return effective_rank
print "Rank: {}, Hidden Layer Size: {}".format(compute_effective_rank(svals), svals.shape[0])
if isinstance(batch_size, int):
if batch_size == 1:
fig_outfile = 'perf_mlp_seque.png'
else:
fig_outfile = 'perf_mlp_minibatchsize_%d.png' % batch_size
else:
fig_outfile = 'perf_mlp_batch.png'
if raw_input("Shall we save this model? (y/n)\n") == 'y':
model_outfile = fig_outfile.split('.')[0] + ".pkl"
fobj = open(model_outfile, 'wb')
cPickle.dump(predict, fobj, protocol=cPickle.HIGHEST_PROTOCOL)
fobj.close()
if raw_input('Save training figure? (y/n): \n') == 'y':
performanceplot(cost_record, tr_err_record, te_err_record, "contrast_" + fig_outfile)
else:
model_outfile = raw_input("Provide path to model_outfile: \n")
fobj = open(model_outfile, 'rb')
cPickle.load(fobj)
fobj.close()
if raw_input("Perform failure analysis? (y/n):\n") == 'y':
failure_analysis.investigate_mlp(teX, teY, predict(teX) > 0.5)
batch_size=1; learning_rate=0.001; # sequential mode: single example
cost = T.mean(Y - y_pred)
update = [[w_o, w_o + learning_rate*cost*T.transpose(X)]]
train = theano.function(inputs=[X, Y], outputs=cost, updates=update, allow_input_downcast=True)
predict = theano.function(inputs=[X], outputs=y_pred, allow_input_downcast=True)
print "batch_size: ", batch_size
print "learning_rate: ", learning_rate
cost_record = []
train_error_record = []
test_error_record = []
for epoch in range(100):
if isinstance(batch_size, int):
for start, end in zip(range(0, len(trX), batch_size), range(batch_size, len(trX), batch_size)):
cost = train(trX[start:end], trY[start:end])
else:
cost = train(trX, trY)
cost_record.append(cost)
if epoch % 1 == 0:
train_error = 1-np.mean(np.sign(trY)== predict(trX))
test_error = 1-np.mean(np.sign(teY)== predict(teX))
train_error_record.append(train_error)
test_error_record.append(test_error)
print "%d,%0.4f,%0.4f" % (epoch, train_error, test_error)
trX, trY = permute(trX, trY)
performanceplot(cost_record, train_error_record, test_error_record, 'contrast_perf_perceptron.png')
updates=update,
allow_input_downcast=True)
predict = theano.function(inputs=[X],
outputs=y_pred,
allow_input_downcast=True)
print "batch_size: ", batch_size
print "learning_rate: ", learning_rate
cost_record = []
train_error_record = []
test_error_record = []
for epoch in range(100):
if isinstance(batch_size, int):
for start, end in zip(range(0, len(trX), batch_size),
range(batch_size, len(trX), batch_size)):
cost = train(trX[start:end], trY[start:end])
else:
cost = train(trX, trY)
cost_record.append(cost)
if epoch % 1 == 0:
train_error = 1 - np.mean(np.sign(trY) == predict(trX))
test_error = 1 - np.mean(np.sign(teY) == predict(teX))
train_error_record.append(train_error)
test_error_record.append(test_error)
print "%d,%0.4f,%0.4f" % (epoch, train_error, test_error)
trX, trY = permute(trX, trY)
performanceplot(cost_record, train_error_record, test_error_record,
'contrast_perf_perceptron.png')
print "Rank: {}, Hidden Layer Size: {}".format(
compute_effective_rank(svals), svals.shape[0])
if isinstance(batch_size, int):
if batch_size == 1:
fig_outfile = 'perf_mlp_seque.png'
else:
fig_outfile = 'perf_mlp_minibatchsize_%d.png' % batch_size
else:
fig_outfile = 'perf_mlp_batch.png'
if raw_input("Shall we save this model? (y/n)\n") == 'y':
model_outfile = fig_outfile.split('.')[0] + ".pkl"
fobj = open(model_outfile, 'wb')
cPickle.dump(predict, fobj, protocol=cPickle.HIGHEST_PROTOCOL)
fobj.close()
if raw_input('Save training figure? (y/n): \n') == 'y':
performanceplot(cost_record, tr_err_record, te_err_record,
"contrast_" + fig_outfile)
else:
model_outfile = raw_input("Provide path to model_outfile: \n")
fobj = open(model_outfile, 'rb')
cPickle.load(fobj)
fobj.close()
if raw_input("Perform failure analysis? (y/n):\n") == 'y':
failure_analysis.investigate_mlp(teX, teY, predict(teX) > 0.5)
