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: 珲春市| 息烽县| 湛江市| 额敏县| 板桥市| 剑河县| 监利县| 榆树市| 明星| 龙陵县| 长春市| 崇左市| 抚州市| 武功县| 池州市| 封丘县| 五家渠市| 高安市| 甘德县| 合肥市| 儋州市| 金川县| 平定县| 普宁市| 绥德县| 吉木乃县| 调兵山市| 滕州市| 南汇区| 罗定市| 酒泉市| 镇巴县| 云安县| 托克托县| 白城市| 长白| 甘谷县| 临澧县| 岳阳县| 南阳市| 龙井市|