HJZ11的博客

import tensorflow as tf"""学习 卷积和池化 api"""def conv2d_answer(): with tf.Graph().as_default(): x = tf.ones(shape=[64, 32, 32, 3]) # [batch_size, height, width, channels=depth] ...

import tensorflow as tf"""学习 relu激活函数 和 dropout使用"""tf.set_random_seed(43)def activation_func(): with tf.Graph().as_default(): hidden_layer_weights = tf.truncated_normal(shape=[4,...

import tensorflow as tfimport osfrom tensorflow.examples.tutorials.mnist import input_dataimport numpy as np"""用CNN来解决 手写数据集的代码。"""mnist = input_data.read_data_sets('../datas/mnist', one_hot...

import tensorflow as tfimport helperimport numpy as npimport matplotlib as mplfrom tensorflow.examples.tutorials.mnist import input_data# 设置字符集，防止中文乱码mpl.rcParams['font.sans-serif'] = [u'simHe...

import cv2import numpy as npimport matplotlib.pyplot as pltimport matplotlib as mpl# 设置字符集，防止中文乱码mpl.rcParams['font.sans-serif'] = [u'simHei']mpl.rcParams['axes.unicode_minus'] = Falsedef br...

# -- encoding:utf-8 --import numpy as npimport matplotlib.pyplot as pltimport tensorflow as tf# 打印numpy的数组对象的时候，中间不省略np.set_printoptions(threshold=np.inf)def show_image(image): shape =...

"""*关于代码的两点说明:*>本代码所编写的类并不代表是tensorflow最好的实践--只是为了展示批归一化。>本案例使用了MNIST数据集作为展示，但代码中设计的网络并不是作为手写字母识别用。之所以如此设计网络结构：1、忠于原论文；2、网络足够复杂，并可以展示批归一化可以加速网络训练。"""import tensorflow as tfimport tqdm...

"""本小节目的：分别运用下面2种API来 完成BN的训练。并比较做了BN和 未做BN的网络的准确率的区别。1. [Batch Normalization with `tf.layers.batch_normalization`](#example_1)2. [Batch Normalization with `tf.nn.batch_normalization`](#example...

import tensorflow as tfdef GroupNormaliztion(x, is_training, groups=32, decay=0.99, scope='gn'): """ :param x: x shape [N, H, W, C] :param is_training: bool值， 批归一化参数 :param grou...

from sklearn.datasets import load_filesfrom keras.utils import np_utilsimport numpy as npfrom glob import globimport osfrom torchvision import datasetsimport torchfrom PIL import Imageimport ...

import tensorflow as tf"""实现Senet block模块"""def Se_block(inputs_tensor, name, reduction=8): """ :param inputs_tensor: 4-D tensor [N, H, W, C] :param reduction: :return: """ with tf.variable_scope(name): _, H, W, C =.

import tensorflow as tf"""实现用于CNN的注意力机制的模块"""def cbam(inputs, reduction=8): """ 变量重用，我们使用的是 tf.AUTO_REUSE :param inputs: 输入的tensor格式为： [N, H, W, C] :param reduction: :retur...

from utils.MobileNetOps import *from utils import helperimport pickleimport numpy as npimport tensorflow as tfimport matplotlib as mplimport os# 设置字符集，防止中文乱码mpl.rcParams['font.sans-serif']...

from tflearn.datasets import oxflower17import matplotlib.pyplot as pltimport numpy as npX, Y = oxflower17.load_data( dirname='../datas/17flower', resize_pics=(224, 224), shuffle=True, one_ho...

from scipy import ioimport os"""# mat 是值MATLAB 的数据特征存储http://www.vlfeat.org/matconvnet/pretrained/#pretrained-models"""def get_vgg_weights(): data_path = '../datas/vgg/imagenet-vgg-veryd...

import osimport numpy as npimport tensorflow as tffrom scipy import iofrom tflearn.datasets import oxflower17"""基于数据集17类花，基于预训练VGG19模型进行迁移学习。# mat 是值MATLAB 的数据特征存储权重地址： http://www.vlfeat.o...

import osimport numpy as npimport tensorflow as tffrom scipy import iofrom tensorflow.examples.tutorials.mnist import input_data# 1、设置超参数learning_rate = 0.001epochs = 10batch_size = 128tes...

import tensorflow as tfimport osimport cv2import numpy as npimport matplotlib.pyplot as pltfrom genplate import GenPlate, gen_sample, charsdef make_example(image, label): """ 产生Example...

import tensorflow as tfimport osimport cv2import numpy as npimport matplotlib.pyplot as pltfrom genplate import GenPlate, gen_sample, charsfrom utils.ops import *def create_dir_path(path): ...

# coding=utf-8"""专门用于模拟数据的产生"""import PILfrom PIL import ImageFontfrom PIL import Imagefrom PIL import ImageDrawimport cv2import numpy as npimport osfrom math import *# font = ImageFont.t...

import osfrom PIL import Imageimport numpy as np"""提交时间：12月22日之前， 邮箱: yingjun@ibeifeng.com要求：准确率在 93%以上。PIL 已经整合到 pillow中，需要安装pillow. 命令： pip install pillow"""image_width = 50image_height...