I ran the following notebook in a docker container with the following commands:

docker pull bethgelab/jupyter-torch:ubuntu-14.04
docker run -it -v `pwd`:/space/ -p 8888:8888 --name keras -w /space/ --rm bethgelab/jupyter-torch:ubuntu-14.04 jupyter notebook --ip=0.0.0.0

The following code is adapted from https://github.com/fastforwardlabs/keras-hello-world/blob/master/kerashelloworld.ipynb



In [1]:

    
%matplotlib inline
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









    



/usr/local/lib/python2.7/dist-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.
  "`IPython.html.widgets` has moved to `ipywidgets`.", ShimWarning)
Using Theano backend.



In [2]:

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









    Out[2]:






  
    
      
      sepal_length
      sepal_width
      petal_length
      petal_width
      species
    
  
  
    
      0
      5.1
      3.5
      1.4
      0.2
      setosa
    
    
      1
      4.9
      3.0
      1.4
      0.2
      setosa
    
    
      2
      4.7
      3.2
      1.3
      0.2
      setosa
    
    
      3
      4.6
      3.1
      1.5
      0.2
      setosa
    
    
      4
      5.0
      3.6
      1.4
      0.2
      setosa



In [3]:

    
sns.pairplot(iris, hue='species');









    



/usr/local/lib/python2.7/dist-packages/matplotlib/font_manager.py:1314: UserWarning: findfont: Font family [u'sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))



In [4]:

    
X = iris.values[:, :4]
y = iris.values[:, 4]



In [5]:

    
train_X, test_X, train_y, test_y = train_test_split(X, y, train_size=0.5, random_state=0)



In [6]:

    
lr = LogisticRegressionCV()
lr.fit(train_X, train_y)









    Out[6]:





LogisticRegressionCV(Cs=10, class_weight=None, cv=None, dual=False,
           fit_intercept=True, intercept_scaling=1.0, max_iter=100,
           multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,
           refit=True, scoring=None, solver='lbfgs', tol=0.0001, verbose=0)



In [7]:

    
print("Accuracy = {:.2f}".format(lr.score(test_X, test_y)))









    



Accuracy = 0.83



In [8]:

    
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)



In [9]:

    
from keras.layers.core import Dropout



In [10]:

    
model = Sequential()



In [11]:

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



In [12]:

    
model.add(Dense(3))
model.add(Dropout(0.01))
model.add(Activation('softmax'))



In [13]:

    
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=["accuracy"])
model.fit(train_X, train_y_ohe, nb_epoch=1000, batch_size=1, verbose=0)
loss, accuracy = model.evaluate(test_X, test_y_ohe, verbose=0)
print("Accuracy = {:.2f}".format(accuracy))









    



Accuracy = 0.97



In [ ]:

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa