官术网_书友最值得收藏!

Putting it all together

We will be using the diabetes dataset from Pima Indians.

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females, at least 21 years old, and of Pima Indian heritage. The datasets consist of several medical predictor variables and one target variable, outcome. Predictor variables include the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.
from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
numpy.random.seed(7)
# load pima indians dataset
dataset = numpy.loadtxt("data/diabetes.csv", delimiter=",", skiprows=1)
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, epochs=150, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

The dataset shape is (768, 9).

Let's look at the value of the dataset:

Values of X, which is columns 0 to 7:

The value of Y is the 8th column of the dataset, as shown in the following screenshot:

主站蜘蛛池模板: 东兴市| 偃师市| 蛟河市| 萨迦县| 万州区| 新龙县| 新平| 金沙县| 龙门县| 乌兰察布市| 泗阳县| 安新县| 甘孜县| 正镶白旗| 图们市| 阜城县| 威远县| 克什克腾旗| 渝中区| 阿合奇县| 德阳市| 中阳县| 公安县| 奇台县| 阿坝县| 布尔津县| 喀喇| 全椒县| 西宁市| 宁波市| 上蔡县| 大英县| 开平市| 岳池县| 盐津县| 镇巴县| 米泉市| 大足县| 喜德县| 四子王旗| 繁峙县|