_2d(convnet, 32, 2, activation = 'relu')convnet = max_pool_2d(convnet, 2)convnet = tf.reshape(convnet, [-1, 24
浏览 4提问于2017-03-13得票数 3
回答已采纳
2回答
CNN模型只预测了1类
、、、
= conv_2d(convnet, 32, 5, activation='relu')convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 128,
浏览 5提问于2018-07-06得票数 0
2回答
=input_data(shape=[None,32,32,3],name='input')
convnet=max_pool_2d(convnet,2)
convnet=max_pool_2d(convnet
浏览 19提问于2017-02-27得票数 1
2d(convnet, 30)
convnet = max_pool_2d(convnet,10)
convnet = max_pool_2d(convnet, 5)
浏览 0提问于2019-02-18得票数 1
, 3, activation='relu')convnet = max_pool_2d(convnet, 3)
convnet = conv_2d(convnet, 128, 3, activation='relu'
浏览 54提问于2019-02-18得票数 0
1回答
无效张板式回调对象
、、、、
= input_data(shape=[None, 1, 1, 1], name='input') convnet = max_pool_2d(convnet, 5) <em
浏览 10提问于2022-07-26得票数 1
回答已采纳
1回答
convnet = input_data(shape=[None, 28, 28, 1], name='input')# http://tflearn.org/activations/convnet = max_pool
浏览 1提问于2017-07-19得票数 1
回答已采纳
_1.weight",module.FeatureExtraction.ConvNet.bn0_1.weight",module.FeatureExtraction.ConvNet.bn0_1.bias",module.FeatureExtraction.ConvNet.bn0_1.num_batches_tracked",module.FeatureExtraction.ConvNet.conv0_2.weight",module.FeatureExtraction.<e
浏览 0提问于2019-07-03得票数 0
= input_data(shape=[None, 28, 28, 1], name='input')
convnet= ma
浏览 39提问于2019-11-07得票数 0
='relu') convnet = conv_2d(convnet, 64, 5, activation='relu') convnet = conv_2d(convnet, 128, 5, activation='relu'
浏览 0提问于2018-12-26得票数 0
回答已采纳
image_shape=(128, 128), mode='folder', grayscale=True, categorical_labels=True, normalize=True)
convnet = conv_2d(convnet, 32, 2, activation='relu')
convnet = max_pool
浏览 2提问于2017-03-20得票数 4
_2d(convnet, 28, 5, activation='relu') # layerconvnet = conv_2d(convnet, (56, 5, activation='relu') convne
浏览 1提问于2018-07-18得票数 1
1回答
=input_data(shape=[None,50,50,3],name='input') convnet=conv_2d(convnet,64,5,activation='relu')
convnet=max_pool_2d(<e
浏览 0提问于2018-03-27得票数 3
回答已采纳
, FILTER_SIZE, activation='relu')convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet</em
浏览 1提问于2018-10-08得票数 0
convnet :" , convnet.shape)
print ("convnet :" , convnet.shape)
convnet = conv_2d(convnet, 48, 5,strides=strides, activa
浏览 6提问于2017-12-15得票数 0
回答已采纳
云服务器
ICP备案
对象存储
云点播
实时音视频
腾讯云开发者
Copyright © 2013 - 2026 Tencent Cloud. All Rights Reserved. 腾讯云 版权所有
深圳市腾讯计算机系统有限公司 ICP备案/许可证号：粤B2-20090059 
粤公网安备44030502008569号
腾讯云计算（北京）有限责任公司 京ICP证150476号 | 京ICP备11018762号