def _doTest (self, W, X, model, queryState, trainPlan):
D,_ = W.shape
recons = queryState.means.dot(model.vocab)
reconsErr = 1./D * np.sum((np.asarray(W.todense()) - recons) * (np.asarray(W.todense()) - recons))
print ("Initial bound is %f\n\n" % ctm.var_bound(W, model, queryState))
print ("Initial reconstruction error is %f\n\n" % reconsErr)
model, query, (bndItrs, bndVals) = stm.train (W, X, model, queryState, trainPlan)
# Plot the bound
plt.plot(bndItrs[5:], bndVals[5:])
plt.xlabel("Iterations")
plt.ylabel("Variational Bound")
plt.show()
# Plot the vocab
ones = np.ones((3,3))
for k in range(model.K):
plt.subplot(2, 3, k)
plt.imshow(ones - model.vocab[k,:].reshape((3,3)), interpolation="none", cmap = cm.Greys_r)
plt.show()
recons = queryState.means.dot(model.vocab)
reconsErr = 1./D * np.sum((np.asarray(W.todense()) - recons) * (np.asarray(W.todense()) - recons))
print ("Final reconstruction error is %f\n\n" % reconsErr)
def _doTest (self, W, X, model, queryState, trainPlan):
D,_ = W.shape
recons = queryState.means.dot(model.vocab)
reconsErr = 1./D * np.sum((np.asarray(W.todense()) - recons) * (np.asarray(W.todense()) - recons))
print ("Initial bound is %f\n\n" % ctm.var_bound(W, model, queryState))
print ("Initial reconstruction error is %f\n\n" % reconsErr)
model, query, (bndItrs, bndVals, bndLikes) = stm.train (W, X, model, queryState, trainPlan)
# Plot the evolution of the bound during training.
fig, ax1 = plt.subplots()
ax1.plot(bndItrs, bndVals, 'b-')
ax1.set_xlabel('Iterations')
ax1.set_ylabel('Bound', color='b')
ax2 = ax1.twinx()
ax2.plot(bndItrs, bndLikes, 'r-')
ax2.set_ylabel('Likelihood', color='r')
fig.show()
plt.show()
# Plot the vocabulary
ones = np.ones((3,3))
for k in range(model.K):
plt.subplot(2, 3, k)
plt.imshow(ones - model.vocab[k,:].reshape((3,3)), interpolation="none", cmap = cm.Greys_r)
plt.show()
recons = queryState.means.dot(model.vocab)
reconsErr = 1./D * np.sum((np.asarray(W.todense()) - recons) * (np.asarray(W.todense()) - recons))
print ("Final reconstruction error is %f\n\n" % reconsErr)
def _doTest (self, W, model, queryState, trainPlan):
D,_ = W.shape
recons = rowwise_softmax(queryState.means).dot(model.vocab)
reconsErr = 1./D * np.sum((np.asarray(W.todense()) - recons) * (np.asarray(W.todense()) - recons))
print ("Initial bound is %f\n\n" % ctm.var_bound(W, model, queryState))
print ("Initial reconstruction error is %f\n\n" % reconsErr)
model, query, (bndItrs, bndVals) = ctm.train (W, None, model, queryState, trainPlan)
# Plot the bound
plt.plot(bndItrs[5:], bndVals[5:])
plt.xlabel("Iterations")
plt.ylabel("Variational Bound")
plt.show()
# Plot the inferred vocab
plt.imshow(model.vocab, interpolation="none", cmap = cm.Greys_r)
plt.show()
recons = rowwise_softmax(queryState.means).dot(model.vocab)
reconsErr = 1./D * np.sum((np.asarray(W.todense()) - recons) * (np.asarray(W.todense()) - recons))
print ("Final reconstruction error is %f\n\n" % reconsErr)
