# grab the MNIST dataset
mnist = MNIST()
# create the softmax classifier
s = SoftmaxLayer(input_size=28 * 28, output_size=10, out_as_probs=False)
# make an optimizer to train it (AdaDelta is a good default)
optimizer = AdaDelta(model=s, dataset=mnist, n_epoch=20)
# perform training!
optimizer.train()
# test it on some images!
test_data = mnist.getDataByIndices(indices=range(25), subset=TEST)
# use the predict function!
preds = s.predict(test_data)
print '-------'
print preds
print mnist.getLabelsByIndices(indices=range(25), subset=TEST)
print
print
del mnist
del s
del optimizer
log.info("Creating softmax with categorical cross-entropy!")
# grab the MNIST dataset
mnist = MNIST(one_hot=True)
# create the softmax classifier
s = SoftmaxLayer(input_size=28*28, output_size=10, cost='categorical_crossentropy', out_as_probs=True)
# make an optimizer to train it (AdaDelta is a good default)
optimizer = AdaDelta(model=s, dataset=mnist, n_epoch=20)
# perform training!
# although this is recommended over print statements everywhere
import logging
import opendeep.log.logger as logger
logger.config_root_logger()
log = logging.getLogger(__name__)
log.info("Creating MLP!")
# grab the MNIST dataset
mnist = MNIST()
# create the basic layer
layer1 = BasicLayer(input_size=28*28, output_size=1000, activation='relu')
# create the softmax classifier
layer2 = SoftmaxLayer(inputs_hook=(1000, layer1.get_outputs()), output_size=10, out_as_probs=False)
# create the mlp from the two layers
mlp = Prototype(layers=[layer1, layer2])
# make an optimizer to train it (AdaDelta is a good default)
optimizer = AdaDelta(model=mlp, dataset=mnist, n_epoch=20)
# perform training!
optimizer.train()
# test it on some images!
test_data = mnist.getDataByIndices(indices=range(25), subset=TEST)
# use the predict function!
preds = mlp.predict(test_data)
print '-------'
print preds
print mnist.getLabelsByIndices(indices=range(25), subset=TEST).astype('int32')
print
print
del mnist
del mlp
del optimizer