参考回答：
tensorflow的图像分类代码
# -*- 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*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=0.8s=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----------------------
`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()`
