import sklearn.datasets
from keras.models import Sequential
from keras.layers.core import Dense, Activation
import pandas as pd
from sklearn.model_selection import train_test_split

ds = sklearn.datasets.load_iris()
X = ds['data']
Y = pd.get_dummies(ds['target']).as_matrix()

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = .2)

model = Sequential([Dense(50,input_dim=4, activation='sigmoid'),Dense(3, activation='softmax')])
model.compile(loss='categorical_crossentropy', optimizer='adadelta')

model.fit(X_train, Y_train, nb_epoch=5)
proba = model.predict_proba(X_test, batch_size=32)
pred = pd.Series(proba.flatten())
true = pd.Series(Y_test.flatten())
print("Coefficient of Correlation", pred.corr(true))