visualize features

Author: lazyprogrammer

unsupervised_class2/visualize_features.py

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+import numpy as np
+import theano
+import theano.tensor as T
+import matplotlib.pyplot as plt
+
+from sklearn.utils import shuffle
+from theano.tensor.shared_randomstreams import RandomStreams
+from util import relu, error_rate, getKaggleMNIST, init_weights
+from unsupervised import DBN
+from rbm import RBM
+
+
+def main(loadfile=None, savefile=None):
+    Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()
+    if loadfile:
+        dbn = DBN([1000, 750, 500, 10]) # AutoEncoder is default
+        dbn = DBN([1000, 750, 500, 10], UnsupervisedModel=RBM)
+        dbn.fit(Xtrain, pretrain_epochs=15)
+    else:
+        dbn.load(loadfile)
+
+    if savefile:
+        dbn.save(savefile)
+
+    # initial weight is D x M
+    # W = dbn.hidden_layers[0].W.eval()
+    # for i in xrange(dbn.hidden_layers[0].M):
+    #     imgplot = plt.imshow(W[:,i].reshape(28, 28), cmap='gray')
+    #     plt.show()
+    #     should_quit = raw_input("Show more? Enter 'n' to quit\n")
+    #     if should_quit == 'n':
+    #         break
+
+    # TODO: save the weights so I can initialize from them later
+    #       and just do the last step
+
+    # print features learned in the last layer
+    for k in xrange(dbn.hidden_layers[-1].M):
+        # activate the kth node
+        X = dbn.fit_to_input(k)
+        imgplot = plt.imshow(X.reshape(28, 28), cmap='gray')
+        plt.show()
+        should_quit = raw_input("Show more? Enter 'n' to quit\n")
+        if should_quit == 'n':
+            break
+
+
+if __name__ == '__main__':
+    main()