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- # Create your first MLP in Keras
- 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("pima-indians-diabetes.data.csv", delimiter=",")
- # 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))
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