tf.square square( x, name=None ) 功能说明: 计算张量对应元素平方 参数列表: 参数名 必选 类型 说明 x 是 张量 是 half, float32, float64, int32, int64, complex64, complex128 其中一种类型 name 否 string 运算名称 示例代码: import tensorflow as tf ini_x=[[1,2],[3,4],[5,6]] x=tf.Variable(ini_x,dtype=tf.i…

本资料是在Ubuntu14.0.4版本下进行,用来进行图像处理,所以只介绍关于图像处理部分的内容,并且默认TensorFlow已经配置好,如果没有配置好,请参考官方文档配置安装,推荐用pip安装.关于配置TensorFlow,官方已经说得很详细了,我这里就不啰嗦了.官方教程看这里:https://www.tensorflow.org/get_started/os_setup 如果安装了GPU版本的TensorFlow,还需要配置Cuda,关于Cuda安装看这里:https://www.tenso…

前言 本文接着上一篇继续来聊Tensorflow的接口,上一篇中用较低层的接口实现了线性模型,本篇中将用更高级的API--tf.estimator来改写线性模型. 还记得之前的文章<机器学习笔记2 - sklearn之iris数据集>吗?本文也将使用tf.estimator改造该示例. 本文代码都是基于API版本r1.4.本文中本地开发环境为Pycharm,在文中不再赘述. tf.estimator 内置模型 比起用底层API"较硬"的编码方式,tf.estimator的在…

import tensorflow as tf import tensorflow.contrib.slim as slim import rawpy import numpy as np import tensorflow as tf import struct import glob import os from PIL import Image import time __sony__ = 0 __huawei__ = 1 __blackberry__ = 2 __stage_raw2ra…

# 23 Batch Normalization import numpy as np import tensorflow as tf import matplotlib.pyplot as plt ACTIVATION = tf.nn.tanh N_LAYERS = 7 N_HIDDEN_UNITS = 30 def fix_seed(seed=1): # reproducible np.random.seed(seed) tf.set_random_seed(seed) def plot_h…

import tensorflow as tf # 22 scope (name_scope/variable_scope) from __future__ import print_function class TrainConfig: batch_size = 20 time_steps = 20 input_size = 10 output_size = 2 cell_size = 11 learning_rate = 0.01 class TestConfig(TrainConfig):…

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt BATCH_START = 0 TIME_STEPS = 20 BATCH_SIZE = 50 INPUT_SIZE = 1 OUTPUT_SIZE = 1 CELL_SIZE = 10 LR = 0.006 BATCH_START_TEST = 0 def get_batch(): global BATCH_START, TIME_STEPS #…

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt BATCH_START = 0 TIME_STEPS = 20 BATCH_SIZE = 50 INPUT_SIZE = 1 OUTPUT_SIZE = 1 CELL_SIZE = 10 LR = 0.006 BATCH_START_TEST = 0 def get_batch(): global BATCH_START, TIME_STEPS x…

import tensorflow as tf import numpy as np def add_layer(inputs, in_size, out_size, n_layer, activation_function=None): # add one more layer and return the output of this layer layer_name = 'layer%s' % n_layer with tf.name_scope(layer_name): with tf.…

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt def add_layer(inputs, in_size, out_size, activation_function=None): Weights = tf.Variable(tf.random_normal([in_size, out_size])) biases = tf.Variable(tf.zeros([1, out_size]) +…