Titanium is light-weight evaluator for PMML models based on NumPy.

pip install titanium==0.0.1




To install keras2pmml, simply:

$ pip install keras2pmml


Example on Iris data - for more examples see the examples folder.

from keras2pmml import keras2pmml
from sklearn.datasets import load_iris
import numpy as np
import theano
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense

iris = load_iris()
X = iris.data
y = iris.target

theano.config.floatX = 'float32'
X = X.astype(theano.config.floatX)
y = y.astype(np.int32)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)

std = StandardScaler()
X_train_scaled = std.fit_transform(X_train)
X_test_scaled = std.transform(X_test)
y_train_ohe = np_utils.to_categorical(y_train)
y_test_ohe = np_utils.to_categorical(y_test)

model = Sequential()
model.add(Dense(input_dim=X_train.shape[1], output_dim=5, activation='tanh'))
model.add(Dense(input_dim=5, output_dim=y_test_ohe.shape[1], activation='sigmoid'))
model.compile(loss='categorical_crossentropy', optimizer='sgd')
model.fit(X_train_scaled, y_train_ohe, nb_epoch=10, batch_size=1, verbose=3,
          validation_data=(X_test_scaled, y_test_ohe))

params = {
    'feature_names': ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'],
    'target_values': ['setosa', 'virginica', 'versicolor'],
    'target_name': 'specie',
    'copyright': 'Václav Čadek',
    'description': 'Simple Keras model for Iris dataset.',
    'model_name': 'Iris Model'

keras2pmml(estimator=model, transformer=std, file='keras_iris.pmml', **params)

Params explained

  • estimator: Keras model to be exported as PMML (for supported models - see bellow).
  • transformer: if provided (and it's supported - see bellow) then scaling is applied to data fields.
  • file: name of the file where the PMML will be exported.
  • feature_names: when provided and have same shape as input layer, then features will have custom names, otherwise generic names (x0,..., xn-1) will be used.
  • target_values: when provided and have same shape as output layer, then target values will have custom names, otherwise generic names (y0,..., yn-1) will be used.
  • target_name: when provided then target variable will have custom name, otherwise generic name class will be used.
  • copyright: who is the author of the model.
  • description: optional parameter that sets description within PMML document.
  • model_name: optional parameter that sets model_name within PMML document.

What is supported?

  • Models
    • keras.models.Sequential
  • Activation functions
    • tanh
    • sigmoid/logistic
  • Scalers
    • sklearn.preprocessing.StandardScaler
    • sklearn.preprocessing.MinMaxScaler


This software is licensed under MIT licence.