def test(netFile,dataSet,trees=None):
if trees==None:
trees = tr.printtree(dataSet)
assert netFile is not None, "Must give model to test"
print "Testing netFile %s"%netFile
with open(netFile,'r') as fid:
opts = pickle.load(fid)
_ = pickle.load(fid)
nn = RNN(opts.wvecDim,opts.outputDim,opts.numWords,opts.alpha,opts.minibatch)
nn.initParams()
nn.fromFile(fid)
cost, Mis = nn.costAndGrad(trees,test=True)
print "Cost = %f, Acc = %f"%(cost, 1.0 - Mis)
return (1.0 - Mis)
#Print a sentence. prnt(train[0].root) nltktree = Tree.fromstring(treeTxt) nltktree.pretty_print() ############################### # Create a toy model for testing. ############################### numW = len(treeM.loadWordMap()) wvecDim = 10 outputDim = 5 rnn = RNN(wvecDim, outputDim, numW, mbSize = 4) rnn.initParams() rnn.L, rnn.W, rnn.b, rnn.Ws, rnn.bs = rnn.stack # Zero gradients rnn.dW[:] = 0 rnn.db[:] = 0 rnn.dWs[:] = 0 rnn.dbs[:] = 0 rnn.dL = collections.defaultdict(rnn.defaultVec) ost = 0.0 correct = [] guess = [] total = 0.0
def run():
print "Loading data..."
model = "RNN"
trees = tr.loadTrees('train')
dev_trees = tr.loadTrees('dev')
wvecDimList = [5, 15, 25, 35, 45]
#wvecDimList = [10,20,40]
accuracy_per_wvecDim = []
epochs = 100
outFileText = "./param/%s/%s_cost_and_acc" % (model, model)
f = open(outFileText, 'w')
for wvecDim in wvecDimList:
nn = RNN(wvecDim, 5, len(tr.loadWordMap()), 30)
nn.initParams()
sgd = optimizer.SGD(nn, alpha=0.01, minibatch=30, optimizer="adagrad")
outFile = "./param/%s/%s_wvecDim_%d_epochs_%d_step_001.bin" % (
model, model, wvecDim, epochs)
train_cost = []
train_acc = []
dev_cost = []
dev_acc = []
cost = 0
accuracy = 0
for e in range(epochs):
start = time.time()
sgd.run(trees)
end = time.time()
print "Time per epoch : %f" % (end - start)
with open(outFile, 'w') as fid:
hyperparam = {}
hyperparam['alpha'] = 0.01
hyperparam['minibatch'] = 30
hyperparam['wvecDim'] = wvecDim
pickle.dump(hyperparam, fid)
nn.toFile(fid)
cost, accuracy = test(nn, trees)
train_cost.append(cost)
train_acc.append(accuracy)
cost, accuracy = test(nn, dev_trees)
dev_cost.append(cost)
dev_acc.append(accuracy)
for tree in trees:
tr.leftTraverse(tree.root, nodeFn=tr.clearFprop)
for tree in dev_trees:
tr.leftTraverse(tree.root, nodeFn=tr.clearFprop)
print "fprop in trees cleared"
plot_cost_acc(
train_cost, dev_cost,
"./figures/%s/%s_Cost_Figure_%d" % (model, model, wvecDim), epochs)
plot_cost_acc(
train_acc, dev_acc,
"./figures/%s/%s_Accuracy_Figure_%d" % (model, model, wvecDim),
epochs)
anwser = "Cost = %f, Acc= %f" % (cost, accuracy)
f.write(anwser)
accuracy_per_wvecDim.append(accuracy)
f.close()
plt.figure(figsize=(6, 4))
plt.title(r"Accuracies and vector Dimension")
plt.xlabel("vector Dimension")
plt.ylabel(r"Accuracy")
plt.ylim(ymin=min(accuracy_per_wvecDim) * 0.8,
ymax=max(accuracy_per_wvecDim) * 1.2)
plt.plot(wvecDimList,
accuracy_per_wvecDim,
color='b',
marker='o',
linestyle='-')
plt.savefig("./figures/%s/%s_Accuracy_and_vectorDimsension.png" %
(model, model))
plt.close()
def run(args=None):
usage = "usage : %prog [options]"
parser = optparse.OptionParser(usage=usage)
parser.add_option("--test",action="store_true",dest="test",default=False)
# Optimizer
parser.add_option("--minibatch",dest="minibatch",type="int",default=30)
parser.add_option("--optimizer",dest="optimizer",type="string",
default="adagrad")
parser.add_option("--epochs",dest="epochs",type="int",default=50)
parser.add_option("--step",dest="step",type="float",default=1e-2)
parser.add_option("--wvecDim",dest="wvecDim",type="int",default=30)
parser.add_option("--outputDim",dest="outputDim",type="int",default=2)
parser.add_option("--alpha",dest="alpha",type="int",default=0.2)
parser.add_option("--outFile",dest="outFile",type="string",
default="models/test.bin")
parser.add_option("--inFile",dest="inFile",type="string",
default="models/test.bin")
parser.add_option("--data",dest="data",type="string",default="brae.pos")
parser.add_option("--dev",dest="dev",type="string",default="brae.dev")
parser.add_option("--wordMap",dest="map",type="string",default="brae.tot")
(opts,args)=parser.parse_args(args)
# make this false if you dont care about your accuracies per epoch, makes things faster!
evaluate_accuracy_while_training = True
# Testing
if opts.test:
test(opts.inFile,opts.data)
return
print "Loading data..."
train_accuracies = []
dev_accuracies = []
trees = tr.printtree(opts.data)
opts.numWords = len(tr.loadWordMap(opts.map))
nn = RNN(opts.wvecDim,opts.outputDim,opts.numWords,opts.alpha,opts.minibatch)
nn.initParams()
sgd = optimizer.SGD(nn,alpha=opts.step,minibatch=opts.minibatch,
optimizer=opts.optimizer)
dev_trees = tr.printtree(opts.dev)
for e in range(opts.epochs):
start = time.time()
print "Running epoch %d"%e
sgd.run(trees)
end = time.time()
print "Time per epoch : %f"%(end-start)
with open(opts.outFile,'w') as fid:
pickle.dump(opts,fid)
pickle.dump(sgd.costt,fid)
nn.toFile(fid)
if evaluate_accuracy_while_training:
print "testing on training set real quick"
train_accuracies.append(test(opts.outFile,opts.data,trees))
print "testing on dev set real quick"
dev_accuracies.append(test(opts.outFile,opts.dev,dev_trees))
if evaluate_accuracy_while_training:
pdb.set_trace()
print train_accuracies
print dev_accuracies
