如何将tf.data.Dataset拆分成x_train、y_train、x_test、y_test_如何将数据集拆分成(X_train，y_train)，(X_test，y_test)？_在tensorflow中创建X_test、X_train、Y_test、Y_train - 腾讯云开发者社区

pass mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train...((x_train, y_train)) test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test)) train_dataset...def test_step(model, x_test, y_test): predictions = model(x_test) loss = loss_object(y_test, predictions..., y_train) in train_dataset: train_step(model, optimizer, x_train, y_train) with train_summary_writer.as_default..., y_test) in test_dataset: test_step(model, x_test, y_test) with test_summary_writer.as_default

3250 0

Keras 3.0发布：全面拥抱 PyTorch！

Keras 3 的 fit()/evaluate()/predict()例程兼容 tf.data.Dataset 对象、PyTorch 的 DataLoader 对象、NumPy 数组和 Pandas..., y_train), (x_test, y_test) = keras.datasets.mnist.load_data() x_train = np.reshape(x_train, (-1, 784...)).astype("float32") x_test = np.reshape(x_test, (-1, 784)).astype("float32") y_train = keras.utils.to_categorical...(y_train) y_test = keras.utils.to_categorical(y_test) # Reserve 10,000 samples for validation. x_val...= x_train[-10000:] y_val = y_train[-10000:] x_train = x_train[:-10000] y_train = y_train[:-10000] #

2711 0

您找到你想要的搜索结果了吗？

是的

没有找到

逻辑回归模型_RF模型

= 0) return x_train, x_test, y_train, y_test def main(): print("---------")...x_train, x_test, y_train, y_test = load_data() model = LogisticRegression() model.fit...() # # x_train, x_test, y_train, y_test = train_test_split(input_data.data, input_data.target,...test_size = 0.2, random_state = 0) # # return x_train, x_test, y_train, y_test def main(): print...("---------") x_train, x_test, y_train, y_test = load_data() model = LogisticRegression() model.fit(x_train

7232 0

Python的机器学习库之Sklearn快速入门1.基本概述2.入门实践3.部分结果

, x_test, y_train, y_test = train_test_split( iris_x[:100], iris_y[:100], test_size=0.3)...x_test)) print(y_test) def knn_test2(): ''' knn实现iris的分类,并且使用交叉验证，并且划分成多次进行交叉验证，得到一个准确度列表...() linear.fit(x_train, y_train) linear.predict(x[:4]) print(linear.score(x_test, y_test))...() model.fit(x_train, y_train) score1=model.score(x_test,y_test) # print(x[:5], y[:5])...() model.fit(x_train, y_train) score2=model.score(x_test,y_test) # print(x[:5], y[:5])

6678 0

解决read_data_sets (from tensorflow.contrib.learn.python.learn.dat

, y_train), (x_test, y_test) = mnist.load_data()对数据进行预处理：pythonCopy codex_train = x_train / 255.0x_test..., y_train, epochs=5)model.evaluate(x_test, y_test)通过以上步骤，我们已经成功解决了read_data_sets函数被弃用的问题，并且使用了新的方式来读取..., y_train), (x_test, y_test) = mnist.load_data()# 数据预处理x_train = x_train / 255.0x_test = x_test / 255.0...# 创建数据集对象train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))test_dataset = tf.data.Dataset.from_tensor_slices...((x_test, y_test))# 打乱数据集并设置批次大小batch_size = 32train_dataset = train_dataset.shuffle(10000).batch(batch_size

3312 0

用线性回归和LSTM做股价预测

, X_test, Y_train, Y_test = train_test_split(X, y, test_size=test_size) response = [X_train,X_test..., X_test, Y_train, Y_test , X_lately =prepare_data(df,forecast_col,forecast_out,test_size) model = linear_model.LinearRegression...(); model.fit(X_train,Y_train); score = model.score(X_test,Y_test); score # 0.9913674520169482...y_test_predict = learner.predict(X_test) plt.plot(y_test_predict) plt.plot(Y_test) ?...y_train.append(scaled_data[i,0]) x_train, y_train = np.array(x_train), np.array(y_train) x_train = np.reshape

1.5K2 0

机器学习之鸢尾花-朴素贝叶斯方法

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4, random_state=42) x_min, x_max...------- # test_LogisticRegression(X_train,X_test,y_train,y_test) # test_LogisticRegression_C(...X_train, X_test, y_train, y_test) # test_LogisticRegression_Cpenaltyssolvers(X_train, X_test, y_train...(X_train, X_test, y_train, y_test) # test_LogisticRegression_multi_classssolvers(X_train, X_test,...y_train, y_test) # test_LogisticRegression_best(X_train, X_test, y_train, y_test) # --------

1.3K2 0

关于新型肺炎数据分析和可视化系列笔记四-sklearn实现数据预测

多项式回归（二次曲线、三次曲线、四次曲线、五次曲线）等算法，具体到预测，主要包括算法的选择，很多时候算法的选择是通过数据的查全率查准率，训练集、测试集、检验集等上的准确率综合评估出来的，二是关于数据集的分拆，...=X[:15] X_test=X[15:19] X_predict=X[19:] y_train=y[:15] y_test=y[15:19] return X_train...,X_test,y_train,y_test,X_predict,X,y,xlabel X_train,X_test,y_train,y_test,X_predict,X,y,Xlabel=init_data...(X_train,y_train) score_test=lr.score(X_test,y_test) y_predict=lr.predict(X_test) #-----------------...--------------- lg=LogisticRegression(C=0.2) lg.fit(X_train,y_train) y_lg_predict=lg.predict(X) print

4081 0

30行代码徒手实现logistic回归

按照75/25比例划分成训练集和验证集。...,X_test,y_train,y_test = train_test_split(data,target) 二模型结构图 ?...): Y_prob = self.predict_prob(X_test) Y_test = Y_prob.copy() Y_test[Y_prob>=0.5...(alpha = 0.1,ITERNUM = 200000) clf.fit(X_train= X_train,y_train= y_train) # 绘制目标函数的迭代曲线 %matplotlib...,y_train) Y_proba = lr.predict_proba(X_test) roc_auc_score(list(y_test),list(Y_proba[:,1])) ?

5464 1

新型肺炎数据分析和可视化-sklearn实现数据预测

1.2K4 0

MLK | Keras 基础模型调参指南

,y_train),(x_test,y_test)=mnist.load_data() number=10000 x_train=x_train[0:number] y_train...=y_train[0:number] x_train=x_train.reshape(number,28*28) x_test=x_test.reshape(x_test.shape[0...(y_train,10) y_test=np_utils.to_categorical(y_test,10) x_train=x_train x_test=x_test...x_train=x_train/255 x_test=x_test/255 return (x_train,y_train),(x_test,y_test) # 调用方法 (x_train...,y_train),(x_test,y_test)=load_data() print(x_train.shape) print(x_test.shape) ''' 随便初始化一个NN模型 ''' model

1.1K2 0

模型的选择与调优

, x_test, y_train, y_test = train_test_split(df_value, df['type'], test_size=0.25) # 切割数据 # 预处理:数据标准化..., x_test, y_train, y_test = train_test_split(df_value, df['type'], test_size=0.25) # 切割数据 # 预处理:数据标准化..., x_test, y_train, y_test = train_test_split(df_value, df['type'], test_size=0.25) # 切割数据 # 预处理:数据标准化..., x_test, y_train, y_test = train_test_split(df_value, df['type'], test_size=0.25) # 切割数据 # 预处理:数据标准化...,y_train) gc_s = gc.score(x_test,y_test) print(gc.best_score_) # 显示在交叉验证中最好的结果 print(gc.best_estimator

3543 0

机器学习简单小例子：19行代码预测鸢尾花品种

import accuracy_score 读入数据 df = pd.read_csv(r"iris\YT-Django-Iris-App-3xj9B0qqps-master\iris.csv") 将数据拆分成训练集和测试集..., X_test, Y_train, Y_test = train_test_split(X,y,test_size=0.2,random_state=1) 训练数据集合测试数据集的比例是8:2 训练模型并预测...model = SVC(gamma='auto') model.fit(X_train,Y_train) predictions = model.predict(X_test) 输入数据预测 iris..., X_test, Y_train, Y_test = train_test_split(X,y,test_size=0.2,random_state=1) model = SVC(gamma='auto...') model.fit(X_train,Y_train) predictions = model.predict(X_test) print(accuracy_score(Y_test,predictions

6841 0

关于重温机器学习笔记-线性模型

4051 0

如何在CDH中使用PySpark分布式运行GridSearch算法

Python的sklearn包中GridSearch模块，能够在指定的范围内自动搜索具有不同超参数的不同模型组合，在数据量过于庞大时对于单节点的运算存在效率问题，本篇文章Fayson主要介绍如何将Python...n_samples = len(digits.images) X = digits.images.reshape((n_samples, -1)) y = digits.target # 将数据集分成训练集和测试集...X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.5, random_state=0) # 设置gridsearch...#在测试集上测试最优的模型的泛化能力. y_true, y_pred = y_test, clf.predict(X_test) print(classification_report(y_true,...X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0) # 设置gridsearch

1.3K3 0

调参必备---GridSearch网格搜索

,X_test,y_train,y_test = train_test_split(iris.data,iris.target,random_state=0) print("Size of training...,y_train) score = svm.score(X_test,y_test) if score > best_score:#找到表现最好的参数...X_trainval,X_test,y_trainval,y_test = train_test_split(iris.data,iris.target,random_state=0) X_train,...,X_test,y_train,y_test = train_test_split(iris.data,iris.target,random_state=10) grid_search.fit(X_train...print("Test set score:{:.2f}".format(grid_search.score(X_test,y_test))) print("Best parameters:{}".format

2.9K8 1

实现深度神经网络只需60行代码

按照75/25比例划分成训练集和验证集。...,X_test,y_train,y_test = train_test_split(data,target) 0 2 模型结构图 0 3 正反传播公式 0 4 NN实现代码 import numpy...): Y_prob = self.predict_prob(X_test) Y_test = Y_prob.copy() Y_test[Y_prob>=0.5]... import roc_auc_scoreY_prob = NN.predict_prob(X_test) roc_auc_score(list(y_test),list(Y_prob)) 隐藏层使用... import roc_auc_scoreY_prob = NN.predict_prob(X_test)roc_auc_score(list(y_test),list(Y_prob)) 以上试验似乎表明

5913 0

60行代码徒手实现深度神经网络

按照75/25比例划分成训练集和验证集。...,X_test,y_train,y_test = train_test_split(data,target) 二模型结构图 ?...): Y_prob = self.predict_prob(X_test) Y_test = Y_prob.copy() Y_test[Y_prob>=...import roc_auc_score Y_prob = NN.predict_prob(X_test) roc_auc_score(list(y_test),list(Y_prob)) ?...import roc_auc_score Y_prob = NN.predict_prob(X_test) roc_auc_score(list(y_test),list(Y_prob)) ?

4063 1

机器学习笔记之线性回归、岭回归、Lasso回归

if __name__ == '__main__': #获得数据集 X_train, X_test, y_train, y_test = load_data() #进行训练并且输出预测结果..., X_test, y_train, y_test = data ridgeRegression = linear_model.Ridge() ridgeRegression.fit(X_train..., X_test, y_train, y_test = load_data() #进行训练并且预测结果 #test_ridge(X_train, X_test, y_train, y_test...(X_train, X_test, y_train, y_test) show_plot(alphas, scores) 6.2 Lasso回归代码示例 import matplotlib.pyplot..., X_test, y_train, y_test = load_data() # 使用默认的alpha #test_lasso(X_train, X_test, y_train, y_test

1.5K3 0

Python3入门机器学习（四）(补)- sklearn 中使用knn算法的总结整理

回顾 1.将数据集分成训练数据集合测试数据集 2.将训练数据集进行归一化 3.使用训练数据集的均值和方差将测试数据集归一化 4.使用训练数集训练处模型 5.使用归一化后的测试数据集测试分类的准确度...2 ---- 1.将数据集分割成测试数据集合训练数据集 from sklearn.model_selection import train_test_split X_train,X_test,y_train...,y_test = train_test_split(X,y) 2.将数据集进行归一化处理 from sklearn.preprocessing import StandardScaler standardScaler...(X_train) X_test = standardScaler.transform(X_test) 3.创建一个KNeighborsClassifier 对象 from sklearn.neighbors...,y_train) 5.使用训练数据集得出分类准确度 sklearn_knn_clf.score(X_test,y_test) 6.使用我们的模型预测新的数据 y_predict = sklearn_knn_clf.predict

3.3K4 0

点击加载更多

扫码

添加站长进交流群

领取专属 10元无门槛券

手把手带您无忧上云

Tensorflow随笔(二)

Keras 3.0发布：全面拥抱 PyTorch！

逻辑回归模型_RF模型

Python的机器学习库之Sklearn快速入门1.基本概述2.入门实践3.部分结果

解决read_data_sets (from tensorflow.contrib.learn.python.learn.dat

用线性回归和LSTM做股价预测

机器学习之鸢尾花-朴素贝叶斯方法

关于新型肺炎数据分析和可视化系列笔记四-sklearn实现数据预测

30行代码徒手实现logistic回归

新型肺炎数据分析和可视化-sklearn实现数据预测

MLK | Keras 基础模型调参指南

模型的选择与调优

机器学习简单小例子：19行代码预测鸢尾花品种

关于重温机器学习笔记-线性模型

如何在CDH中使用PySpark分布式运行GridSearch算法

调参必备---GridSearch网格搜索

实现深度神经网络只需60行代码

60行代码徒手实现深度神经网络

机器学习笔记之线性回归、岭回归、Lasso回归

Python3入门机器学习（四）(补)- sklearn 中使用knn算法的总结整理

扫码

相关资讯

热门标签

活动推荐

运营活动

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐