TensorFlow可视化cifar-10卷积后的特征 tensorflow

本文大概步骤：准备数据→搭建网络→训练测试（并添加tensorboard的IMAGES）
目录
准备数据
下载数据集
反序列化数据
调整数据
定义训练、测试的样本和标签
网络搭建及训练测试
网络结构：
输入层：32*32*3 → 32个5*5的三通道卷积核 → 卷积层1:32*32*32 → 3*3步幅为2的池化 → 池化层1:16*16*32 →64个5*5的64通道卷积核 → 卷积层2:16*16*64 → 3*3步幅为2的池化 → 池化层2:8*8*64 → 全连接层1:384 → 全连接层2:192 → 输出层:10
整体代码
可视化结果
准备数据

下载数据集
反序列化数据
调整数据
定义训练、测试的样本和标签

下载数据集

打开官网http://www.cs.toronto.edu/~kriz/cifar.html，找到相应的python版本的数据集进行下载，如下图：（另外也有别的下载方式，import cifar10 cifar10.maybe_download_and_extract()，此处仅做简单介绍，细节请自行百度or谷歌）

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下载好后的数据如下图所示：其中，训练数据为data_batch_1~5，测试数据为test_batch，每个batch为10000幅图像

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反序列化数据

在官网中给出了相应的反序列化代码：(反序列化后的数据是一个字典，相应的key为：b'filename',b'labels',b'data')

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值得一提的是反序列化后的data中的每一个图像为一行数据，共10000行，每行的前1024个值为相应red通道的图像值，接下来的1024个值为green通道，最后的1024个值为blue通道

调整数据

进过上面的知识铺垫后，我们将相应的数据做一定的调整，代码如下：首先将训练数据和测试数据进行反序列化，然后进行调整(由于每行数据是按红、绿、蓝通道图像进行排列的，reshape为(32,32,3)之后进行显示并不是原图，因此要将其调整为：每三个值为一个像素的红绿蓝通道的值，然后遍历完所有像素。调整之前是：每1024个值为所有像素在一个通道的值，遍历三个通道。这里可能解释的不清楚，烦请自行理解)

def unpickle(file): ''' :param file: 输入文件是cifar-10的python版本文件，一共五个batch，每个batch10000个32×32大小的彩色图像，一次输入一个 :return: 返回的是一个字典，key包括b'filename',b'labels',b'data' ''' import pickle with open(file, 'rb') as fo: dict = pickle.load(fo, encoding='bytes') return dictdef onehot(labels): ''' :param labels: 输入标签b'labels'，大小为10000的列表，每一个元素直接是对应图像的类别，需将其转换成onehot格式 :return: onehot格式的标签，10000行，10列，每行对应一个图像，列表示10个分类，该图像的类所在列为1，其余列为0 ''' #先建立一个全0的，10000行，10列的数组 onehot_labels = np.zeros([len(labels), 10]) # 这里的索引方式比较特殊，第一个索引为[0, 1, 2, ..., len(labels)]， # 第二个索引为[第1个的图像的类, 第2个的图像的类, ..., 最后一个的图像的类] # 即将所有图像的类别所对应的位置改变为1 onehot_labels[np.arange(len(labels)), labels] = 1 return onehot_labels# 反序列化cifar-10训练数据和测试数据 data1 = unpickle(r'.\cifar-10\cifar-10-python\cifar-10-batches-py\data_batch_1') data2 = unpickle(r'.\cifar-10\cifar-10-python\cifar-10-batches-py\data_batch_2') data3 = unpickle(r'.\cifar-10\cifar-10-python\cifar-10-batches-py\data_batch_3') data4 = unpickle(r'.\cifar-10\cifar-10-python\cifar-10-batches-py\data_batch_4') data5 = unpickle(r'.\cifar-10\cifar-10-python\cifar-10-batches-py\data_batch_5') test_data = https://www.it610.com/article/unpickle(r'.\cifar-10\cifar-10-python\cifar-10-batches-py\test_batch')# 调整数据：先将其reshape为(10000, 3, 32, 32)：10000幅图像，3个通道，32行，32列 # 再通过transpose(0, 2, 3, 1)对相应轴进行调换，将其调整为(10000, 32, 32, 3)：10000幅图像，32行，32列，3个通道 # 最后再将(10000, 32, 32, 3)其reshape为1000行，32*32*3列的数据(10000, -1) data1[b'data'] = data1[b'data'].reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).reshape(10000, -1) data2[b'data'] = data2[b'data'].reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).reshape(10000, -1) data3[b'data'] = data3[b'data'].reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).reshape(10000, -1) data4[b'data'] = data4[b'data'].reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).reshape(10000, -1) data5[b'data'] = data5[b'data'].reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).reshape(10000, -1) test_data[b'data'] = test_data[b'data'].reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).reshape(10000, -1)

代码中的transpose可参考https://blog.csdn.net/u012762410/article/details/78912667

定义训练、测试的样本和标签

对训练数据（data1~5）进行合并得到50000行数据，将训练数据和测试数据的标签转换成one-hot格式，代码如下：

# 合并5个batch得到相应的训练数据和标签 x_train = np.concatenate((data1[b'data'], data2[b'data'], data3[b'data'], data4[b'data'], data5[b'data']), axis=0) y_train = np.concatenate((data1[b'labels'], data2[b'labels'], data3[b'labels'], data4[b'labels'], data5[b'labels']), axis=0) y_train = onehot(y_train)# 得到测试数据和测试标签 x_test = test_data[b'data'] y_test = onehot(test_data[b'labels'])

onehot函数中用到了一种独特的索引方式：

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网络搭建及训练测试由于本文的重点在于可视化卷积层特征，因此并没有对超参数进行特别的设置

网络结构：

输入层：32*32*3 → 32个5*5的三通道卷积核 → 卷积层1:32*32*32 → 3*3步幅为2的池化 → 池化层1:16*16*32 →64个5*5的64通道卷积核 → 卷积层2:16*16*64 → 3*3步幅为2的池化 → 池化层2:8*8*64 → 全连接层1:384 → 全连接层2:192 → 输出层:10

# 设置超参数 lr = 1e-4# learning rate epoches = 1# 使用整个数据集训练的次数 batch_size = 500# 每次训练的样本数 n_batch = 20# x_train.shape[0]//batch_size n_features = 32*32*3 n_classes = 10 n_fc1 = 384 n_fc2 = 192# 模型输入 x = tf.placeholder(tf.float32, [None, n_features]) y = tf.placeholder(tf.float32, [None, n_classes])# 设置各层权重weight = { 'conv1': tf.Variable(tf.truncated_normal([5, 5, 3, 32], stddev=0.05)), 'conv2': tf.Variable(tf.truncated_normal([5, 5, 32, 64], stddev=0.05)), 'fc1': tf.Variable(tf.truncated_normal([8*8*64, n_fc1], stddev=0.04)), 'fc2': tf.Variable(tf.truncated_normal([n_fc1, n_fc2], stddev=0.04)), 'fc3': tf.Variable(tf.truncated_normal([n_fc2, n_classes], stddev=1/192.0)) }# 设置各层偏置bias = { 'conv1': tf.Variable(tf.constant(0., shape=[32])), 'conv2': tf.Variable(tf.constant(0., shape=[64])), 'fc1': tf.Variable(tf.constant(0., shape=[n_fc1])), 'fc2': tf.Variable(tf.constant(0., shape=[n_fc2])), 'fc3': tf.Variable(tf.constant(0., shape=[n_classes])) }x_input = tf.reshape(x, [-1, 32, 32, 3])# 可视化输入图像 tf.summary.image('input', x_input, max_outputs=4)conv1 = tf.nn.conv2d(x_input, weight['conv1'], [1, 1, 1, 1], padding='SAME') conv1 = tf.nn.bias_add(conv1, bias['conv1']) conv1 = tf.nn.relu(conv1)img_conv1 = conv1[:, :, :, 0:1]# 可视化第一层卷积层特征 tf.summary.image('conv1', img_conv1, max_outputs=4)pool1 = tf.nn.avg_pool(conv1, [1, 3, 3, 1], [1, 2, 2, 1], padding='SAME')#norm1 = tf.nn.lrn(pool1, depth_radius=4.0, bias=1.0, alpha=0.001/9.0, beta=0.75)conv2 = tf.nn.conv2d(pool1, weight['conv2'], [1, 1, 1, 1], padding='SAME') conv2 = tf.nn.bias_add(conv2, bias['conv2']) conv2 = tf.nn.relu(conv2)img_conv2 = conv2[:, :, :, 0:1]# 可视化第二层卷积层特征 tf.summary.image('conv2', img_conv2, max_outputs=4)#norm2 = tf.nn.lrn(conv2, depth_radius=4.0, bias=1.0, alpha=0.001/9.0, beta=0.75)pool2 = tf.nn.avg_pool(conv2, [1, 3, 3, 1], [1, 2, 2, 1], padding='SAME')reshape = tf.reshape(pool2, [-1, 8*8*64])fc1 = tf.matmul(reshape, weight['fc1']) fc1 = tf.nn.bias_add(fc1, bias['fc1']) fc1 = tf.nn.relu(fc1)fc2 = tf.matmul(fc1, weight['fc2']) fc2 = tf.nn.bias_add(fc2, bias['fc2']) fc2 = tf.nn.relu(fc2)fc3 = tf.matmul(fc2, weight['fc3']) fc3 = tf.nn.bias_add(fc3, bias['fc3']) prediction = tf.nn.softmax(fc3)loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))train_step = tf.train.AdamOptimizer(lr).minimize(loss)correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))with tf.Session() as sess: sess.run(tf.global_variables_initializer()) writer = tf.summary.FileWriter(r'.\cifar-10\logs', sess.graph) merged = tf.summary.merge_all() for step in range(epoches): for batch in range(n_batch): # print(batch*batch_size, (batch + 1)*batch_size) xs = x_train[batch*batch_size: (batch + 1)*batch_size, :] ys = y_train[batch*batch_size: (batch + 1)*batch_size, :] summary, _ = sess.run([merged, train_step], feed_dict={x: xs, y: ys}) writer.add_summary(summary, batch) print(batch) if step % 20 == 0: acc = sess.run(accuracy, feed_dict={x: x_test[:2000, :], y: y_test[:2000, :]}) print(step, acc)

代码中值得注意的是：

tf.summary.image的参数中，通道数必须是1,3,4，而卷积层输出的特征通道数分别为32,64。因此，在可视化的时候只取了第0个特征，即代码：

img_conv1 = conv1[:, :, :, 0:1] 和 img_conv2 = conv2[:, :, :, 0:1]

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整体代码代码主要参考于：TensorFlow深度学习应用实践-王晓华

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import time import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'def unpickle(file): ''' :param file: 输入文件是cifar-10的python版本文件，一共五个batch，每个batch10000个32×32大小的彩色图像，一次输入一个 :return: 返回的是一个字典，key包括b'filename',b'labels',b'data' ''' import pickle with open(file, 'rb') as fo: dict = pickle.load(fo, encoding='bytes') return dictdef onehot(labels): ''' :param labels: 输入标签b'labels'，大小为10000的列表，每一个元素直接是对应图像的类别，需将其转换成onehot格式 :return: onehot格式的标签，10000行，10列，每行对应一个图像，列表示10个分类，该图像的类所在列为1，其余列为0 ''' #先建立一个全0的，10000行，10列的数组 onehot_labels = np.zeros([len(labels), 10]) # 这里的索引方式比较特殊，第一个索引为[0, 1, 2, ..., len(labels)]， # 第二个索引为[第1个的图像的类, 第2个的图像的类, ..., 最后一个的图像的类] # 即将所有图像的类别所对应的位置改变为1 onehot_labels[np.arange(len(labels)), labels] = 1 return onehot_labels# 反序列化cifar-10训练数据和测试数据 data1 = unpickle(r'.\cifar-10\cifar-10-python\cifar-10-batches-py\data_batch_1') data2 = unpickle(r'.\cifar-10\cifar-10-python\cifar-10-batches-py\data_batch_2') data3 = unpickle(r'.\cifar-10\cifar-10-python\cifar-10-batches-py\data_batch_3') data4 = unpickle(r'.\cifar-10\cifar-10-python\cifar-10-batches-py\data_batch_4') data5 = unpickle(r'.\cifar-10\cifar-10-python\cifar-10-batches-py\data_batch_5') test_data = https://www.it610.com/article/unpickle(r'.\cifar-10\cifar-10-python\cifar-10-batches-py\test_batch')# 调整数据：先将其reshape为(10000, 3, 32, 32)：10000幅图像，3个通道，32行，32列 # 再通过transpose(0, 2, 3, 1)对相应轴进行调换，将其调整为(10000, 32, 32, 3)：10000幅图像，32行，32列，3个通道 # 最后再将(10000, 32, 32, 3)其reshape为1000行，32*32*3列的数据(10000, -1) data1[b'data'] = data1[b'data'].reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).reshape(10000, -1) data2[b'data'] = data2[b'data'].reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).reshape(10000, -1) data3[b'data'] = data3[b'data'].reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).reshape(10000, -1) data4[b'data'] = data4[b'data'].reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).reshape(10000, -1) data5[b'data'] = data5[b'data'].reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).reshape(10000, -1) test_data[b'data'] = test_data[b'data'].reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).reshape(10000, -1)# 合并5个batch得到相应的训练数据和标签 x_train = np.concatenate((data1[b'data'], data2[b'data'], data3[b'data'], data4[b'data'], data5[b'data']), axis=0) y_train = np.concatenate((data1[b'labels'], data2[b'labels'], data3[b'labels'], data4[b'labels'], data5[b'labels']), axis=0) y_train = onehot(y_train)# 得到测试数据和测试标签 x_test = test_data[b'data'] y_test = onehot(test_data[b'labels'])# 打印相关数据的shape print('Training data shape:', x_train.shape) print('Training labels shape:', y_train.shape) print('Testing data shape:', x_test.shape) print('Testing labels shape:', y_test.shape)# 设置超参数 lr = 1e-4# learning rate epoches = 1# 使用整个数据集训练的次数 batch_size = 500# 每次训练的样本数 n_batch = 20# x_train.shape[0]//batch_size n_features = 32*32*3 n_classes = 10 n_fc1 = 384 n_fc2 = 192# 模型输入 x = tf.placeholder(tf.float32, [None, n_features]) y = tf.placeholder(tf.float32, [None, n_classes])# 设置各层权重weight = { 'conv1': tf.Variable(tf.truncated_normal([5, 5, 3, 32], stddev=0.05)), 'conv2': tf.Variable(tf.truncated_normal([5, 5, 32, 64], stddev=0.05)), 'fc1': tf.Variable(tf.truncated_normal([8*8*64, n_fc1], stddev=0.04)), 'fc2': tf.Variable(tf.truncated_normal([n_fc1, n_fc2], stddev=0.04)), 'fc3': tf.Variable(tf.truncated_normal([n_fc2, n_classes], stddev=1/192.0)) }# 设置各层偏置bias = { 'conv1': tf.Variable(tf.constant(0., shape=[32])), 'conv2': tf.Variable(tf.constant(0., shape=[64])), 'fc1': tf.Variable(tf.constant(0., shape=[n_fc1])), 'fc2': tf.Variable(tf.constant(0., shape=[n_fc2])), 'fc3': tf.Variable(tf.constant(0., shape=[n_classes])) }x_input = tf.reshape(x, [-1, 32, 32, 3])# 可视化输入图像 tf.summary.image('input', x_input, max_outputs=4)conv1 = tf.nn.conv2d(x_input, weight['conv1'], [1, 1, 1, 1], padding='SAME') conv1 = tf.nn.bias_add(conv1, bias['conv1']) conv1 = tf.nn.relu(conv1)img_conv1 = conv1[:, :, :, 0:1]# 可视化第一层卷积层特征 tf.summary.image('conv1', img_conv1, max_outputs=4)pool1 = tf.nn.avg_pool(conv1, [1, 3, 3, 1], [1, 2, 2, 1], padding='SAME')#norm1 = tf.nn.lrn(pool1, depth_radius=4.0, bias=1.0, alpha=0.001/9.0, beta=0.75)conv2 = tf.nn.conv2d(pool1, weight['conv2'], [1, 1, 1, 1], padding='SAME') conv2 = tf.nn.bias_add(conv2, bias['conv2']) conv2 = tf.nn.relu(conv2)img_conv2 = conv2[:, :, :, 0:1]# 可视化第二层卷积层特征 tf.summary.image('conv2', img_conv2, max_outputs=4)#norm2 = tf.nn.lrn(conv2, depth_radius=4.0, bias=1.0, alpha=0.001/9.0, beta=0.75)pool2 = tf.nn.avg_pool(conv2, [1, 3, 3, 1], [1, 2, 2, 1], padding='SAME')reshape = tf.reshape(pool2, [-1, 8*8*64])fc1 = tf.matmul(reshape, weight['fc1']) fc1 = tf.nn.bias_add(fc1, bias['fc1']) fc1 = tf.nn.relu(fc1)fc2 = tf.matmul(fc1, weight['fc2']) fc2 = tf.nn.bias_add(fc2, bias['fc2']) fc2 = tf.nn.relu(fc2)fc3 = tf.matmul(fc2, weight['fc3']) fc3 = tf.nn.bias_add(fc3, bias['fc3']) prediction = tf.nn.softmax(fc3)loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))train_step = tf.train.AdamOptimizer(lr).minimize(loss)correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))with tf.Session() as sess: sess.run(tf.global_variables_initializer()) writer = tf.summary.FileWriter(r'.\cifar-10\logs', sess.graph) merged = tf.summary.merge_all() for step in range(epoches): for batch in range(n_batch): # print(batch*batch_size, (batch + 1)*batch_size) xs = x_train[batch*batch_size: (batch + 1)*batch_size, :] ys = y_train[batch*batch_size: (batch + 1)*batch_size, :] summary, _ = sess.run([merged, train_step], feed_dict={x: xs, y: ys}) writer.add_summary(summary, batch) print(batch) if step % 20 == 0: acc = sess.run(accuracy, feed_dict={x: x_test[:2000, :], y: y_test[:2000, :]}) print(step, acc)

可视化结果 【TensorFlow可视化cifar-10卷积后的特征】