diabetes_dense.py

model=Sequential()
model.add(Dense(64,input_dim=train_x.shape[1]))
model.add(Dense(64))
model.add(Dense(64))
model.add(Dense(64))
model.add(Dense(64,))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['acc'])
model.summary()


his=model.fit(train_x,train_y,epochs=50,batch_size=1,verbose=1)

predict=model.predict(test_x)
def predictions(predict):
    for i in predict:
        if i>0.5:
            return 1
        else:
            return 0
predictions=list(map(predictions,predict))
print('Recall Score --> ',recall_score(test_y,predictions))
print("Classification Report\n",classification_report(test_y,predictions))