Just the code from Keras Helloworld

helloworld.py

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LogisticRegressionCV

from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.utils import np_utils

iris = sns.load_dataset("iris")
iris.head()

sns.pairplot(iris, hue='species');
X = iris.values[:, :4]
y = iris.values[:, 4]

train_X, test_X, train_y, test_y = train_test_split(X, y, train_size=0.5, random_state=0)
lr = LogisticRegressionCV()
lr.fit(train_X, train_y)

print("Accuracy = {:.2f}".format(lr.score(test_X, test_y)))
def one_hot_encode_object_array(arr):
    '''One hot encode a numpy array of objects (e.g. strings)'''
    uniques, ids = np.unique(arr, return_inverse=True)
    return np_utils.to_categorical(ids, len(uniques))

train_y_ohe = one_hot_encode_object_array(train_y)
test_y_ohe = one_hot_encode_object_array(test_y)

model = Sequential()
model.add(Dense(16, input_shape=(4,)))
model.add(Activation('sigmoid'))

model.add(Dense(3))
model.add(Activation('softmax'))

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=["accuracy"])
time model.fit(train_X, train_y_ohe, nb_epoch=100, batch_size=1, verbose=0)