tool/python/examples/mnist_mlp.py

#!/usr/bin/env python
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import sys, os
sys.path.append(os.path.join(os.path.dirname(__file__),'..'))
from singa.model import *
from examples.datasets import mnist

# Sample parameter values for Mnist MLP example
pvalues = {'batchsize' : 64, 'shape' : 784, 'random_skip' : 5000,
           'std_value' : 127.5, 'mean_value' : 127.5}
X_train, X_test, workspace = mnist.load_data(**pvalues)

m = Sequential('mlp', argv=sys.argv)

''' Weight and Bias are initialized by
    uniform distribution with scale=0.05 at default
'''
m.add(Dense(2500, init='uniform', activation='tanh'))
m.add(Dense(2000, init='uniform', activation='tanh'))
m.add(Dense(1500, init='uniform', activation='tanh'))
m.add(Dense(1000, init='uniform', activation='tanh'))
m.add(Dense(500,  init='uniform', activation='tanh'))
m.add(Dense(10, init='uniform', activation='softmax'))

sgd = SGD(lr=0.001, lr_type='step')
topo = Cluster(workspace)
m.compile(loss='categorical_crossentropy', optimizer=sgd, cluster=topo)

m.fit(X_train, nb_epoch=100, with_test=True)
result = m.evaluate(X_test, batch_size=100, test_steps=10)

#e.g., display result
#for k, v in sorted(result.items(), key=lambda x: x[0]):
#  print k, v