# -*- 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)net=tf.layers.conv2d(      inputs=x,      filters=32,      kernel_size=[5, 5],      padding="same",      activation=tf.nn.relu)net=tf.layers.max_pooling2d(inputs=net, pool_size=[2, 2], strides=2)#第二个卷积层(50->25)net=tf.layers.conv2d(      inputs=net,      filters=64,      kernel_size=[5, 5],      padding="same",      activation=tf.nn.relu)net=tf.layers.max_pooling2d(inputs=net, pool_size=[2, 2], strides=2)#第三个卷积层(25->12)net=tf.layers.conv2d(      inputs=net,      filters=128,      kernel_size=[3, 3],      padding="same",      activation=tf.nn.relu)net=tf.layers.max_pooling2d(inputs=net, pool_size=[2, 2], strides=2)#第四个卷积层(12->6)net=tf.layers.conv2d(      inputs=net,      filters=128,      kernel_size=[3, 3],      padding="same",      activation=tf.nn.relu)net=tf.layers.max_pooling2d(inputs=net, pool_size=[2, 2], strides=2)net = tf.reshape(net, [-1, 6 * 6 * 128])#全连接层net = tf.layers.dense(inputs=net, units=1024, activation=tf.nn.relu)net = tf.layers.dense(inputs=net, units=512, activation=tf.nn.relu)logits= tf.layers.dense(inputs=net, units=5, activation=None)#---------------------------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()