def classifier(hidden_units, n_unlabeled_inputs, n_labeled_inputs):
"""
Train a semi-supervised classifier. We begin with pretraining,
creating an autoencoder which uses ``n_unlabeled_inputs`` from the
MNIST training data. This is then converted into a classifier
which is fine-tuned using the ``n_labeled_inputs``.
For comparison a classifier is also created which does not make
use of the unlabeled data.
"""
training_data, test_inputs, actual_test_results = \
mnist_loader.load_data_nn()
print "\nUsing pretraining and %s items of unlabeled data" %\
n_unlabeled_inputs
net_ae = train_autoencoder(hidden_units, training_data[:n_unlabeled_inputs])
net_c = Network([784, hidden_units, 10])
net_c.biases = net_ae.biases[:1]+[np.random.randn(10, 1)/np.sqrt(10)]
net_c.weights = net_ae.weights[:1]+\
[np.random.randn(10, hidden_units)/np.sqrt(10)]
net_c.SGD(training_data[-n_labeled_inputs:], 300, 10, 0.01, 0.05)
print "Result on test data: %s / %s" % (
net_c.evaluate(test_inputs, actual_test_results), len(test_inputs))
print "Training a network with %s items of training data" % n_labeled_inputs
net = Network([784, hidden_units, 10])
net.SGD(training_data[-n_labeled_inputs:], 300, 10, 0.01, 0.05)
print "Result on test data: %s / %s" % (
net.evaluate(test_inputs, actual_test_results), len(test_inputs))
return net_c
def classifier(hidden_units, n_unlabeled_inputs, n_labeled_inputs):
"""
Train a semi-supervised classifier. We begin with pretraining,
creating an autoencoder which uses ``n_unlabeled_inputs`` from the
MNIST training data. This is then converted into a classifier
which is fine-tuned using the ``n_labeled_inputs``.
For comparison a classifier is also created which does not make
use of the unlabeled data.
"""
training_data, test_inputs, actual_test_results = \
mnist_loader.load_data_nn()
print "\nUsing pretraining and %s items of unlabeled data" %\
n_unlabeled_inputs
net_ae = train_autoencoder(hidden_units,
training_data[:n_unlabeled_inputs])
net_c = Network([784, hidden_units, 10])
net_c.biases = net_ae.biases[:1] + [np.random.randn(10, 1) / np.sqrt(10)]
net_c.weights = net_ae.weights[:1]+\
[np.random.randn(10, hidden_units)/np.sqrt(10)]
net_c.SGD(training_data[-n_labeled_inputs:], 300, 10, 0.01, 0.05)
print "Result on test data: %s / %s" % (net_c.evaluate(
test_inputs, actual_test_results), len(test_inputs))
print "Training a network with %s items of training data" % n_labeled_inputs
net = Network([784, hidden_units, 10])
net.SGD(training_data[-n_labeled_inputs:], 300, 10, 0.01, 0.05)
print "Result on test data: %s / %s" % (net.evaluate(
test_inputs, actual_test_results), len(test_inputs))
return net_c
def unroll(deep_autoencoder):
"""
Return a Network that contains the compression stage of the
``deep_autoencoder``."""
net = Network(deep_autoencoder.layers)
net.weights = deep_autoencoder.weights[:len(deep_autoencoder.layers)-1]
net.biases = deep_autoencoder.biases[:len(deep_autoencoder.layers)-1]
return net
