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 autoencoder_results(hidden_units):
"""
Train an autoencoder using the MNIST training data and plot the
results when the first ten MNIST test images are passed through
the autoencoder.
"""
training_data, test_inputs, actual_test_results = \
mnist_loader.load_data_nn()
net = train_autoencoder(hidden_units, training_data)
plot_test_results(net, test_inputs)
def autoencoder_results(hidden_units):
"""
Train an autoencoder using the MNIST training data and plot the
results when the first ten MNIST test images are passed through
the autoencoder.
"""
training_data, test_inputs, actual_test_results = \
mnist_loader.load_data_nn()
net = train_autoencoder(hidden_units, training_data)
plot_test_results(net, test_inputs)
Use PCA to reconstruct some of the MNIST test digits. """ # My libraries import mnist_loader # Third-party libraries import matplotlib import matplotlib.pyplot as plt import numpy as np from sklearn.decomposition import RandomizedPCA # Training training_data, test_inputs, actual_test_results = mnist_loader.load_data_nn() pca = RandomizedPCA(n_components=30) nn_images = [x for (x, y) in training_data] pca_images = np.concatenate(nn_images, axis=1).transpose() pca_r = pca.fit(pca_images) # Try PCA on first ten test images test_images = np.array(test_inputs[:10]).reshape((10,784)) test_outputs = pca_r.inverse_transform(pca_r.transform(test_images)) # Plot the first ten test images and the corresponding outputs fig = plt.figure() ax = fig.add_subplot(111) images_in = [test_inputs[j].reshape(-1, 28) for j in range(10)] images_out = [test_outputs[j].reshape(-1, 28) for j in range(10)] image_in = np.concatenate(images_in, axis=1)
import mnist_loader
# Third-party libraries
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
#### Parameters
# Size of the training sets. May range from 1000 to 12,500. Lower
# will be faster, higher will give more accuracy.
SIZE = 5000
# Number of hidden units in the autoencoder
HIDDEN = 30
print("\nGenerating training data")
training_data, _, _ = mnist_loader.load_data_nn()
td_1 = [(x, x) for x, _ in training_data[0:SIZE]]
td_2 = [(x, x) for x, _ in training_data[12500:12500 + SIZE]]
td_3 = [x for x, _ in training_data[25000:25000 + SIZE]]
test = [x for x, _ in training_data[37500:37500 + SIZE]]
print("\nFinding first autoencoder")
ae_1 = Network([784, HIDDEN, 784])
ae_1.SGD(td_1, 4, 10, 0.01, 0.05)
print("\nFinding second autoencoder")
ae_2 = Network([784, HIDDEN, 784])
ae_2.SGD(td_1, 4, 10, 0.01, 0.05)
print("\nGenerating encoded training data")
encoded_td_1 = [
# Third-party libraries import matplotlib import matplotlib.pyplot as plt import numpy as np #### Parameters # Size of the training sets. May range from 1000 to 12,500. Lower # will be faster, higher will give more accuracy. SIZE = 5000 # Number of hidden units in the autoencoder HIDDEN = 30 print "\nGenerating training data" training_data, _, _ = mnist_loader.load_data_nn() td_1 = [(x, x) for x, _ in training_data[0:SIZE]] td_2 = [(x, x) for x, _ in training_data[12500:12500+SIZE]] td_3 = [x for x, _ in training_data[25000:25000+SIZE]] test = [x for x, _ in training_data[37500:37500+SIZE]] print "\nFinding first autoencoder" ae_1 = Network([784, HIDDEN, 784]) ae_1.SGD(td_1, 4, 10, 0.01, 0.05) print "\nFinding second autoencoder" ae_2 = Network([784, HIDDEN, 784]) ae_2.SGD(td_1, 4, 10, 0.01, 0.05) print "\nGenerating encoded training data" encoded_td_1 = [sigmoid_vec(np.dot(ae_1.weights[0], x)+ae_1.biases[0])
~~~~~~~~~ Use PCA to reconstruct some of the MNIST test digits. """ # My libraries import mnist_loader # Third-party libraries import matplotlib import matplotlib.pyplot as plt import numpy as np from sklearn.decomposition import RandomizedPCA # Training training_data, test_inputs, actual_test_results = mnist_loader.load_data_nn() pca = RandomizedPCA(n_components=30) nn_images = [x for (x, y) in training_data] pca_images = np.concatenate(nn_images, axis=1).transpose() pca_r = pca.fit(pca_images) # Try PCA on first ten test images test_images = np.array(test_inputs[:10]).reshape((10, 784)) test_outputs = pca_r.inverse_transform(pca_r.transform(test_images)) # Plot the first ten test images and the corresponding outputs fig = plt.figure() ax = fig.add_subplot(111) images_in = [test_inputs[j].reshape(-1, 28) for j in range(10)] images_out = [test_outputs[j].reshape(-1, 28) for j in range(10)] image_in = np.concatenate(images_in, axis=1)
