Python tree.loadWordMap函数代码示例

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本文整理汇总了Python中tree.loadWordMap函数的典型用法代码示例。如果您正苦于以下问题：Python loadWordMap函数的具体用法？Python loadWordMap怎么用？Python loadWordMap使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。

在下文中一共展示了loadWordMap函数的9个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: run

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("--outputDim",dest="outputDim",type="int",default=5)
    parser.add_option("--wvecDim",dest="wvecDim",type="int",default=30)
    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="train")


    (opts,args)=parser.parse_args(args)

    # Testing
    if opts.test:
        test(opts.inFile,opts.data)
        return
    
    print "Loading data..."
    # load training data
    trees = tr.loadTrees()
    opts.numWords = len(tr.loadWordMap())

    rnn = nnet.RNN(opts.wvecDim,opts.outputDim,opts.numWords,opts.minibatch)
    rnn.initParams()

    sgd = optimizer.SGD(rnn,alpha=opts.step,minibatch=opts.minibatch,
        optimizer=opts.optimizer)

    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)
            rnn.toFile(fid)

开发者ID:GuesPita，项目名称:semantic-rntn，代码行数:51，代码来源:runNNet.py

示例2: len

                numGrad = (costP - cost)/epsilon
                err = np.abs(dL[j][i] - numGrad)
                #print "Analytic %.9f, Numerical %.9f, Relative Error %.9f"%(dL[j][i],numGrad,err)
                err2+=err
                count+=1

        if 0.001 > err2/count:
            print "Passed :)"
        else:
            print "Failed : Sum of Error = %.9f" % (err2/count)

if __name__ == '__main__':

    import tree as treeM
    train = treeM.loadTrees()
    numW = len(treeM.loadWordMap())

    wvecDim = 10
    outputDim = 5

    nn = RNTN(wvecDim,outputDim,numW,mbSize=4)
    nn.initParams()

    mbData = train[:1]
    #cost, grad = nn.costAndGrad(mbData)

    print "Numerical gradient check..."
    nn.check_grad(mbData)

开发者ID:vineelpratap，项目名称:Polarity-prediction-using-RNTNs，代码行数:28，代码来源:rntn.py

示例3: run

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("--middleDim",dest="middleDim",type="int",default=10)
    parser.add_option("--outputDim",dest="outputDim",type="int",default=5)
    parser.add_option("--wvecDim",dest="wvecDim",type="int",default=30)

    # for DCNN only
    parser.add_option("--ktop",dest="ktop",type="int",default=5)
    parser.add_option("--m1",dest="m1",type="int",default=10)
    parser.add_option("--m2",dest="m2",type="int",default=7)
    parser.add_option("--n1",dest="n1",type="int",default=6)
    parser.add_option("--n2",dest="n2",type="int",default=12)
    
    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="train")

    parser.add_option("--model",dest="model",type="string",default="RNN")

    (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, opts.model)
        return
    
    print "Loading data..."
    train_accuracies = []
    dev_accuracies = []
    # load training data
    trees = tr.loadTrees('train')
    opts.numWords = len(tr.loadWordMap())

    if (opts.model=='RNTN'):
        nn = RNTN(opts.wvecDim,opts.outputDim,opts.numWords,opts.minibatch)
    elif(opts.model=='RNN'):
        nn = RNN(opts.wvecDim,opts.outputDim,opts.numWords,opts.minibatch)
    elif(opts.model=='RNN2'):
        nn = RNN2(opts.wvecDim,opts.middleDim,opts.outputDim,opts.numWords,opts.minibatch)
    elif(opts.model=='RNN3'):
        nn = RNN3(opts.wvecDim,opts.middleDim,opts.outputDim,opts.numWords,opts.minibatch)
    elif(opts.model=='DCNN'):
        nn = DCNN(opts.wvecDim,opts.ktop,opts.m1,opts.m2, opts.n1, opts.n2,0, opts.outputDim,opts.numWords, 2, opts.minibatch,rho=1e-4)
        trees = cnn.tree2matrix(trees)
    else:
        raise '%s is not a valid neural network so far only RNTN, RNN, RNN2, RNN3, and DCNN'%opts.model
    
    nn.initParams()

    sgd = optimizer.SGD(nn, alpha=opts.step, minibatch=opts.minibatch, optimizer=opts.optimizer)


    dev_trees = tr.loadTrees("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,"train",opts.model,trees))
            print "testing on dev set real quick"
            dev_accuracies.append(test(opts.outFile,"dev",opts.model,dev_trees))
            # clear the fprop flags in trees and dev_trees
            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"


    if evaluate_accuracy_while_training:
        pdb.set_trace()
        print train_accuracies
        print dev_accuracies

开发者ID:ryu577，项目名称:base，代码行数:96，代码来源:runNNet.py

示例4: len

parser.add_option("--epochs",dest="epochs",type="int",default=50)
parser.add_option("--step",dest="step",type="float",default=1e-2)
parser.add_option("--outputDim",dest="outputDim",type="int",default=5)
parser.add_option("--wvecDim",dest="wvecDim",type="int",default=30)
parser.add_option("--outFile",dest="outFile",type="string",
        default="models/distrntn.bin")
parser.add_option("--inFile",dest="inFile",type="string",
        default="models/distrntn.bin")
parser.add_option("--data",dest="data",type="string",default="train")
(opts,args)=parser.parse_args(None)


print "Loading data..."
# load training data
trees = tr.loadTrees()
opts.numWords = len(tr.loadWordMap())


#setup the rntn
rnn = nnet.RNN(opts.wvecDim,opts.outputDim,opts.numWords,opts.minibatch)
rnn.initParams()
sgd = optimizer.SGD(rnn,alpha=opts.step,minibatch=opts.minibatch,
    optimizer=opts.optimizer)

#setup spark
if mode == "local":
   # Set heap space size for java
   #os.environ["_JAVA_OPTIONS"] = "-Xmx1g"
   conf = (SparkConf()
           .setMaster("local[*]")
           .setAppName(appname)

开发者ID:iron-fe，项目名称:rntn_on_Spark，代码行数:31，代码来源:distrntn.py

示例5: run

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("--middleDim",dest="middleDim",type="int",default=10)
    parser.add_option("--outputDim",dest="outputDim",type="int",default=5)
    parser.add_option("--wvecDim",dest="wvecDim",type="int",default=30)

    # By @tiagokv, just to ease the first assignment test
    parser.add_option("--wvecDimBatch",dest="wvecDimBatch",type="string",default="")

    # for DCNN only
    parser.add_option("--ktop",dest="ktop",type="int",default=5)
    parser.add_option("--m1",dest="m1",type="int",default=10)
    parser.add_option("--m2",dest="m2",type="int",default=7)
    parser.add_option("--n1",dest="n1",type="int",default=6)
    parser.add_option("--n2",dest="n2",type="int",default=12)

    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="train")

    parser.add_option("--model",dest="model",type="string",default="RNN")

    (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,opts.model)
        return

    print "Loading data..."
    train_accuracies = []
    dev_accuracies = []
    # load training data
    trees = tr.loadTrees('train')
    opts.numWords = len(tr.loadWordMap())

    if (opts.model=='RNTN'):
        nn = RNTN(opts.wvecDim,opts.outputDim,opts.numWords,opts.minibatch)
    elif(opts.model=='RNN'):
        nn = RNN(opts.wvecDim,opts.outputDim,opts.numWords,opts.minibatch)
    elif(opts.model=='RNN2'):
        nn = RNN2(opts.wvecDim,opts.middleDim,opts.outputDim,opts.numWords,opts.minibatch)
    elif(opts.model=='RNN3'):
        nn = RNN3(opts.wvecDim,opts.middleDim,opts.outputDim,opts.numWords,opts.minibatch)
    elif(opts.model=='DCNN'):
        nn = DCNN(opts.wvecDim,opts.ktop,opts.m1,opts.m2, opts.n1, opts.n2,0, opts.outputDim,opts.numWords, 2, opts.minibatch,rho=1e-4)
        trees = cnn.tree2matrix(trees)
    else:
        raise '%s is not a valid neural network so far only RNTN, RNN, RNN2, RNN3, and DCNN'%opts.model

    nn.initParams()

    sgd = optimizer.SGD(nn,alpha=opts.step,minibatch=opts.minibatch,
        optimizer=opts.optimizer)

    # assuring folder for plots exists
    if( os.path.isdir('plots') == False ): os.makedirs('test')
    if( os.path.isdir('plots/' + opts.model ) == False ): os.makedirs('plots/' + opts.model)

    dev_trees = tr.loadTrees("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,"train",opts.model,trees))
            print "testing on dev set real quick"
            dev_accuracies.append(test(opts.outFile,"dev",opts.model,dev_trees))
            # clear the fprop flags in trees and dev_trees
            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"
#.........这里部分代码省略.........

开发者ID:tiagokv，项目名称:cs224d，代码行数:101，代码来源:runNNet.py

示例6: prnt


if __name__ == '__main__' and __package__ is None:
    from os import sys, path
    sys.path.append(path.dirname(path.dirname(path.abspath('__file__'))))

sys.dont_write_bytecode = True


###############################
# Take a peek at how the tree works.
###############################
import tree as treeM
train = treeM.loadTrees()

word_to_id = treeM.loadWordMap()
id_to_word = {}
for i in word_to_id:
	id_to_word[word_to_id[i]] = i

treeTxt = ""
def prnt(node):
	global treeTxt
	if node.isLeaf:
		#print(id_to_word[node.word], end = " ")
		treeTxt = treeTxt + id_to_word[node.word] + " "
		return 0
	else:
		treeTxt = treeTxt + "("
		prnt(node.left)
		prnt(node.right)

开发者ID:ryu577，项目名称:base，代码行数:29，代码来源:main.py

示例7: run

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("--init",dest="init",type="float",default=0.01)

    parser.add_option("--outputDim",dest="outputDim",type="int",default=5)
    parser.add_option("--wvecDim",dest="wvecDim",type="int",default=30)

    parser.add_option("--rho",dest="rho",type="float",default=1e-6)

    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="train")

    parser.add_option("--model",dest="model",type="string",default="RNTN")

    parser.add_option("--maxTrain",dest="maxTrain", type="int", default=-1)
    parser.add_option("--activation",dest="acti", type="string", default="tanh")

    parser.add_option("--partial",action="store_true",dest="partial",default=False)
    parser.add_option("--w2v",dest="w2vmodel", type="string")

    (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:
        cmfile = opts.inFile + ".confusion_matrix-" + opts.data + ".png"
        test(opts.inFile,opts.data,opts.model,acti=opts.acti)
        return
    
    print "Loading data..."

    embedding = None
    wordMap = None
    if opts.w2vmodel is not None:
        print "Loading pre-trained word2vec model from %s" % opts.w2vmodel
        w2v = models.Word2Vec.load(opts.w2vmodel)
        embedding, wordMap = readW2v(w2v,opts.wvecDim)

    train_accuracies = []
    train_rootAccuracies = []
    dev_accuracies = []
    dev_rootAccuracies = []
    # load training data
    trees = tr.loadTrees('train',wordMap=wordMap)[:opts.maxTrain] #train.full.15
    if opts.maxTrain > -1:
        print "Training only on %d trees" % opts.maxTrain
    opts.numWords = len(tr.loadWordMap())


    if opts.partial==True:
        print "Only partial feedback"

    if (opts.model=='RNTN'):
        nn = RNTN(wvecDim=opts.wvecDim,outputDim=opts.outputDim,numWords=opts.numWords,
                  mbSize=opts.minibatch,rho=opts.rho, acti=opts.acti, init=opts.init, partial=opts.partial)
    else:
        raise '%s is not a valid neural network so far only RNTN, RNN'%opts.model
    
    nn.initParams(embedding=embedding)

    sgd = optimizer.SGD(nn,alpha=opts.step,minibatch=opts.minibatch,
        optimizer=opts.optimizer)


    dev_trees = tr.loadTrees("dev") #dev.full.15
    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"
            acc, sacc = test(opts.outFile,"train",opts.model,trees, acti=opts.acti)
            train_accuracies.append(acc)
            train_rootAccuracies.append(sacc)
            print "testing on dev set"
            dacc, dsacc = test(opts.outFile,"dev",opts.model,dev_trees, acti=opts.acti)
            dev_accuracies.append(dacc)
#.........这里部分代码省略.........

开发者ID:juliakreutzer，项目名称:wtfrnn，代码行数:101，代码来源:runNNet.py

示例8: run

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("--rho",dest="rho",type="float",default=1e-4)

    parser.add_option("--middleDim",dest="middleDim",type="int",default=10)
    parser.add_option("--outputDim",dest="outputDim",type="int",default=3)
    parser.add_option("--wvecDim",dest="wvecDim",type="int",default=30)

    # for DCNN only
    parser.add_option("--ktop",dest="ktop",type="int",default=5)
    parser.add_option("--m1",dest="m1",type="int",default=10)
    parser.add_option("--m2",dest="m2",type="int",default=7)
    parser.add_option("--n1",dest="n1",type="int",default=6)
    parser.add_option("--n2",dest="n2",type="int",default=12)
    
    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="train")

    parser.add_option("--model",dest="model",type="string",default="RNN")

    parser.add_option("--pretrain",dest="pretrain",default=False)
    parser.add_option("--dropout",dest="dropout",default=False)

    (opts,args)=parser.parse_args(args)


    # make this false if you dont care about your accuracies per epoch
    evaluate_accuracy_while_training = True

    # Testing
    if opts.test:
        test(opts.inFile,opts.data,opts.model,e=1000)
        return
    
    print "Loading data..."
    train_accuracies = []
    dev_accuracies = []
    # load training data
    trees = tr.loadTrees('train')
    opts.numWords = len(tr.loadWordMap())

    if (opts.model=='RNTN'):
        nn = RNTN(opts.wvecDim,opts.outputDim,opts.numWords,opts.minibatch,opts.pretrain,opts.dropout,opts.rho)
    elif(opts.model=='RNN'):
        nn = RNN(opts.wvecDim,opts.outputDim,opts.numWords,opts.minibatch,opts.pretrain,opts.dropout,opts.rho)
    elif(opts.model=='RNN2'):
        nn = RNN2(opts.wvecDim,opts.middleDim,opts.outputDim,opts.numWords,opts.minibatch,opts.pretrain,opts.dropout,opts.rho)
    elif(opts.model=='RNN2TANH'):
        nn = RNN2(opts.wvecDim,opts.middleDim,opts.outputDim,opts.numWords,opts.minibatch,opts.pretrain,opts.dropout,opts.rho)
    elif(opts.model=='RNN3'):
        nn = RNN3(opts.wvecDim,opts.middleDim,opts.outputDim,opts.numWords,opts.minibatch,opts.pretrain,opts.dropout,opts.rho)
    elif(opts.model=='DCNN'):
        nn = DCNN(opts.wvecDim,opts.ktop,opts.m1,opts.m2, opts.n1, opts.n2,0, opts.outputDim,opts.numWords, 2, opts.minibatch,rho=1e-4)
        trees = cnn.tree2matrix(trees)
    else:
        raise '%s is not a valid neural network so far only RNTN, RNN, RNN2, and DCNN'%opts.model
    
    nn.initParams()

    sgd = optimizer.SGD(nn,alpha=opts.step,minibatch=opts.minibatch,
        optimizer=opts.optimizer)

    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,"train",opts.model,trees,e))
            print "testing on dev set real quick"
            dev_accuracies.append(test(opts.outFile,"dev",opts.model,e=e))
        if e%10==0:
            if evaluate_accuracy_while_training:
                print train_accuracies
                print dev_accuracies
                plt.figure()
                plt.plot(train_accuracies,label = 'Train')
                plt.plot(dev_accuracies,label = 'Dev')
                plt.legend()
                plt.savefig('temp/train_dev_accuracies_'+str(opts.model)+'_middle_'+str(opts.middleDim)+'.png')
#.........这里部分代码省略.........

开发者ID:yucca43，项目名称:NNproject，代码行数:101，代码来源:runNNet.py

示例9: run

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

开发者ID:successar，项目名称:RAE，代码行数:72，代码来源:runNNet.py

注：本文中的tree.loadWordMap函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。

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Python tree.loadWordMap函数代码示例

示例1: run

示例2: len

示例3: run

示例4: len

示例5: run

示例6: prnt

示例7: run

示例8: run

示例9: run

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