四、创建文件名队列和内存队列 创建文件名队列 利用tensorflow的tf.train.string_input_producer()(注2) 函数。给函数传入一个文件名列表,系统将会转换未文件名队列。tf.train.string_input_producer() 函数有两个重要的参数,分别是num_epochs和shuffle,num_epochs表示epochs数,shuffle表示是否打乱文件名队列内文件的顺...
import numpy as np import pandas as pd import matplotlib.pyplot as plt import tensorflow as tf from sklearn.preprocessing import StandardScaler In [2]: (x_train_all, y_train_all), (x_test, y_test) = tf.keras.datasets.cifar10.load_data() x_train_all.shape Out[2]: (50000, 32,...
import numpy as np import pandas as pd import matplotlib.pyplot as plt import tensorflow as tf from sklearn.preprocessing import StandardScaler In [2]: (x_train_all, y_train_all), (x_test, y_test) = tf.keras.datasets.cifar10.load_data() x_train_all.shape Out[2]: (50000, 32,...
[1]Tensorflow官方文档: https://www.tensorflow.org [2]tf.transpose函数解析: http://blog.csdn.net/u013555719/article/details/79344063 [3]tf.slice函数解析: http://blog.csdn.net/u013555719/article/details/79343847 [4]CIFAR10/CIFAR100数据集介绍: http://blog.csdn.net/u013555719/article/details/...
用TensorFlow实现CNN识别cifar10数据集。 知识回顾: CNN的本质是先把数据进行特征提取,再送进DNN。前面特征提取的部分可以概括为CBAPD,C表示卷积(convolution),B表示批标准化(batch normalization),A表示激活(activation),P表示池化(pool),D表示(dropout)
cifar10数据集上进行图片分类,基于tensorflow框架,旨在探究不同的改进策略对分类准确率的影响,如何一步步得提高准确率
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data # 1.设置输入和输出节点的个数,配置神经网络的参数 INPUT_NODE = 784 # 输入层的节点数。对于 MNIST 数据集,这个就等于图片的像素 OUTPUT_NODE = 10 # 输出层的节点数。等于类别的数目。
首先处理输入,在 /home/your_name/TensorFlow/cifar10/ 下建立 cifar10_input.py,输入如下代码: from__future__importabsolute_import# 绝对导入from__future__importdivision# 精确除法,/是精确除,//是取整除from__future__importprint_function# 打印函数importosimporttensorflowastf# 建立一个 cifar10_data 的...
环境pycharm2019 + tensorflow2.0 avx2_for_cpu 还是先放完整源码 import tensorflow as tf import matplotlib.pyplot as plt fromtensorflow.keras import layers, optimizers, datasets import datetime import time import os def data_sets(): (train_images, train_labels), (val_images, val_labels) = dataset...
markdown cell, type '# %% [markdown]'# %%from IPythonimportget_ipython# %%get_ipython().run_line_magic('matplotlib','inline')importmatplotlib as mplimportmatplotlib.pyplot as pltimportnumpy as npimportosimportpandas as pdimportsklearnimportsysimporttensorflow as tfimporttimefrom tensorflowimport...