Training model using optimum values of hyperparameters

optimum values of hyperparameters.py

from sklearn.metrics import classification_report, accuracy_score

# Defining the model

def create_model():
    model = Sequential()
    model.add(Dense(16,input_dim = 8,kernel_initializer = 'uniform',activation = 'tanh'))
    model.add(Dropout(0.1))
    model.add(Dense(4,input_dim = 16,kernel_initializer = 'uniform',activation = 'tanh'))
    model.add(Dropout(0.1))
    model.add(Dense(1,activation = 'sigmoid'))
    
    adam = Adam(lr = 0.001)
    model.compile(loss = 'binary_crossentropy',optimizer = adam,metrics = ['accuracy'])
    return model

# Create the model

model = KerasClassifier(build_fn = create_model,verbose = 0,batch_size = 40,epochs = 10)

# Fitting the model

model.fit(X_standardized,y)

# Predicting using trained model

y_predict = model.predict(X_standardized)

# Printing the metrics

print(accuracy_score(y,y_predict))
print(classification_report(y,y_predict))