用Alexnet网络做一个二分类问题，输入的图片也是227乘227的彩图。遇到了如下的问题
也不知道怎么解决，求大神帮忙
from future import division, print_function, absolute_import
import os
import random
from PIL import Image
import numpy as np
import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d, upsample_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression
#import tflearn.datasets.oxflower17 as oxflower17
#X, Y = oxflower17.load_data(one_hot=True, resize_pics=(227, 227))
np.random.seed(170)
def load_data(DataDir):
data = np.empty((170,227,227,3),dtype="float32") #37是图片个数，800*800为图片大小，3是图片通道数
label = np.empty((170,),dtype="int")
imgs = os.listdir(DataDir)
num = len(imgs)
for i in range(num):
img = Image.open(DataDir+imgs[i])
arr = np.asarray(img,dtype="float32")
data[i,:,:,:] = arr
if i<53:
label[i] = int(0) #o是无缺陷类，共170张图，第0-52张为无缺陷类。
else:
label[i] = int(1)
data /= np.max(data) #这两行是数据归一化，不用管
data -= np.mean(data)
return data,label
data,label=load_data('C:/Users/Administrator/Desktop/cnntest/picture/')
index = [i for i in range(len(data))]
random.shuffle(index) #之前做标签时，数据是按类排的，这边直接打乱顺序。所以标签还是一一对应的。
data = data[index]
label = labelindex = (data[0:119],data[120:]) #traindata包括了两类数据，不用分开来输入。7:3训练集：预测集
(TrainLabel,TestLabel) = (label[0:119],label[120:])
Building 'AlexNet'
network = input_data(shape=[None, 227, 227, 3])
network = conv_2d(network, 96, 11, strides=4, activation='relu') #96为滤波器个数，11为滤波器大小
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, activation='relu', group=2)
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = upsample_2d(network,2,name='upsample')
network = fully_connected(network, 4096, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='relu')
network = dropout(network, 0.5)
#net = tflearn.global_avg_pool(net)
network = fully_connected(network, 2, activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
Training
print('Training ------------')
model = tflearn.DNN(network, checkpoint_path='model_alexnet', max_checkpoints=1, tensorboard_verbose=2)
model.fit(TrainData,TrainLabel, n_epoch=5, validation_set=0.1, shuffle=True,
show_metric=True, batch_size=64, snapshot_step=200,
snapshot_epoch=False, run_id='CNNPOTATO')
model.save('CNNPOTATO.model')
model.load('CNNPOTATO.model')
print('\nTesting ------------')
Evaluate the model with the metrics we defined earlier
loss, accuracy = model.evaluate(TestData, TestLabel)
print('\ntest loss: ', loss)
print('\ntest accuracy: ', accuracy)
#print(model.predict([Y[1]]))
