def _clf_mlp(trX, teX, trY, teY): print "MLP" print trX.shape, "trX shape" print "Enter Layer for MLP" layer = input() # print "enter delIdx" # delIdx=input() # while(delIdx): # trX=np.delete(trX,-1,axis=0) # trY=np.delete(trY,-1,axis=0) # delIdx=delIdx-1 print "factors", factors(trX.shape[0]) teY = teY.astype(np.int32) trY = trY.astype(np.int32) print trX.shape, "trX shape" print "enter no of mini batch" mini_batch = int(input()) mlp = TfMultiLayerPerceptron( eta=0.01, epochs=100, hidden_layers=layer, activations=['relu' for i in range(len(layer))], print_progress=3, minibatches=mini_batch, optimizer='adam', random_seed=1) mlp.fit(trX, trY) pred = mlp.predict(teX) print _f_count(teY), "test f count" pred = pred.astype(np.int32) print _f_count(pred), "pred f count" conf_mat = confusion_matrix(teY, pred) process_cm(conf_mat, to_print=True) print precision_score(teY, pred), "Precision Score" print recall_score(teY, pred), "Recall Score" print roc_auc_score(teY, pred), "ROC_AUC"
X, y = iris_data() X = X[:, [0, 3]] # standardize training data X_std = (X - X.mean(axis=0)) / X.std(axis=0) print X_std # Gradient Descent nn1 = TfMultiLayerPerceptron(eta=0.5, epochs=20, hidden_layers=[10], activations=['logistic'], optimizer='gradientdescent', print_progress=3, minibatches=1, random_seed=1) nn1 = nn1.fit(X_std, y) fig = plot_decision_regions(X=X_std, y=y, clf=nn1, legend=2) plt.title('Multi-layer perception w. 1 hidden layer (logistic sigmod)') plt.show() plt.plot(range(len(nn1.cost_)), nn1.cost_) plt.ylabel("Cost") plt.xlabel("Epochs") plt.show()