eachwith tanh activation function finallywe can also control the complexity of neural network by using an penalty to shrink the weights toward zeroas we did in ridge regression and the linear classifiers the parameter for this in the mlpclassifier is alpha (as in the linear regression models)and it' set to very low value (little regularizationby default figure - shows the effect of different values of alpha on the two_moons datasetusing two hidden layers of or units eachin[ ]figaxes plt subplots( figsize=( )for axxn_hidden_nodes in zip(axes[ ])for axalpha in zip(axx[ ])mlp mlpclassifier(algorithm=' -bfgs'random_state= hidden_layer_sizes=[n_hidden_nodesn_hidden_nodes]alpha=alphamlp fit(x_trainy_trainmglearn plots plot_ d_separator(mlpx_trainfill=truealpha ax=axmglearn discrete_scatter(x_train[: ]x_train[: ]y_trainax=axax set_title("n_hidden=[{}{}]\nalpha={ }formatn_hidden_nodesn_hidden_nodesalpha) supervised learning