: DEFTREECODE (ARRAY_TYPE, "array_type", tcc_type, 0)
    Line 217: DEFTREECODE (RECORD_TYPE, "record_type", tcc_type, 0)
    Line 223: DEFTREECODE (UNION_TYPE, "union_type", tcc_type, 0)    /* C union type */
    Line 229: DEFTREECODE (QUAL_UNION_TYPE, "qual_union_type", tcc_type, 0)
    Line 236: DEFTREECODE (VOID_TYPE, "void_type", tcc_type, 0)
    Line 246: DEFTREECODE (FUNCTION_TYPE, "function_type", tcc_type, 0)
    Line 253: DEFTREECODE (METHOD_TYPE, "method_type", tcc_type, 0)
    Line 259: DEFTREECODE (LANG_TYPE, "lang_type", tcc_type, 0)
    Line 265: DEFTREECODE (OPAQUE_TYPE, "opaque_type", tcc_type, 0)
    Line 271: DEFTREECODE (VOID_CST, "void_cst", tcc_constant, 0)
    Line 291: DEFTREECODE (INTEGER_CST, "integer_cst", tcc_constant, 0)
    Line 294: DEFTREECODE (POLY_INT_CST, "poly_int_cst", tcc_constant, 0)
    Line 297: DEFTREECODE (REAL_CST, "real_cst", tcc_constant, 0)
    Line 300: DEFTREECODE (FIXED_CST, "fixed_cst", tcc_constant, 0)
    Line 304: DEFTREECODE (COMPLEX_CST, "complex_cst", tcc_constant, 0)
    Line 307: DEFTREECODE (VECTOR_CST, "vector_cst", tcc_constant, 0)
    Line 310: DEFTREECODE (STRING_CST, "string_cst", tcc_constant, 0)
    Line 320: DEFTREECODE (RAW_DATA_CST, "raw_data_cst", tcc_constant, 0)
    Line 380: DEFTREECODE (FUNCTION_DECL, "function_decl", tcc_declaration, 0)
    Line 381: DEFTREECODE (LABEL_DECL, "label_decl", tcc_declaration, 0)
    Line 386: DEFTREECODE (FIELD_DECL, "field_decl", tcc_declaration, 0)
    Line 387: DEFTREECODE (VAR_DECL, "var_decl", tcc_declaration, 0)
    Line 388: DEFTREECODE (CONST_DECL, "const_decl", tcc_declaration, 0)
    Line 389: DEFTREECODE (PARM_DECL, "parm_decl", tcc_declaration, 0)
    Line 390: DEFTREECODE (TYPE_DECL, "type_decl", tcc_declaration, 0)
    Line 391: DEFTREECODE (RESULT_DECL, "result_decl", tcc_declaration, 0)
    Line 395: DEFTREECODE (DEBUG_EXPR_DECL, "debug_expr_decl", tcc_declaration, 0)
    Line 398: DEFTREECODE (DEBUG_BEGIN_STMT, "debug_begin_stmt", tcc_statement, 0)
    Line 402: DEFTREECODE (NAMESPACE_DECL, "namespace_decl", tcc_declaration, 0)
    Line 413: DEFTREECODE (IMPORTED_DECL, "imported_decl", tcc_declaration, 0)
    Line 423: DEFTREECODE (NAMELIST_DECL, "namelist_decl", tcc_declaration, 0)
    Line 427: DEFTREECODE (TRANSLATION_UNIT_DECL, "translation_unit_decl",\
    Line 440: DEFTREECODE (COMPONENT_REF, "component_ref", tcc_reference, 3)
    Line 450: DEFTREECODE (BIT_FIELD_REF, "bit_field_ref", tcc_reference, 3)
    Line 457: DEFTREECODE (ARRAY_REF, "array_ref", tcc_reference, 4)
    Line 462: DEFTREECODE (ARRAY_RANGE_REF, "array_range_ref", tcc_reference, 4)
    Line 466: DEFTREECODE (REALPART_EXPR, "realpart_expr", tcc_reference, 1)
    Line 467: DEFTREECODE (IMAGPART_EXPR, "imagpart_expr", tcc_reference, 1)
    Line 479: DEFTREECODE (VIEW_CONVERT_EXPR, "view_convert_expr", tcc_reference, 1)
    Line 482: DEFTREECODE (INDIRECT_REF, "indirect_ref", tcc_reference, 1)
    Line 495: DEFTREECODE (OBJ_TYPE_REF, "obj_type_ref", tcc_expression, 3)
    Line 522: DEFTREECODE (CONSTRUCTOR, "constructor", tcc_exceptional, 0)
    Line 525:    of DEFTREECODE saying how many operands there are.
    Line 532: DEFTREECODE (COMPOUND_EXPR, "compound_expr", tcc_expression, 2)
    Line 535: DEFTREECODE (MODIFY_EXPR, "modify_expr", tcc_expression, 2)
    Line 540: DEFTREECODE (INIT_EXPR, "init_expr", tcc_expression, 2)
    Line 548: DEFTREECODE (TARGET_EXPR, "target_expr", tcc_expression, 4)
    Line 562: DEFTREECODE (COND_EXPR, "cond_expr", tcc_expression, 3)
    Line 565: DEFTREECODE (VEC_DUPLICATE_EXPR, "vec_duplicate_expr", tcc_unary, 1)
    Line 575: DEFTREECODE (VEC_SERIES_EXPR, "vec_series_expr", tcc_binary, 2)
    Line 587: DEFTREECODE (VEC_COND_EXPR, "vec_cond_expr", tcc_expression, 3)
    Line 606: DEFTREECODE (VEC_PERM_EXPR, "vec_perm_expr", tcc_expression, 3)
    Line 629: DEFTREECODE (BIND_EXPR, "bind_expr", tcc_expression, 3)
    Line 637: DEFTREECODE (CALL_EXPR, "call_expr", tcc_vl_exp, 3)
    Line 644: DEFTREECODE (WITH_CLEANUP_EXPR, "with_cleanup_expr", tcc_expression, 1)
    Line 661: DEFTREECODE (CLEANUP_POINT_EXPR, "cleanup_point_expr", tcc_expression, 1)
    Line 713: DEFTREECODE (PLACEHOLDER_EXPR, "placeholder_expr", tcc_exceptional, 0)
    Line 716: DEFTREECODE (PLUS_EXPR, "plus_expr", tcc_binary

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如何阅读、学习 Tcc (Tiny C Compiler) 源代码？如何解析 Tcc 源代码？ 阅读和解析 TCC（Tiny C Compiler） 的源代码需要对编译器的基本工作原理和代码结构有一定的了解。 阅读详情

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B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")
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