数据的下载：
（共有三个版本：python,matlab,binary version 适用于C语言）
http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
http://www.cs.toronto.edu/~kriz/cifar-10-matlab.tar.gz
http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz
import os
import _pickle as cPickle #python3
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
#dataset dir
CIFAR_DIR = "./cifa-10-batches-py"
def load_data(filename):
'''read data from data file'''
with open(filename,'rb') as f:
data1 = cPickle.load(f,encoding='bytes')
return data1[b'data'],data1[b'labels']
class CifarData:
def __init__(self,filenames,need_shuffle):
all_data = []
all_labels = []
for filename in filenames:
data,labels = load_data(filename)
for item,label in zip(data,labels):
if label in [0,1]:
all_data.append(item)
all_labels.append(label)
self._data = np.vstack(all_data)
self._data = self._data/127.5-1
self._labels = np.hstack(all_labels)
print("============================")
print(self._data.shape)
print(self._labels.shape)
self._num_examples = self._data.shape[0]
self._need_shuffle = need_shuffle
self._indicator = 0
if self._need_shuffle:
self._shuffle_data()
def _shuffle_data(self):
#[0,1,2,3,4,5]->[5,3,2,4,0,1]
p = np.random.permutation(self._num_examples)
self._data = self._data[p]
self._labels = self._labels[p]
def next_batch(self,batch_size):
"""return batch_size examples as a batch."""
end_indicator = self._indicator + batch_size
if end_indicator>self._num_examples:
if self._need_shuffle:
self._shuffle_data()
self._indicator = 0
end_indicator = batch_size
else:
raise Exception("have no more examples...")
if batch_size > self._num_examples:
raise Exception("batch size is larger than all examples")
batch_data = self._data[self._indicator:end_indicator]
batch_labels = self._labels[self._indicator:end_indicator]
self._indicator = end_indicator
return batch_data,batch_labels
x = tf.placeholder(tf.float32,[None,3072])
#x = tf.placeholder(tf.float32,[None,32,32,3])
#[None]
y = tf.placeholder(tf.int64,[None])
#y = tf.placeholder(tf.int64,[10])
#(3071 ,1)
w = tf.get_variable('w',[x.get_shape()[-1],1],
initializer = tf.random_normal_initializer(0,1))
#(1,)
b = tf.get_variable('b',[1],
initializer = tf.constant_initializer(0.0))
#[None,3072]*[3072,1] = [None,1]
y_ = tf.matmul(x,w)+b
#[None,1]
p_y_1 = tf.nn.sigmoid(y_)
#[None,1]
y_reshaped = tf.reshape(y,(-1,1))
y_reshaped_float = tf.cast(y_reshaped,tf.float32)
loss = tf.reduce_mean(tf.square(y_reshaped_float-p_y_1))
#bool
predict = p_y_1>0.5
#[1,0,1,0,0,0,1,1,1,1,1,1,1,0,0,0,0,0,0]
correct_prediction = tf.equal(tf.cast(predict,tf.int64),y_reshaped)
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float64))
with tf.name_scope('train_op'):
train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)
train_filenames = [os.path.join(CIFAR_DIR,'data_batch_%d' % i) for i in range(1,6)]
test_filenames = [os.path.join(CIFAR_DIR,'test_batch')]
train_data = CifarData(train_filenames,True)
test_data = CifarData(test_filenames,False)
#batch_data,batch_labels = train_data.next_batch(10)
#print("-----------------------------------------------------")
#print(batch_data)
#print(batch_labels)
init = tf.global_variables_initializer()
batch_size = 20
train_steps = 100000
test_steps = 100
with tf.Session() as sess:
sess.run(init)
for i in range(train_steps):
batch_data1,batch_labels1 = train_data.next_batch(batch_size)
#batch_data,batch_labels = sess.run([])
#print("-------------------1-------------------")
#print(batch_data1)
#print(batch_labels1)
#print("-------------------2-------------------")
loss_val,acc_val,_ = sess.run(
[loss,accuracy,train_op],
#[train_op,loss],
feed_dict={
x:batch_data1,
y:batch_labels1
}
)
if (i+1)%500 ==0:
print('Train step:%d,loss:%4.5f,acc:%4.5f'
%(i+1,loss_val,acc_val))
if (i+1)%5000 ==0:
test_data = CifarData(test_filenames,False)
all_test_acc_val = []
for j in range(test_steps):
test_batch_data,test_batch_labels = test_data.next_batch(batch_size)
test_acc_val = sess.run(
[accuracy],
feed_dict = {
x:test_batch_data,
y:test_batch_labels
}
)
all_test_acc_val.append(test_acc_val)
test_acc = np.mean(all_test_acc_val)
print('Test Step:%d, acc:%4.5f'%(i+1,test_acc))