Difference in result in every run of Neural network?

I have written a simple neural network (MLP Regressor), to fit simple data frame columns. To have an optimum architecture, I also defined it as a function to see whether it is converging to a pattern. But every time that I run the model, it gives me a different result than the last time that I tried, and I do not know why? Due to the fact that it is fairly difficult to make the question reproducible, I can not post the data but I can post the architecture of the network here:

def MLP(): #After 50
    nn=30
    nl=25
    a=2
    s=0
    learn=2
    learn_in=4.22220046e-05
    max_i=1000
    return nn,nl,a,s,learn,learn_in,max_i#,



def process(df):
    y = df.iloc[:,-1]
    X = df.drop(columns=['col3'])
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=27)
    return X_train, X_test, y_train, y_test

def MLPreg(x_train, y_train):#
    nn,nl,a,s,learn,learn_in,max_i=MLP()#nl,
    act=['identity', 'logistic', 'relu','tanh'] #'identity'=Linear
    activ=act[a]
    sol=['lbfgs', 'sgd', 'adam']
    solv=sol[s]
    l_r=['constant','invscaling','adaptive']
    lr=l_r[learn]
    model = MLPRegressor(hidden_layer_sizes=(nl,nn), activation=activ, solver=solv, alpha=0.00001, batch_size='auto', 
    learning_rate=lr, learning_rate_init=learn_in, power_t=0.5, max_iter=max_i, shuffle=True, random_state=None,
    tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, 
    validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10, max_fun=15000)
#     model = MLPRegressor(max_iter = 7000)
#     param_list = {hidden_layer_sizes: [(10,),(50,)], activation: [identity, tanh, relu], solver: [lbfgs, sgd, adam], alpha: [0.00005,0.0005]}
#     gridsearchcv = GridSearchCV(estimator=model, param_grid=param_list)


    model.fit(x_train, y_train)
    return model
```