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()
