| #!/usr/bin/env python |
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| #* |
| #* Licensed to the Apache Software Foundation (ASF) under one |
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| #* distributed with this work for additional information |
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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, |
| 'std_value' : 127.5, 'mean_value' : 127.5} |
| X_train, X_test, workspace = mnist.load_data(**pvalues) |
| |
| m = Sequential('mlp', argv=sys.argv) |
| |
| 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) |
| |
| ''' For doing test only, normally users sets checkpoint path |
| e.g., assume that checkpoint exists by |
| m.fit(X_train, nb_epoch=100, checkpoint_freq=100) |
| ''' |
| path = workspace+'/checkpoint/step100-worker0' |
| result = m.evaluate(X_test, batch_size=100, test_steps=100, checkpoint_path=path) |