基于Tensorflow的cifar10分类网络模型
Tensorflow算是老牌深度学习框架了，但是相比Pytorch来说，会稍微显得有些笨重，主要是计算必须在session中进行，在编写某些更为灵活的网络结构时，会比较麻烦。不过Tensorflow对分布式训练的支持较好，所以如果是需要使用分布式计算的情况下，使用Tensorflow会相对更加稳定一些。
这里主要是用Tensorflow复现之前用Pytorch实现的cifar10分类网络模型，代码主要是依据《complete guide to tensorflow for deep learning with python》这门课的第3节中CNN部分改写而来的。这门课由浅入深讲的相当不错，有大佬上传到了B站，可以在线观看，链接：https://www.bilibili.com/video/av23719974，同时课程用到的代码被B站okdidns大佬找出来了，地址是：https://github.com/SLPeoples/TensorFlow-Deep-Learning-Python，感兴趣的同学可以去看下。
首先是导入数据：
import tensorflow as tf
CIFAR_DIR = 'cifar-10-batches-py/'
def unpickle(file):
import pickle
with open(file, 'rb') as fo:
cifar_dict = pickle.load(fo, encoding='bytes')
return cifar_dict
dirs = ['batches.meta','data_batch_1',
'data_batch_2','data_batch_3',
'data_batch_4','data_batch_5',
'test_batch']
all_data = [0,1,2,3,4,5,6]
for i,direc in zip(all_data,dirs):
all_data[i] = unpickle(CIFAR_DIR+direc)
batch_meta = all_data[0]
data_batch1 = all_data[1]
data_batch2 = all_data[2]
data_batch3 = all_data[3]
data_batch4 = all_data[4]
data_batch5 = all_data[5]
test_batch = all_data[6]
import numpy as np
X = data_batch1[b"data"]
X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype("uint8")
def one_hot_encode(vec, vals=10):
n = len(vec)
out = np.zeros((n, vals))
out[range(n), vec] = 1
return out
class CifarHelper():
def __init__(self):
self.i = 0
self.all_train_batches = [
data_batch1,data_batch2,
data_batch3,data_batch4,data_batch5]
self.test_batch = [test_batch]
self.training_images = None
self.training_labels = None
self.test_images = None
self.test_labels = None
def set_up_images(self):
print("Setting Up Training Images and Labels")
self.training_images = np.vstack(
[d[b"data"] for d in self.all_train_batches])
train_len = len(self.training_images)
self.training_images = self.training_images.reshape(
train_len,3,32,32).transpose(0,2,3,1)/255
self.training_labels = one_hot_encode(
np.hstack([d[b"labels"] for d in self.all_train_batches]), 10)
print("Setting Up Test Images and Labels")
self.test_images = np.vstack([d[b"data"] for d in self.test_batch])
test_len = len(self.test_images)
self.test_images = self.test_images.reshape(
test_len,3,32,32).transpose(0,2,3,1)/255
self.test_labels = one_hot_encode(np.hstack(
[d[b"labels"] for d in self.test_batch]), 10)
def next_batch(self, batch_size):
x = self.training_images[self.i:self.i+batch_size].reshape(batch_size,32,32,3)
y = self.training_labels[self.i:self.i+batch_size]
self.i = (self.i + batch_size) % len(self.training_images)
return x, y
def test_next_batch(self, batch_size):
x = self.test_images[self.i:self.i+batch_size].reshape(batch_size,32,32,3)
y = self.test_labels[self.i:self.i+batch_size]
self.i = (self.i + batch_size) % len(self.test_images)
return x, y
ch = CifarHelper()
ch.set_up_images()上面的函数主要是用来读入cifar10格式的数据，这里在原程序的基础上加了一个test_next_batch，是因为在使用测试集进行验证的时候，我的电脑如果同时用10000张测试集的图片放入网络进行训练会导致显存不足……所以分batch来计算。
下面，我们要定义神经网络中需要用到的不同层，包括卷积层、全连接层、最大池化层、均值池化层、BN层等等，这也就是我感觉Tensorflow相比之下不那么方便的原因，每次架一个网络都得定义一次……
def init_weights(shape):
init_random_dist = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(init_random_dist)
def init_bias(shape):
init_bias_vals = tf.constant(0.1, shape=shape)
return tf.Variable(init_bias_vals)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2by2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
def avg_pool_2by2(x):
return tf.nn.avg_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
def global_avg_pool(x):
return tf.nn.avg_pool(x, ksize=[1, 8, 8, 1],
strides=[1, 8, 8, 1], padding='SAME')
def norm(x):
return tf.nn.lrn(x, depth_radius=4, bias=1.0, alpha=0.001/9.0, beta=0.75)
def conv_layer(input_x, shape):
W = init_weights(shape)
b = init_bias([shape[3]])
return tf.nn.relu(conv2d(input_x, W) + b)
def fc_layer(input_layer, size):
input_size = int(input_layer.get_shape()[1])
W = init_weights([input_size, size])
b = init_bias([size])
return tf.matmul(input_layer, W) + b定义完成之后，在建立模型的时候就和keras没有太大的区别了，按结构拼起来就好：
x = tf.placeholder(tf.float32,shape=[None,32,32,3])
y_true = tf.placeholder(tf.float32,shape=[None,10])
hold_prob1 = tf.placeholder(tf.float32)
hold_prob2 = tf.placeholder(tf.float32)
conv_1 = conv_layer(x,shape=[3,3,3,64])
conv_2 = conv_layer(conv_1,shape=[3,3,64,64])
bn_1 = norm(conv_2)
pooling_1 = max_pool_2by2(bn_1)
dropout_1 = tf.nn.dropout(pooling_1, keep_prob=hold_prob1)
conv_3 = conv_layer(dropout_1,shape=[3,3,64,128])
conv_4 = conv_layer(conv_3,shape=[3,3,128,128])
bn_2 = norm(conv_4)
pooling_2 = avg_pool_2by2(bn_2)
dropout_2 = tf.nn.dropout(pooling_2, keep_prob=hold_prob1)
conv_5 = conv_layer(dropout_2,shape=[3,3,128,256])
conv_6 = conv_layer(conv_5,shape=[3,3,256,256])
bn_3 = norm(conv_6)
pooling_3 = global_avg_pool(bn_3)
flat_1 = tf.reshape(pooling_3,[-1,256])
dropout_3 = tf.nn.dropout(flat_1, keep_prob=hold_prob2)
y_pred = fc_layer(dropout_3,10)
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_true,logits=y_pred))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
train = optimizer.minimize(cross_entropy)
init = tf.global_variables_initializer()之后就可以开始训练啦，不过由于tensorflow中的计算要在一个session中进行，有的时候训练完了忘了保存网络，就很悲剧了……
saver = tf.train.Saver()
Epoch = 10
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(500 * Epoch):
batch = ch.next_batch(100)
_, loss = sess.run([train, cross_entropy], feed_dict={
x: batch[0], y_true: batch[1],
hold_prob1: 0.9, hold_prob2: 0.5})
print("Epoch:", i//500+1, " ", (i%500+1) * 100, "/ 50000 ", "Loss =", loss)
if i%500 == 499:
matches = tf.equal(tf.argmax(y_pred,1),tf.argmax(y_true,1))
acc = tf.reduce_mean(tf.cast(matches,tf.float32))
acc_mean = 0
for _ in range(100):
batch = ch.test_next_batch(100)
acc_mean += sess.run(acc, feed_dict={
x:batch[0],y_true:batch[1],
hold_prob1: 1.0, hold_prob2: 1.0})
print("\n")
print("test acc =", acc_mean / 100)
print('\n')
saver.save(sess, './cifar10')最后是读取保存好的网络的方法
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, './cifar10')
matches = tf.equal(tf.argmax(y_pred,1),tf.argmax(y_true,1))
acc = tf.reduce_mean(tf.cast(matches,tf.float32))
acc_mean = 0
for _ in range(100):
batch = ch.test_next_batch(100)
acc_mean += sess.run(acc, feed_dict={
x:batch[0],y_true:batch[1],
hold_prob1: 1.0, hold_prob2: 1.0})
print("\n")
print("test acc =", acc_mean / 100)
print('\n')平心而论，其实Tensorflow使用起来也不算复杂，但是由于数据结构不如Pytorch灵活，我个人用起来会觉得稍微有点别扭。
最后放上完整代码的Github地址：https://github.com/PolarisShi/cifar10