#-*- coding:utf-8 -*-### required libaraiedimport osimport matplotlib.image as imgimport matplotlib.pyplot as pltimport skimagefrom skimage import color, data, transformfrom scipy import ndimageimport numpy as npimport tensorflow as tffrom IPython.co…

# coding: utf-8 # In[1]:import osimport numpy as npfrom skimage import color, data, transform, io # In[34]: import tensorflow as tfimport numpy as np train10_images = np.load('train10_images.npy')train10_labels = np.load('train10_labels.npy') y=tf.pl…

import osimport kerasimport timeimport numpy as npimport tensorflow as tffrom random import shufflefrom keras.utils import np_utilsfrom skimage import color, data, transform, io trainDataDirList = os.listdir("F:\\MachineLearn\\ML-xiaoxueqi\\fruits\\t…

import osimport numpy as npimport matplotlib.pyplot as pltfrom skimage import color,data,transform,io labelList = os.listdir("F:\\MachineLearn\\ML-xiaoxueqi\\fruits\\Training")allFruitsImageName = []for i in range(10): allFruitsImageName.append(…

import osimport numpy as npimport matplotlib.pyplot as pltfrom skimage import color,data,transform,io labelList = os.listdir("F:\\MachineLearn\\ML-xiaoxueqi\\fruits\\Training")allFruitsImageName = []for i in range(len(labelList)): allFruitsImage…

import osimport numpy as npimport matplotlib.pyplot as pltfrom PIL import Image, ImageChopsfrom skimage import color,data,transform,io #获取所有数据文件夹名称fileList = os.listdir("F:\\data\\flowers")trainDataList = []trianLabel = []testDataList = []testLa…

import osimport numpy as npimport matplotlib.pyplot as pltfrom PIL import Image, ImageChopsfrom skimage import color,data,transform,io #获取所有数据文件夹名称fileList = os.listdir("F:\\data\\flowers")trainDataList = []trianLabel = []testDataList = []testLa…

import numpy as np import tensorflow as tf import matplotlib.pyplot as plt def distort_color(image, color_ordering=0): ''' 随机调整图片的色彩,定义两种处理顺序. ''' if color_ordering == 0: image = tf.image.random_brightness(image, max_delta=32./255.) image = tf.image.…

import glob import os.path import numpy as np import tensorflow as tf from tensorflow.python.platform import gfile import tensorflow.contrib.slim as slim # 加载通过TensorFlow-Slim定义好的inception_v3模型. import tensorflow.contrib.slim.python.slim.nets.incepti…

import numpy as np import tensorflow as tf import matplotlib.pyplot as plt # 使用'r'会出错,无法解码,只能以2进制形式读取 # img_raw = tf.gfile.FastGFile('E:\\myresource\\moutance.jpg','rb').read() img_raw = open('E:\\myresource\\moutance.jpg','rb').read() # 把二进制文件解码为uin…

#-*- coding:utf- -*- import time import keras import skimage import numpy as np import tensorflow as tf import matplotlib.image as img from scipy import ndimage from skimage import color, data, transform %matplotlib inline #设置文件目录 Training = r'F:\\da…

import kerasimport matplotlib.pyplot as pltfrom keras.models import Sequentialfrom keras.layers import Dense,Activation,Flatten,Dropout,Convolution2D,MaxPooling2Dfrom keras.utils import np_utilsfrom keras.optimizers import RMSpropfrom skimage import…

import os import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data INPUT_NODE = 784 OUTPUT_NODE = 10 LAYER1_NODE = 500 def get_weight_variable(shape, regularizer): weights = tf.get_variable("weights", shape, initializer…

import tensorflow as tf tf.reset_default_graph() # 配置神经网络的参数 INPUT_NODE = 784 OUTPUT_NODE = 10 IMAGE_SIZE = 28 NUM_CHANNELS = 1 NUM_LABELS = 10 # 第一层卷积层的尺寸和深度 CONV1_DEEP = 32 CONV1_SIZE = 5 # 第二层卷积层的尺寸和深度 CONV2_DEEP = 64 CONV2_SIZE = 5 # 全连接层的节点个数 FC…

import os import glob import os.path import numpy as np import tensorflow as tf from tensorflow.python.platform import gfile # 原始输入数据的目录,这个目录下有5个子目录,每个子目录底下保存这属于该 # 类别的所有图片. INPUT_DATA = 'F:\\TensorFlowGoogle\\201806-github\\datasets\\flower_photos'…

import tensorflow as tf # 输入数据 from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("E:\\MNIST_data", one_hot=True) # 定义网络的超参数 learning_rate = 0.001 training_iters = 200000 batch_size = 128 display_step =…

import tensorflow as tf import numpy as np from tensorflow.examples.tutorials.mnist import input_data #设置输入参数 batch_size = 128 test_size = 256 # 初始化权值与定义网络结构,建构一个3个卷积层和3个池化层,一个全连接层和一个输出层的卷积神经网络 # 首先定义初始化权重函数 def init_weights(shape): return tf.Variabl…

import tempfile import tensorflow as tf train_files = tf.train.match_filenames_once("E:\\output.tfrecords") test_files = tf.train.match_filenames_once("E:\\output_test.tfrecords") # 解析一个TFRecord的方法. def parser(record): features = tf.pa…

import numpy as np import tensorflow as tf import matplotlib.pyplot as plt def distort_color(image, color_ordering=0): if color_ordering == 0: image = tf.image.random_brightness(image, max_delta=32./255.) image = tf.image.random_saturation(image, low…

# -*- coding: utf-8 -*- import glob import os.path import numpy as np import tensorflow as tf from tensorflow.python.platform import gfile import tensorflow.contrib.slim as slim import tensorflow.contrib.slim.python.slim.nets.inception_v3 as inceptio…

import glob import os.path import numpy as np import tensorflow as tf from tensorflow.python.platform import gfile import tensorflow.contrib.slim as slim # 因为slim.nets包在 tensorflow 1.3 中有一些问题,所以这里为了方便 # 我们将slim.nets.inception_v3中的代码拷贝到了同一个文件夹下. # imp…

import glob import os.path import numpy as np import tensorflow as tf from tensorflow.python.platform import gfile # 原始输入数据的目录,这个目录下有5个子目录,每个子目录底下保存这属于该 # 类别的所有图片. INPUT_DATA = 'F:\\TensorFlowGoogle\\201806-github\\datasets\\flower_photos\\' # 输出文件地址…

import glob import os.path import numpy as np import tensorflow as tf from tensorflow.python.platform import gfile # 原始输入数据的目录,这个目录下有5个子目录,每个子目录底下保存这属于该 # 类别的所有图片. INPUT_DATA = 'F:\\TensorFlowGoogle\\201806-github\\datasets\\flower_photos\\' # 输出文件地址…

# 导入模块 import numpy as np import tensorflow as tf import matplotlib.pyplot as plt # 加载数据 from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("E:\\MNIST_data\\", one_hot=True) #模型训练 # 设置超参数 learning_rate =…

#加载TF并导入数据集 import tensorflow as tf from tensorflow.contrib import rnn from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("E:\\MNIST_data\\", one_hot=True) #设置训练的超参数,学习率 训练迭代最大次数,输入数据的个数 learning_rate= 0…

#训练过程的可视化 ,TensorBoard的应用 #导入模块并下载数据集 import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data #设置超参数 max_step=1000 learning_rate=0.001 dropout=0.9 # 用logdir明确标明日志文件储存路径 #训练过程中的数据储存在E:\\MNIST_data\\目录中,通过这个路径指定--log_dir data…

import numpy as np import tensorflow as tf import matplotlib.pyplot as plt def add_layer(inputs, in_size, out_size, activation_function = None): #构建权重: in_sizeXout_size大小的矩阵 weights = tf.Variable(tf.random_normal([in_size, out_size]))#生成随机数 #构建偏置: 1X…

import tensorflow as tf from tensorflow.python.framework import graph_util v1 = tf.Variable(tf.constant(1.0, shape=[1]), name = "v1") v2 = tf.Variable(tf.constant(2.0, shape=[1]), name = "v2") result = v1 + v2 init_op = tf.global_varia…

import tensorflow as tf files = tf.train.match_filenames_once("E:\\MNIST_data\\output.tfrecords") filename_queue = tf.train.string_input_producer(files, shuffle=False) # 读取文件. reader = tf.TFRecordReader() _,serialized_example = reader.read(filen…

import numpy as np import tensorflow as tf import matplotlib.pyplot as plt image_raw_data = tf.gfile.FastGFile("F:\\TensorFlowGoogle\\201806-github\\datasets\\cat.jpg",'rb').read() with tf.Session() as sess: img_data = tf.image.decode_jpeg(image…