第3章 第10节 Keras/tensorflow

推荐给朋友

● MXNet和Tensorflow的区别

参考回答:

MXNet有两个主要的进程server和worker,worker之间不能进行通信,只能通过server互相影响。Tensorflow有worker,server,client三种进程,worker是可以相互通信的,可以根据op的依赖关系主动收发数据。MXNet常用来做数据并行,每个GPU设备上包含了计算图中所有的op,而Tensorflow可以由用户指定op的放置,一般情况下一个GPU设备负责某个和几个op的训练任务。

● Tensorflow的工作原理

参考回答:

Tensorflow是用数据流图来进行数值计算的,而数据流图是描述有向图的数值计算过程。在有向图中,节点表示为数学运算,边表示传输多维数据,节点也可以被分配到计算设备上从而并行的执行操作。

● Tensorflow中interactivesession和session的区别

参考回答:

Tf. Interactivesession()默认自己就是用户要操作的会话,而tf.Session()没有这个默认,所以eval()启动计算时需要指明使用的是哪个会话。

● 手写一下tensorflow的图像分类代码

参考回答:

tensorflow的图像分类代码
# -*- coding: utf-8 -*-
from skimage import io,transform
import glob
import os
import tensorflow as tf
import numpy as np
import time
path='e:/flower/'

#将所有的图片resize成100*100

w=100
h=100
c=3


#读取图片

def read_img(path):
cate=[path+x for x in os.listdir(path) if os.path.isdir(path+x)]
imgs=[]
labels=[]
for idx,folder in enumerate(cate):
for im in glob.glob(folder+'/*.jpg'):
print('reading the images:%s'%(im))
img=io.imread(im)
img=transform.resize(img,(w,h))
imgs.append(img)
labels.append(idx)
return np.asarray(imgs,np.float32),np.asarray(labels,np.int32)
data,label=read_img(path)

#打乱顺序

num_example=data.shape[0]
arr=np.arange(num_example)
np.random.shuffle(arr)
data=data[arr]
label=label[arr]


#将所有数据分为训练集和验证集

ratio=0.8
s=np.int(num_example*ratio)
x_train=data[:s]
y_train=label[:s]
x_val=data[s:]
y_val=label[s:]

#-----------------构建网络----------------------

x=tf.placeholder(tf.float32,shape=[None,w,h,c],name='x')
y_=tf.placeholder(tf.int32,shape=[None,],name='y_')

#第一个卷积层(100——>50)

conv1=tf.layers.conv2d(inputs=x,filters=32,kernel_size=[5, 5],padding="same",
activation=tf.nn.relu,kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
pool1=tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)

#第二个卷积层(50->25)

conv2=tf.layers.conv2d(inputs=pool1,filters=64,kernel_size=[5, 5],padding="same",
activation=tf.nn.relu,kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
pool2=tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

#第三个卷积层(25->12)

conv3=tf.layers.conv2d(inputs=pool2,filters=128,kernel_size=[3, 3],padding="same",
activation=tf.nn.relu,kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
pool3=tf.layers.max_pooling2d(inputs=conv3, pool_size=[2, 2], strides=2)

#第四个卷积层(12->6)

conv4=tf.layers.conv2d(inputs=pool3,filters=128,kernel_size=[3, 3],padding="same",
activation=tf.nn.relu,kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
pool4=tf.layers.max_pooling2d(inputs=conv4, pool_size=[2, 2], strides=2)
re1 = tf.reshape(pool4, [-1, 6 * 6 * 128])

#全连接层

dense1 = tf.layers.dense(inputs=re1, units=1024, activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003))
dense2= tf.layers.dense(inputs=dense1, units=512, activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003))
logits= tf.layers.dense(inputs=dense2, units=5, activation=None,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003))

#---------------------------网络结束---------------------------

loss=tf.losses.sparse_softmax_cross_entropy(labels=y_,logits=logits)
train_op=tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
correct_prediction = tf.equal(tf.cast(tf.argmax(logits,1),tf.int32), y_)
acc= tf.reduce_mean(tf.cast(correct_prediction, tf.float32))


#定义一个函数,按批次取数据

def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):
assert len(inputs) == len(targets)
if shuffle:
indices = np.arange(len(inputs))
np.random.shuffle(indices)
for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):
if shuffle:
excerpt = indices[start_idx:start_idx + batch_size]
else:
excerpt = slice(start_idx, start_idx + batch_size)
yield inputs[excerpt], targets[excerpt]

#训练和测试数据,可将n_epoch设置更大一些

n_epoch=10
batch_size=64
sess=tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
for epoch in range(n_epoch):
start_time = time.time()
#training
train_loss, train_acc, n_batch = 0, 0, 0
for x_train_a, y_train_a in minibatches(x_train, y_train, batch_size, shuffle=True):
_,err,ac=sess.run([train_op,loss,acc], feed_dict={x: x_train_a, y_: y_train_a})
train_loss += err; train_acc += ac; n_batch += 1
print("   train loss: %f" % (train_loss/ n_batch))
print("   train acc: %f" % (train_acc/ n_batch))
#validation
val_loss, val_acc, n_batch = 0, 0, 0
for x_val_a, y_val_a in minibatches(x_val, y_val, batch_size, shuffle=False):
err, ac = sess.run([loss,acc], feed_dict={x: x_val_a, y_: y_val_a})
val_loss += err; val_acc += ac; n_batch += 1
print("   validation loss: %f" % (val_loss/ n_batch))
print("   validation acc: %f" % (val_acc/ n_batch))
sess.close()