import numpy as np import tensorflow as tf import matplotlib.pyplot as plt #读取图片 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…
<!DOCTYPE html> <html> <head> <meta charset="utf-8"> </head> <body> <p>输入数字并点击验证按钮:</p> <input id="id1" type="number" min="100" max="300" required> <…
# 1. 模型定义. import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist_data = input_data.read_data_sets('F:\\TensorFlowGoogle\\201806-github\\datasets\\MNIST_data', one_hot=True) # 通过TensorFlow中的placeholder定义输入. x = tf.pl…
# 1. 数据预处理. import keras from keras.models import Model from keras.datasets import mnist from keras.layers import Input, Dense from tflearn.layers.core import fully_connected num_classes = 10 img_rows, img_cols = 28, 28 # 通过Keras封装好的API加载MNIST数据. (tr…
# 1. 数据预处理. import keras from keras.models import Model from keras.datasets import mnist from keras.layers import Input, Dense from tflearn.layers.core import fully_connected num_classes = 10 img_rows, img_cols = 28, 28 # 通过Keras封装好的API加载MNIST数据. (tr…
# 1. 数据预处理 import keras from keras import backend as K from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D num_classes = 10 img_rows, img_cols = 28, 28 # 通过Keras封装好的API加载M…
# 1. 自定义模型并训练. import numpy as np import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data tf.logging.set_verbosity(tf.logging.INFO) def lenet(x, is_training): x = tf.reshape(x, shape=[-1, 28, 28, 1]) conv1 = tf.layers.conv2…
# 1. 模型定义. import numpy as np import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data tf.logging.set_verbosity(tf.logging.INFO) mnist = input_data.read_data_sets("F:\\TensorFlowGoogle\\201806-github\\datasets\\MNIST_data&qu…
# 1. 通过TensorFlow-Slim定义卷机神经网络 import numpy as np import tensorflow as tf import tensorflow.contrib.slim as slim from tensorflow.examples.tutorials.mnist import input_data # 通过TensorFlow-Slim来定义LeNet-5的网络结构. def lenet5(inputs): inputs = tf.reshape(in…
import numpy as np import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data 1.在自己编译器运行的python环境的...\Python3\Lib\site-packages,该目录下有文件夹tensorflow, tensorflow_core, ensorflow_estimator 2.进入tensorflow_core\examples文件夹,如果文件夹下只有s…
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…
Matplotlib 是 Python 的绘图库. 它可与 NumPy 一起使用,提供了一种有效的 MatLab 开源替代方案. 它也可以和图形工具包一起使用,如 PyQt 和 wxPython. Windows 系统安装 Matplotlib 进入到 cmd 窗口下,执行以下命令: python -m pip install -U pip setuptools python -m pip install matplotlib Linux 系统安装 Matplotlib 可以使用 Linux 包…
import tensorflow as tf from tensorflow import keras from keras import Sequential,datasets, layers, optimizers, metrics def preprocess(x, y): """数据处理函数""" x = tf.cast(x, dtype=tf.float32) / 255. y = tf.cast(y, dtype=tf.int32)…
实验课程名称:大数据处理技术 实验项目名称:hadoop集群实现PageRank算法 实验类型:综合性 实验日期:2018年 6 月4日-6月14日 学生姓名 吴裕雄 学号 15210120331 班级 软工三班 专业名称 软件工程 实验组 其他成员 无 实验地点 F110 实验成绩 (教师签名)   实验目的与要求 了解PageRank算法 学会用mapreduce解决实际的复杂计算问题 搭建hadoop分布式集群 编写mapreduce代码 根据输入的网页链接数据,能够得到最终的pagera…
import tensorflow as tf from tensorflow import keras from tensorflow.keras import datasets import os # do not print irrelevant information # os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # x: [60k,28,28], [10,28,28] # y: [60k], [10k] (x, y), (x_test, y_te…
import tensorflow as tf from tensorflow import keras # train: 60k | test: 10k (x, y), (x_test, y_test) = keras.datasets.mnist.load_data() x.shape y.shape # 0纯黑.255纯白 x.min(), x.max(), x.mean() x_test.shape, y_test.shape # 0-9有10种分类结果 y_onehot = tf.on…
import os import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data from tensorflow.contrib.tensorboard.plugins import projector INPUT_NODE = 784 OUTPUT_NODE = 10 LAYER1_NODE = 500 def get_weight_variable(shape, regularizer):…
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data # 1. 生成变量监控信息并定义生成监控信息日志的操作. SUMMARY_DIR = "F:\\temp\\log" BATCH_SIZE = 100 TRAIN_STEPS = 3000 def variable_summaries(var, name): with tf.name_scope('summaries'):…
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=tf.trunca…
import numpy as np import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data # 读取文件. filename_queue = tf.train.string_input_producer(["F:\\output.tfrecords"]) reader = tf.TFRecordReader() _,serialized_example = reader.re…
import numpy as np import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data # 定义函数转化变量类型. def _int64_feature(value): return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) def _bytes_feature(value): return tf.…
import tensorflow as tf INPUT_NODE = 784 OUTPUT_NODE = 10 IMAGE_SIZE = 28 NUM_CHANNELS = 1 NUM_LABELS = 10 CONV1_DEEP = 32 CONV1_SIZE = 5 CONV2_DEEP = 64 CONV2_SIZE = 5 FC_SIZE = 512 def inference(input_tensor, train, regularizer): with tf.variable_s…
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data INPUT_NODE = 784 # 输入节点 OUTPUT_NODE = 10 # 输出节点 LAYER1_NODE = 500 # 隐藏层数 BATCH_SIZE = 100 # 每次batch打包的样本个数 # 模型相关的参数 LEARNING_RATE_BASE = 0.8 LEARNING_RATE_DECAY = 0.9…
# 1. 数据预处理. from keras.layers import LSTM from keras.datasets import imdb from keras.models import Sequential from keras.preprocessing import sequence from keras.layers import Dense, Embedding max_features = 20000 maxlen = 80 batch_size = 32 # 加载数据并将…
将原来版本的keras卸载了,再安装2.1.5版本的keras就可以了.…
找到对应的keras配置文件keras.json 将里面的内容修改为以下就可以了…
# 1. 通过TFLearn的API定义卷机神经网络. import tflearn import tflearn.datasets.mnist as mnist from tflearn.layers.conv import conv_2d, max_pool_2d from tflearn.layers.estimator import regression from tflearn.layers.core import input_data, dropout, fully_connecte…
import tensorflow as tf import numpy as np ''' 初始化运算图,它包含了上节提到的各个运算单元,它将为W,x,b,h构造运算部件,并将它们连接 起来 ''' graph = tf.Graph() #一次tensorflow代码的运行都要初始化一个session session = tf.InteractiveSession(graph=graph) ''' 我们定义三种变量,一种叫placeholder,它对应输入变量,也就是上节计算图所示的圆圈部分,…
!mkdir '/content/gdrive/My Drive/conversation' ''' 将文本句子分解成单词,并构建词库 ''' path = '/content/gdrive/My Drive/conversation/' with open(path + 'question.txt', 'r') as fopen: text_question = fopen.read().lower().split('\n') with open(path + 'answer.txt', 'r…
!pip install gym import random import numpy as np import matplotlib.pyplot as plt from keras.layers import Dense, Dropout, Activation from keras.models import Sequential from keras.optimizers import Adam from keras import backend as K from collection…