tensorflow 1.0用CNN进行图像分类

技术  /  管理员 发布于 7年前   142

tensorflow升级到1.0之后，增加了一些高级模块： 如tf.layers, tf.metrics, 和tf.losses，使得代码稍微有些简化。

任务：花卉分类

版本：tensorflow 1.0

数据：flower-photos

花总共有五类，分别放在5个文件夹下。

闲话不多说，直接上代码，希望大家能看懂：）

复制代码

# -*- coding: utf-8 -*-from skimage import io,transformimport globimport osimport tensorflow as tfimport numpy as npimport timepath='e:/flower/'#将所有的图片resize成100*100w=100h=100c=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=0.8s=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----------------------#占位符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=10batch_size=64sess=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()

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。