def runMain():
(ti, sparseX) = expt.setExptParams()
X = sparseX.todense()
# compile all the functions we'll need before I set up the session
tlog = simple.Compiler(db=ti.db, prog=ti.prog, autoset_db_params=False)
for modeString in [
"t_stress/io", "t_influences/io", "t_cancer_spont/io",
"t_cancer_smoke/io"
]:
_ = tlog.inference(modeString)
session = tf.Session()
session.run(tf.global_variables_initializer())
start0 = time.time()
for modeString in [
"t_stress/io", "t_influences/io", "t_cancer_spont/io",
"t_cancer_smoke/io"
]:
session.run(tf.global_variables_initializer())
print 'eval', modeString,
fd = {tlog.input_placeholder_name(modeString): X}
session.run(tlog.inference(modeString), feed_dict=fd)
print 'time', time.time() - start0, 'sec'
tot = time.time() - start0
print 'total time', tot, 'sec'
return tot
def runMain():
(db, prog, modeSet, queries) = expt.setExptParams()
tlog = simple.Compiler(db=db, prog=prog, autoset_db_params=False)
fps1 = expt.compileAll(db, prog, modeSet, queries)
fps2 = tfCompileAll(tlog, modeSet, queries) # expect <= 2.5 fps
qps = runTF(tlog) # expect less than 23 qps
return fps2, qps
def runMain(num=250):
params = expt.setExptParams(num)
prog = params['prog']
tlog = simple.Compiler(db=prog.db, prog=prog, autoset_db_params=False)
train_data = tlog.load_big_dataset('inputs/train-%d.exam' % num)
mode = params['targetMode']
loss = tlog.loss(mode)
optimizer = tf.train.AdagradOptimizer(0.1)
train_step = optimizer.minimize(loss)
session = tf.Session()
session.run(tf.global_variables_initializer())
t0 = time.time()
epochs = 10
for i in range(epochs):
b = 0
for (_,(TX,TY)) in tlog.minibatches(train_data,batch_size=125):
print 'epoch',i+1,'of',epochs,'minibatch',b+1
train_fd = {tlog.input_placeholder_name(mode):TX, tlog.target_output_placeholder_name(mode):TY}
session.run(train_step, feed_dict=train_fd)
b += 1
print 'learning time',time.time()-t0,'sec'
predicted_y = tlog.inference(mode)
actual_y = tlog.target_output_placeholder(mode)
correct_predictions = tf.equal(tf.argmax(actual_y,1), tf.argmax(predicted_y,1))
accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))
test_data = tlog.load_small_dataset('inputs/test-%d.exam' % num)
UX,UY = test_data[mode]
test_fd = {tlog.input_placeholder_name(mode):UX, tlog.target_output_placeholder_name(mode):UY}
acc = session.run(accuracy, feed_dict=test_fd)
print 'test acc',acc
return acc #expect 27.2
def runMain(saveInPropprFormat=True):
params = expt.setExptParams()
prog = params['prog']
tlog = simple.Compiler(db=prog.db, prog=prog, autoset_db_params=False)
train_data = tlog.load_small_dataset('inputs/train.examples')
test_data = tlog.load_small_dataset('inputs/test.examples')
mode = 'samebib/io'
TX, TY = train_data[mode]
UX, UY = test_data[mode]
loss = tlog.loss(mode)
optimizer = tf.train.AdagradOptimizer(0.1)
train_step = optimizer.minimize(loss)
train_fd = {
tlog.input_placeholder_name(mode): TX,
tlog.target_output_placeholder_name(mode): TY
}
test_fd = {
tlog.input_placeholder_name(mode): UX,
tlog.target_output_placeholder_name(mode): UY
}
t0 = time.time()
session = tf.Session()
session.run(tf.global_variables_initializer())
epochs = 30
for i in range(epochs):
# progress
print 'epoch', i + 1, 'of', epochs
session.run(train_step, feed_dict=train_fd)
print 'learning time', time.time() - t0, 'sec'
inference = tlog.inference(mode)
predicted_y = session.run(inference, feed_dict=test_fd)
actual_y = tlog.target_output_placeholder(mode)
correct_predictions = tf.equal(tf.argmax(actual_y, 1),
tf.argmax(predicted_y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))
if saveInPropprFormat:
# save test results in ProPPR format
from tensorlog import declare
from tensorlog import dataset
from tensorlog import expt as tlog_expt
m = declare.asMode(mode)
native_test_data = dataset.Dataset({m: tlog.xc._unwrapOutput(UX)},
{m: tlog.xc._unwrapOutput(UY)})
savedTestExamples = 'tmp-cache/cora-test.examples'
savedTestPredictions = 'tmp-cache/cora-test.solutions.txt'
native_test_data.saveProPPRExamples(savedTestExamples, tlog.db)
tlog_expt.Expt.predictionAsProPPRSolutions(
savedTestPredictions, 'samebib', tlog.db,
tlog.xc._unwrapOutput(UX), tlog.xc._unwrapOutput(predicted_y))
print 'ready for commands like: proppr eval %s %s --metric auc --defaultNeg' % (
savedTestExamples, savedTestPredictions)
acc = session.run(accuracy, feed_dict=test_fd)
print 'test acc', acc
return acc