最近看到tensorflow训练cifar10数据集，说实话相比于mnist数据集，cifar10有了一个质的飞跃，从单通道灰度图像转变到三通道彩色图像。
cifar10
下面来简单介绍下cifar10数据集，该数据集共有60000张彩色图像，这些图像是32*32*3，分为10个类，每类6000张图。这里面有50000张用于训练，构成了5个训练批，每一批10000张图；另外10000用于测试，单独构成一批。测试批的数据里，取自10类中的每一类，每一类随机取1000张。抽剩下的就随机排列组成了训练批。注意一个训练批中的各类图像并不一定数量相同，总的来看训练批，每一类都有5000张图。Tensorflow自带有cifar的例子，可以在线下载cifar数据集，也可以离线下载，然后读取数据，在这里主要讲解如何搭建训练工程。下面请看代码：
import cifar10,cifar10_input
import tensorflow as tf
import numpy as np
import time
max_steps = 3000
batch_size = 128
data_dir = 'C:\\Users\\new\\Desktop\\cifar-10-batches-bin'
def variable_with_weight_loss(shape, stddev, wl):
var = tf.Variable(tf.truncated_normal(shape, stddev=stddev))
if wl is not None:
weight_loss = tf.multiply(tf.nn.l2_loss(var), wl, name='weight_loss')
tf.add_to_collection('losses', weight_loss)
return var
def loss(logits, labels):
# """Add L2Loss to all the trainable variables.
# Add summary for "Loss" and "Loss/avg".
# Args:
# logits: Logits from inference().
# labels: Labels from distorted_inputs or inputs(). 1-D tensor
# of shape [batch_size]
# Returns:
# Loss tensor of type float.
# """
# # Calculate the average cross entropy loss across the batch.
#将labels数据格式转换为int64
labels = tf.cast(labels, tf.int64)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=labels, name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
tf.add_to_collection('losses', cross_entropy_mean)
# The total loss is defined as the cross entropy loss plus all of the weight
# decay terms (L2 loss).
return tf.add_n(tf.get_collection('losses'), name='total_loss')
###
images_train, labels_train = cifar10_input.distorted_inputs(data_dir=data_dir,
batch_size=batch_size)
images_test, labels_test = cifar10_input.inputs(eval_data=True,
data_dir=data_dir,
batch_size=batch_size)
#images_train, labels_train = cifar10.distorted_inputs()
#images_test, labels_test = cifar10.inputs(eval_data=True)
image_holder = tf.placeholder(tf.float32, [batch_size, 24, 24, 3])
label_holder = tf.placeholder(tf.int32, [batch_size])
#logits = inference(image_holder)
weight1 = variable_with_weight_loss(shape=[5, 5, 3, 64], stddev=5e-2, wl=0.0)
kernel1 = tf.nn.conv2d(image_holder, weight1, [1, 1, 1, 1], padding='SAME')
bias1 = tf.Variable(tf.constant(0.0, shape=[64]))
#将w*x和b加起来
conv1 = tf.nn.relu(tf.nn.bias_add(kernel1, bias1))
pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
padding='SAME')
#LRN为局部响应归一化，一般在激活或者池化后使用，让强信号更强，弱信号更弱，通常很少使用，被dropout等方法替代
norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
weight2 = variable_with_weight_loss(shape=[5, 5, 64, 64], stddev=5e-2, wl=0.0)
kernel2 = tf.nn.conv2d(norm1, weight2, [1, 1, 1, 1], padding='SAME')
bias2 = tf.Variable(tf.constant(0.1, shape=[64]))
conv2 = tf.nn.relu(tf.nn.bias_add(kernel2, bias2))
norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
padding='SAME')
#在这里通过reshape函数把结构化数据转变成向量数据格式，这一步就是把卷积层转换为全连接层
reshape = tf.reshape(pool2, [batch_size, -1])#这里是因为数据是以batch_size个存储的，不是单个，其实就是batch_size*单个数据
dim = reshape.get_shape()[1].value
weight3 = variable_with_weight_loss(shape=[dim, 384], stddev=0.04, wl=0.004)
bias3 = tf.Variable(tf.constant(0.1, shape=[384]))
local3 = tf.nn.relu(tf.matmul(reshape, weight3) + bias3)
weight4 = variable_with_weight_loss(shape=[384, 192], stddev=0.04, wl=0.004)
bias4 = tf.Variable(tf.constant(0.1, shape=[192]))
local4 = tf.nn.relu(tf.matmul(local3, weight4) + bias4)
weight5 = variable_with_weight_loss(shape=[192, 10], stddev=1/192.0, wl=0.0)
bias5 = tf.Variable(tf.constant(0.0, shape=[10]))
logits = tf.add(tf.matmul(local4, weight5), bias5)
#总的损失函数
loss = loss(logits, label_holder)
train_op = tf.train.AdamOptimizer(1e-3).minimize(loss) #0.72
top_k_op = tf.nn.in_top_k(logits, label_holder, 1)
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
tf.train.start_queue_runners()
###
for step in range(max_steps):
start_time = time.time()
image_batch,label_batch = sess.run([images_train,labels_train])
_, loss_value = sess.run([train_op, loss],feed_dict={image_holder: image_batch,
label_holder:label_batch})
duration = time.time() - start_time
if step % 10 == 0:
examples_per_sec = batch_size / duration
sec_per_batch = float(duration)
format_str = ('step %d, loss = %.2f (%.1f examples/sec; %.3f sec/batch)')
print(format_str % (step, loss_value, examples_per_sec, sec_per_batch))
###
num_examples = 10000
import math
num_iter = int(math.ceil(num_examples / batch_size))
true_count = 0
total_sample_count = num_iter * batch_size
step = 0
while step < num_iter:
image_batch,label_batch = sess.run([images_test,labels_test])
predictions = sess.run([top_k_op],feed_dict={image_holder: image_batch,
label_holder:label_batch})
true_count += np.sum(predictions)
step += 1
precision = true_count / total_sample_count
print('precision @ 1 = %.3f' % precision)