基于tensorflow的3D CNN代码实现

原文地址：http://blog.csdn.net/sinat_31824577/article/details/60325571

结合Udacity 上的 deep learning 公开课 https://cn.udacity.com/course/deep-learning–ud730 。
3D 卷积神经网络 相比于2D, 多一维仅此而已。原理上与2D 上几乎差不多，但是直接将2D 的网络拿过来直接使用，还是会遇到各种各样的问题，比如说有些库不支持 3D 的卷积运算，caffe就似乎不支持，theano 中没有maxpooling3D , 所以需要自己补充相关的运算。Tensorflow 都很全，在其下搭建3D CNN 很方便。

1. 2D CNN

如下图所示，为经典的lenet-5 模型:conv-pool-conv-pool-conv -fullconnect-softmax，所有的卷积核大小都是5*5，将低层次的像素变化通过卷积来学习层层特征，最后转变成一个84维的向量，最后经过多类回归分析（softmax层），输出类别预测。详细介绍可参考http://blog.csdn.net/xuanyuansen/article/details/41800721

2.3D CNN

原理上即参照2D CNN 把各个变量增加一维。

3. Tensorflow 上实现

其中，confusionMatrix 表示用于计算结果的类，可按批处理。
a.3D_cnn.py

#!/usr/bin/env python2# -*- coding: utf-8 -*-"""Created on Thu Feb 23 10:51:28 2017@author: cdn"""import numpy as npnp.random.seed(1234)import timeitimport osimport matplotlib.pyplot as pltfrom sklearn.cross_validation import StratifiedKFoldimport tensorflow as tffrom tensorflow.contrib.layers import fully_connected, convolution2d, flatten, dropoutfrom tensorflow.python.layers.pooling import max_pooling3dfrom tensorflow.python.ops.nn import relu,softmaxfrom tensorflow.python.framework.ops import reset_default_graphfrom ConfusionMatrix import ConfusionMatrixdef onehot(t, num_classes):    out = np.zeros((t.shape[0], num_classes))    for row, col in enumerate(t):        out[row, col] = 1    return outdef load_data(fold_index):    np.random.seed(1234)    A_data = np.random.uniform(-0.5,1.5, (500, 65, 52, 51)).astype('float32')       B_data = np.random.uniform(0,1, (500, 65, 52, 51)).astype('float32')  # load two classes for classfication    A_num,sizeX,sizeY,sizeZ = A_data.shape    B_num,_,_,_ = B_data.shape    size_input = [1,sizeX,sizeY,sizeZ]    np.random.seed(1234)    random_idx = np.random.permutation(A_num+B_num)    all_data = np.concatenate((A_data,B_data),axis=0)[random_idx]    labels = np.hstack((np.ones((A_num,)),np.zeros((B_num,))))[random_idx]    nn =5    skf = StratifiedKFold(labels,nn)    train_id = ['']*nn    test_id = ['']*nn    a = 0    for train,test in skf:        train_id[a] = train        test_id[a] = test        a = a+1    testid = test_id[fold_index]    validid = test_id[fold_index-1]    trainid = list(set(train_id[fold_index])-set(validid))    x_train = all_data[trainid]    y_train = labels[trainid]    x_test = all_data[testid]    y_test = labels[testid]    x_valid = all_data[validid]    y_valid = labels[validid]     return x_train,y_train,x_test,y_test,x_valid,y_valid,size_inputn_fold = 5train_accuracy = np.zeros((n_fold,))    test_accuracy = np.zeros((n_fold,))valid_accuracy = np.zeros((n_fold,))  t1_time = timeit.default_timer()#for fi in range(n_fold):num_classes = 2num_filters_conv1 = 10num_filters_conv2 = 25num_filters_conv3 = 40num_filters_conv4 = 40dense_num = 100size_conv = 3 # [height, width]pool_size = 2batch_size = 5nb_epoch = 50fi = 0X_train,y_train,X_test,y_test,X_val,y_val,size_input = load_data(fi)X_train = X_train.reshape(X_train.shape[0], 1, X_train.shape[1], X_train.shape[2],X_train.shape[3])  X_val = X_val.reshape(X_val.shape[0], 1, X_val.shape[1],X_val.shape[2],X_val.shape[3])  X_test = X_test.reshape(X_test.shape[0], 1, X_test.shape[1], X_test.shape[2],X_test.shape[3])  print('X_train shape:', X_train.shape)  print(X_train.shape[0], 'train samples')  print(X_val.shape[0], 'validate samples')  print(X_test.shape[0], 'test samples')  train_accuracy = np.zeros((n_fold,))    test_accuracy = np.zeros((n_fold,))valid_accuracy = np.zeros((n_fold,))  t1_time = timeit.default_timer()for fi in range(n_fold):      print('Now running on fold %d'%(fi+1))    num_classes = 2    x_train,y_train,x_test,y_test,x_valid,y_valid,size_input = load_data(fi)    nchannels,rows,cols,deps = size_input    x_train = x_train.astype('float32')    x_train = x_train.reshape((-1,nchannels,rows,cols,deps))    targets_train = y_train.astype('int32')    x_valid = x_valid.astype('float32')    x_valid = x_valid.reshape((-1,nchannels,rows,cols,deps))    targets_valid = y_valid.astype('int32')    x_test = x_test.astype('float32')    x_test = x_test.reshape((-1,nchannels,rows,cols,deps))    targets_test = y_test.astype('int32')    # define a simple feed forward neural network    # hyperameters of the model    num_classes = 2    channels = x_train.shape[1]    height = x_train.shape[2]    width = x_train.shape[3]    depth = x_train.shape[4]    num_filters_conv1 = 10    num_filters_conv2 = 25    num_filters_conv3 = 40    num_filters_conv4 = 40    kernel_size_conv1 = [3, 3, 3] # [height, width]    pool_size = [2,2,2]    stride_conv1 = [1,1,1] # [stride_height, stride_width]    num_l1 = 100    # resetting the graph ...    reset_default_graph()    # Setting up placeholder, this is where your data enters the graph!    x_pl = tf.placeholder(tf.float32, [None, channels, height, width, depth])    l_reshape = tf.transpose(x_pl, [0, 2, 3, 4, 1]) # TensorFlow uses NHWC instead of NCHW    is_training = tf.placeholder(tf.bool)#used for dropout    # Building the layers of the neural network    # we define the variable scope, so we more easily can recognise our variables later    l_conv1 = convolution2d(l_reshape, num_filters_conv1, kernel_size_conv1, stride_conv1,activation_fn=relu, scope="l_conv1")    l_maxpool1 = max_pooling3d(l_conv1,pool_size,pool_size)    l_conv2 = convolution2d(l_maxpool1, num_filters_conv2, kernel_size_conv1, stride_conv1,activation_fn=relu,scope="l_conv2")    l_maxpool2 = max_pooling3d(l_conv2,pool_size,pool_size)    l_conv3 = convolution2d(l_maxpool2, num_filters_conv3, kernel_size_conv1, stride_conv1,activation_fn=relu,scope="l_conv3")    l_maxpool3 = max_pooling3d(l_conv3,pool_size,pool_size)    l_conv4 = convolution2d(l_maxpool3, num_filters_conv4, kernel_size_conv1, stride_conv1,activation_fn=relu,scope="l_conv4")    l_flatten = flatten(l_conv4, scope="flatten") # use l_conv1 instead of l_reshape    l1 = fully_connected(l_flatten, num_l1, activation_fn=relu, scope="l1")    l1 = dropout(l1, is_training=is_training, scope="dropout")    y = fully_connected(l1, num_classes, activation_fn=softmax, scope="y")    # y_ is a placeholder variable taking on the value of the target batch.    y_ = tf.placeholder(tf.float32, [None, num_classes])    # computing cross entropy per sample    cross_entropy = -tf.reduce_sum(y_ * tf.log(y+1e-8), reduction_indices=[1])    # averaging over samples    cross_entropy = tf.reduce_mean(cross_entropy)    # defining our optimizer    optimizer = tf.train.AdamOptimizer(learning_rate=0.001)    # applying the gradients    train_op = optimizer.minimize(cross_entropy)    #Test the forward pass#    x = np.random.normal(0,1, (45, 1,65, 52, 51)).astype('float32') #dummy data    # restricting memory usage, TensorFlow is greedy and will use all memory otherwise    gpu_opts = tf.GPUOptions(per_process_gpu_memory_fraction=0.2)    # initialize the Session    sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_opts))    sess.run(tf.global_variables_initializer())#    res = sess.run(fetches=[y], feed_dict={x_pl: x})#    res = sess.run(fetches=[y], feed_dict={x_pl: x, is_training: False}) # for when using dropout#    print "y", res[0].shape    #Training Loop    from confusionmatrix import ConfusionMatrix    batch_size = 10    num_epochs = 50    num_samples_train = x_train.shape[0]    num_batches_train = num_samples_train // batch_size    num_samples_valid = x_valid.shape[0]    num_batches_valid = num_samples_valid // batch_size    train_acc, train_loss = [], []    valid_acc, valid_loss = [], []    test_acc, test_loss = [], []    cur_loss = 0    loss = []    try:        for epoch in range(num_epochs):            #Forward->Backprob->Update params            cur_loss = 0            for i in range(num_batches_train):                idx = range(i*batch_size, (i+1)*batch_size)                x_batch = x_train[idx]                target_batch = targets_train[idx]#                feed_dict_train = {x_pl: x_batch, y_: onehot(target_batch, num_classes)}                feed_dict_train = {x_pl: x_batch, y_: onehot(target_batch, num_classes), is_training: True}                fetches_train = [train_op, cross_entropy]                res = sess.run(fetches=fetches_train, feed_dict=feed_dict_train)                batch_loss = res[1] #this will do the complete backprob pass                cur_loss += batch_loss            loss += [cur_loss/batch_size]            confusion_valid = ConfusionMatrix(num_classes)            confusion_train = ConfusionMatrix(num_classes)            for i in range(num_batches_train):                idx = range(i*batch_size, (i+1)*batch_size)                x_batch = x_train[idx]                targets_batch = targets_train[idx]                # what to feed our accuracy op#                feed_dict_eval_train = {x_pl: x_batch}                feed_dict_eval_train = {x_pl: x_batch, is_training: False}                # deciding which parts to fetch                fetches_eval_train = [y]                # running the validation                res = sess.run(fetches=fetches_eval_train, feed_dict=feed_dict_eval_train)                # collecting and storing predictions                net_out = res[0]                 preds = np.argmax(net_out, axis=-1)                 confusion_train.batch_add(targets_batch, preds)            confusion_valid = ConfusionMatrix(num_classes)            for i in range(num_batches_valid):                idx = range(i*batch_size, (i+1)*batch_size)                x_batch = x_valid[idx]                targets_batch = targets_valid[idx]                # what to feed our accuracy op#                feed_dict_eval_train = {x_pl: x_batch}                feed_dict_eval_train = {x_pl: x_batch, is_training: False}                # deciding which parts to fetch                fetches_eval_train = [y]                # running the validation                res = sess.run(fetches=fetches_eval_train, feed_dict=feed_dict_eval_train)                # collecting and storing predictions                net_out = res[0]                preds = np.argmax(net_out, axis=-1)                 confusion_valid.batch_add(targets_batch, preds)            train_acc_cur = confusion_train.accuracy()            valid_acc_cur = confusion_valid.accuracy()            train_acc += [train_acc_cur]            valid_acc += [valid_acc_cur]            print "Epoch %i : Train Loss %e , Train acc %f,  Valid acc %f " \            % (epoch+1, loss[-1], train_acc_cur, valid_acc_cur)    except KeyboardInterrupt:        pass    #get test set score    confusion_test = ConfusionMatrix(num_classes)    # what to feed our accuracy op#    feed_dict_eval_train = {x_pl: x_test}    feed_dict_eval_train = {x_pl: x_test, is_training: False}    # deciding which parts to fetch    fetches_eval_train = [y]    # running the validation    res = sess.run(fetches=fetches_eval_train, feed_dict=feed_dict_eval_train)    # collecting and storing predictions    net_out = res[0]     preds = np.argmax(net_out, axis=-1)     confusion_test.batch_add(targets_test, preds)    print "\nTest set Acc:  %f" %(confusion_test.accuracy())    test_acc = confusion_test.accuracy()    epoch = np.arange(len(train_acc))    plt.figure()    plt.plot(epoch,train_acc,'r',epoch,valid_acc,'b')    plt.legend(['Train Acc','Val Acc'])    plt.xlabel('Epochs'), plt.ylabel('Acc'), plt.ylim([0.2,1.03])    plt.show()    train_accuracy[fi] = train_acc[-1]    test_accuracy[fi] = test_acc    valid_accuracy[fi] = valid_acc[-1]print '\nMean accuray of test set: %f %%' %(np.mean(test_accuracy)*100)t2_time = timeit.default_timer()print(('The code for Tensorflow ' +os.path.split(__file__)[1] +' ran for %.2fm' % ((t2_time - t1_time) / 60.))) 
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b. ConfusionMatrix.py

import numpy as npclass ConfusionMatrix:    """       Simple confusion matrix class       row is the true class, column is the predicted class    """    def __init__(self, num_classes, class_names=None):        self.n_classes = num_classes        if class_names is None:            self.class_names = map(str, range(num_classes))        else:            self.class_names = class_names        # find max class_name and pad        max_len = max(map(len, self.class_names))        self.max_len = max_len        for idx, name in enumerate(self.class_names):            if len(self.class_names) < max_len:                self.class_names[idx] = name + " "*(max_len-len(name))        self.mat = np.zeros((num_classes,num_classes),dtype='int')    def __str__(self):        # calucate row and column sums        col_sum = np.sum(self.mat, axis=1)        row_sum = np.sum(self.mat, axis=0)        s = []        mat_str = self.mat.__str__()        mat_str = mat_str.replace('[','').replace(']','').split('\n')        for idx, row in enumerate(mat_str):            if idx == 0:                pad = " "            else:                pad = ""            class_name = self.class_names[idx]            class_name = " " + class_name + " |"            row_str = class_name + pad + row            row_str += " |" + str(col_sum[idx])            s.append(row_str)        row_sum = [(self.max_len+4)*" "+" ".join(map(str, row_sum))]        hline = [(1+self.max_len)*" "+"-"*len(row_sum[0])]        s = hline + s + hline + row_sum        # add linebreaks        s_out = [line+'\n' for line in s]        return "".join(s_out)    def batch_add(self, targets, preds):        assert targets.shape == preds.shape        assert len(targets) == len(preds)        assert max(targets) < self.n_classes        assert max(preds) < self.n_classes        targets = targets.flatten()        preds = preds.flatten()        for i in range(len(targets)):                self.mat[targets[i], preds[i]] += 1    def get_errors(self):        tp = np.asarray(np.diag(self.mat).flatten(),dtype='float')        fn = np.asarray(np.sum(self.mat, axis=1).flatten(),dtype='float') - tp        fp = np.asarray(np.sum(self.mat, axis=0).flatten(),dtype='float') - tp        tn = np.asarray(np.sum(self.mat)*np.ones(self.n_classes).flatten(),                        dtype='float') - tp - fn - fp        return tp, fn, fp, tn    def accuracy(self):        """        Calculates global accuracy        :return: accuracy        :example: >>> conf = ConfusionMatrix(3)                  >>> conf.batchAdd([0,0,1],[0,0,2])                  >>> print conf.accuracy()        """        tp, _, _, _ = self.get_errors()        n_samples = np.sum(self.mat)        return np.sum(tp) / n_samples    def sensitivity(self):        tp, tn, fp, fn = self.get_errors()        res = tp / (tp + fn)        res = res[~np.isnan(res)]        return res    def specificity(self):        tp, tn, fp, fn = self.get_errors()        res = tn / (tn + fp)        res = res[~np.isnan(res)]        return res    def positive_predictive_value(self):        tp, tn, fp, fn = self.get_errors()        res = tp / (tp + fp)        res = res[~np.isnan(res)]        return res    def negative_predictive_value(self):        tp, tn, fp, fn = self.get_errors()        res = tn / (tn + fn)        res = res[~np.isnan(res)]        return res    def false_positive_rate(self):        tp, tn, fp, fn = self.get_errors()        res = fp / (fp + tn)        res = res[~np.isnan(res)]        return res    def false_discovery_rate(self):        tp, tn, fp, fn = self.get_errors()        res = fp / (tp + fp)        res = res[~np.isnan(res)]        return res    def F1(self):        tp, tn, fp, fn = self.get_errors()        res = (2*tp) / (2*tp + fp + fn)        res = res[~np.isnan(res)]        return res    def matthews_correlation(self):        tp, tn, fp, fn = self.get_errors()        numerator = tp*tn - fp*fn        denominator = np.sqrt((tp + fp)*(tp + fn)*(tn + fp)*(tn + fn))        res = numerator / denominator        res = res[~np.isnan(res)]        return res