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文章目录结论1. 导入2. 读取数据3. 计数器4. 实验4.1 Shuffle——》Repeat——》Batch4.2 Shuffle——》Batch——》Repeat4.3 Batch——》Repeat——》Shuffle4.4 Repeat——》Batch——》Shuffle结论操作结果先Shuffle每个Repeat的顺序都不一样后Shuffle所有的Re...

import osos.environ['TF_CPP_MIN_LOG_LEVEL']='2'import tensorflow as tffrom tensorflow.keras import layers, optimizers, datasets, Sequential from tensorflow.python.client import device_libpri...

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import tensorflow as tfprint(tf.__version__)import osos.environ['TF_CPP_MIN_LOG_LEVEL']='2'import warningswarnings.filterwarnings('ignore')from tensorflow import kerasfrom tensorflow.keras i...

转自博客： http://blog.youkuaiyun.com/xummgg/article/details/69214366 前言上月导师在组会上交我们用tensorflow写深度学习和卷积神经网络，并把其PPT的参考学习资料给了我们， 这是codelabs上的教程：《TensorFlow and deep learning,wit

import tensorflow as tfimport numpy as npfrom tensorflow.examples.tutorials.mnist import input_data# Import MNIST datamnist = input_data.read_data_sets('MNIST_data', one_hot=True)## In this exa

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data# Import MNIST datamnist = input_data.read_data_sets('MNIST_data', one_hot=True)# Parameterslearning_rate = 0.01

import tensorflow as tfimport numpyimport matplotlib.pyplot as pltrng = numpy.random# Parameterslearning_rate = 0.01training_epochs = 1000display_step = 50# Training Datatrain_X = numpy.asa

大多数正常人能在一秒内完成的任务，现在都由人工智能来完成。 ——吴恩达 深度学习模型中常见的数学知识向量、矩阵、多维数组卷积的运算提取局部特征激活函数，sigmoid、tanh、ReLUsoftmax将向量转化为概率值log-loss（cross-entropy）作为惩罚项网络参数优化的梯度反向传播算法随即梯度下降及其变种import tensorfl

tensorflow的常用运算包括求和、差、积、商、平均等import tensorflow as tf