Python base.NNBase类代码示例(python中nn.base.nnbase类的具体用法)

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


Python base.NNBase类代码示例(python中nn.base.nnbase类的具体用法)

在下文中一共展示了NNBase类的15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: __init__

 def __init__(self, L0, U0=None,
                 alpha=0.005, rseed=10, bptt=1):

        self.hdim = L0.shape[1] # word vector dimensions      |D|
        self.vdim = L0.shape[0] # vocab size                  |V|
        param_dims = dict(H = (self.hdim, self.hdim),
                          U = L0.shape)
        # note that only L gets sparse updates
        param_dims_sparse = dict(L = L0.shape)
        NNBase.__init__(self, param_dims, param_dims_sparse)

        #### YOUR CODE HERE ####


        # Initialize word vectors
        # either copy the passed L0 and U0 (and initialize in your notebook)
        # or initialize with gaussian noise here
        self.sparams.L = 0.1 * random.standard_normal(self.sparams.L.shape)
        # self.params.U
        self.params.U = 0.1 * random.standard_normal(self.params.U.shape)
        # Initialize H matrix, as with W and U in part 1
        # self.params.H = random_weight_matrix(*self.params.H.shape)
        self.params.H = random_weight_matrix(*self.params.H.shape)

        self.bptt = bptt
        self.alpha = alpha
开发者ID:icodingc,项目名称:CS224d,代码行数:26,代码来源:rnnlm.py

示例2: __init__

 def __init__(self, L0, U0=None,alpha=0.005, lreg = 0.00001, rseed=10, bptt=1):

        self.hdim = L0.shape[1] # word vector dimensions
        self.vdim = L0.shape[0] # vocab size
        param_dims = dict(H = (self.hdim, self.hdim), W = (self.hdim,self.hdim))
                          #,U = L0.shape)
        # note that only L gets sparse updates
        param_dims_sparse = dict(L = L0.shape)
        NNBase.__init__(self, param_dims, param_dims_sparse)

        self.alpha = alpha
        self.lreg = lreg
        #### YOUR CODE HERE ####
        
        # Initialize word vectors
        # either copy the passed L0 and U0 (and initialize in your notebook)
        # or initialize with gaussian noise here

        # Initialize H matrix, as with W and U in part 1
        self.bptt = bptt
        random.seed(rseed)
        self.params.H = random_weight_matrix(*self.params.H.shape)
        self.params.W = random_weight_matrix(*self.params.W.shape)
        #self.params.U = 0.1*np.random.randn(*L0.shape)
        self.sparams.L = L0.copy()
开发者ID:alphadl,项目名称:cs224d,代码行数:25,代码来源:rnnlmWithHierarchicalSoftmax.py

示例3: __init__

 def __init__(self, L0, Dy=N_ASPECTS*SENT_DIM, U0=None,
                 alpha=0.005, rseed=10, bptt=5):

        self.hdim = L0.shape[1] # word vector dimensions
        self.vdim = L0.shape[0] # vocab size
        self.ydim = Dy
        param_dims = dict(H = (self.hdim, self.hdim),
                          U = (self.ydim, self.hdim),
                          b1 = (self.hdim,),
                          b2 =(self.ydim,))
        # note that only L gets sparse updates
        param_dims_sparse = dict(L = L0.shape)
        NNBase.__init__(self, param_dims, param_dims_sparse)

        #### YOUR CODE HERE ####
        var = .1
        sigma = sqrt(var)
        from misc import random_weight_matrix
        random.seed(rseed)
        # Initialize word vectors
        self.bptt = bptt
        self.alpha = alpha
        self.params.H=random_weight_matrix(*self.params.H.shape)
        if U0 is not None:
            self.params.U= U0.copy()
        else:
            self.params.U= random_weight_matrix(*self.params.U.shape)
        self.sparams.L = L0.copy()
        self.params.b1 = zeros((self.hdim,))
        self.params.b2 = zeros((self.ydim,))
开发者ID:laisun,项目名称:EntitySentiment,代码行数:30,代码来源:rnn_simple.py

示例4: __init__

 def __init__(self, L0, U0=None,
                 alpha=0.005, rseed=10, bptt=1):

        self.hdim = L0.shape[1] # word vector dimensions
        self.vdim = L0.shape[0] # vocab size
        param_dims = dict(H = (self.hdim, self.hdim),
                          U = L0.shape)
        # note that only L gets sparse updates
        param_dims_sparse = dict(L = L0.shape)
        NNBase.__init__(self, param_dims, param_dims_sparse)

        #### YOUR CODE HERE ####


        # Initialize word vectors
        # either copy the passed L0 and U0 (and initialize in your notebook)
        # or initialize with gaussian noise here
        # Initialize H matrix, as with W and U in part 1
        self.sparams.L = L0.copy()
        if U0 is None:
            self.params.U = random.normal(0, 0.1, param_dims['U'])
        else:
            self.params.U = U0.copy()
        self.params.H = random_weight_matrix(*param_dims['H'])
        self.alpha = alpha
#        self.rseed = rseed
        self.bptt = bptt
开发者ID:ZhengXuxiao,项目名称:DLforNLP,代码行数:27,代码来源:rnnlm.py

示例5: __init__

 def __init__(self, L0, U0=None,
                 alpha=0.005, rseed=10, bptt=1):
        random.seed(rseed)
        self.hdim = L0.shape[1] # word vector dimensions
        self.vdim = L0.shape[0] # vocab size
        param_dims = dict(H = (self.hdim, self.hdim),
                          U = L0.shape)
        # note that only L gets sparse updates
        param_dims_sparse = dict(L = L0.shape)
        NNBase.__init__(self, param_dims, param_dims_sparse)

        #### YOUR CODE HERE ####
        self.sparams.L = L0.copy()
        self.params.H = random_weight_matrix(self.hdim, self.hdim)
        self.alpha = alpha
        self.bptt = bptt


        # Initialize word vectors
        # either copy the passed L0 and U0 (and initialize in your notebook)
        # or initialize with gaussian noise here
        if U0 is not None:
            self.params.U = U0.copy()
        else:
            sigma = 0.1
            mu = 0
            #self.params.U = random.normal(mu, sigma, (self.vdim, self.hdim))
            self.params.U = sigma*random.randn(self.vdim, self.hdim) + mu
开发者ID:janenie,项目名称:rnn_research,代码行数:28,代码来源:rnnlm.py

示例6: __init__

 def __init__(self, L0, D0, U0=None,
                 alpha=0.005, rseed=10, bptt=1):

        self.hdim = L0.shape[1] # word vector dimensions
        self.vdim = L0.shape[0] # vocab size
        self.ddim = D0.shape[0] # doc size
        param_dims = dict(H = (self.hdim, self.hdim),
                          U = L0.shape, G = L0.shape)
        # note that only L gets sparse updates
        param_dims_sparse = dict(L = L0.shape, D = D0.shape)
        NNBase.__init__(self, param_dims, param_dims_sparse)

        #### YOUR CODE HERE ####

        self.bptt = bptt
        self.alpha = alpha

        # Initialize word vectors
        self.sparams.L = L0.copy()
        self.sparams.D = D0.copy()

        self.params.U = random.randn(self.vdim, self.hdim)*0.1

        # Initialize H matrix, as with W and U in part 1
        self.params.H = random_weight_matrix(self.hdim, self.hdim)
        self.params.G = random_weight_matrix(self.vdim, self.hdim)
开发者ID:afgiel,项目名称:docvec,代码行数:26,代码来源:drnnlm.py

示例7: __init__

 def __init__(self, wv, dims=[100, 5],
                 reg=0.1, alpha=0.001,
                 rseed=10):
        """
        Set up classifier: parameters, hyperparameters
        """
        ##
        # Store hyperparameters
        self.lreg = reg # regularization
        self.alpha = alpha # default learning rate
        self.nclass = dims[1] # number of output classes

        ##
        # NNBase stores parameters in a special format
        # for efficiency reasons, and to allow the code
        # to automatically implement gradient checks
        # and training algorithms, independent of the
        # specific model architecture
        # To initialize, give shapes as if to np.array((m,n))
        param_dims = dict(W = (dims[1], dims[0]), # 5x100 matrix
                          b = (dims[1])) # column vector
        # These parameters have sparse gradients,
        # which is *much* more efficient if only a row
        # at a time gets updated (e.g. word representations)
        param_dims_sparse = dict(L=wv.shape)
        NNBase.__init__(self, param_dims, param_dims_sparse)

        ##
        # Now we can access the parameters using
        # self.params.<name> for normal parameters
        # self.sparams.<name> for params with sparse gradients
        # and get access to normal NumPy arrays
        self.sparams.L = wv.copy() # store own representations
        self.params.W = random_weight_matrix(*self.params.W.shape)
开发者ID:kornev,项目名称:cs224d-hw2,代码行数:34,代码来源:softmax_example.py

示例8: __init__

 def __init__(self, L0, U0=None,
                 alpha=0.005, rseed=10, bptt=1):

        self.hdim = L0.shape[1] # word vector dimensions
        self.vdim = L0.shape[0] # vocab size
        param_dims = dict(H = (self.hdim, self.hdim),
                          U = (L0.shape if U0 is None else U0.shape))
        # note that only L gets sparse updates
        param_dims_sparse = dict(L = L0.shape)
        NNBase.__init__(self, param_dims, param_dims_sparse)

        #### YOUR CODE HERE ####
        self.alpha = alpha

        self.bptt = bptt

        # Initialize word vectors
        # either copy the passed L0 and U0 (and initialize in your notebook)
        # or initialize with gaussian noise here
        random.seed(rseed)
        
        sigma = sqrt(0.1)
        self.sparams.L = random.normal(0, sigma, L0.shape)
        self.params.U = random.normal(0, sigma, param_dims['U'])
        
        # Initialize H matrix, as with W and U in part 1
        self.params.H = random_weight_matrix(*param_dims['H'])

        self.lamb = .0001 # regularization
开发者ID:arthur-tsang,项目名称:EqnMaster,代码行数:29,代码来源:rnnlm.py

示例9: __init__

 def __init__(self, L0, U0=None,
                 alpha=0.005, rseed=10):

        self.hdim = L0.shape[1] # word vector dimensions
        self.vdim = L0.shape[0] # vocab size
        param_dims = dict(LH = (self.hdim, self.hdim),
                          RH = (self.hdim, self.hdim),
                          U = (self.vdim, self.hdim * 2))
        # note that only L gets sparse updates
        param_dims_sparse = dict(LL = L0.shape, RL = L0.shape)
        NNBase.__init__(self, param_dims, param_dims_sparse)

        #### YOUR CODE HERE ####
        np.random.seed(rseed) # be sure to seed this for repeatability!
        self.alpha = alpha

        # Initialize word vectors
        # either copy the passed L0 and U0 (and initialize in your notebook)
        # or initialize with gaussian noise here
        #self.sparams.LL = np.random.randn(*L0.shape) * np.sqrt(0.1)
        #self.sparams.RL = np.random.randn(*L0.shape) * np.sqrt(0.1)
        self.sparams.LL = L0
        self.sparams.RL = L0
        self.params.U = np.random.randn(self.vdim, self.hdim*2) * np.sqrt(0.1)

        # Initialize H matrix, as with W and U in part 1
        self.params.LH = random_weight_matrix(self.hdim, self.hdim)
        self.params.RH = random_weight_matrix(self.hdim, self.hdim)
开发者ID:nishithbsk,项目名称:SentenceGeneration,代码行数:28,代码来源:brnnlm.py

示例10: __init__

 def __init__(self, L0, U0=None,
                 alpha=0.005, rseed=10, bptt=1):

        self.hdim = L0.shape[1] # word vector dimensions
        self.vdim = L0.shape[0] # vocab size
        param_dims = dict(H = (self.hdim, self.hdim),
                          U = L0.shape)
        # note that only L gets sparse updates
        param_dims_sparse = dict(L = L0.shape)
        NNBase.__init__(self, param_dims, param_dims_sparse)
开发者ID:YunshengWei,项目名称:Deep-Learning-for-Natural-Language-Processing,代码行数:10,代码来源:rnnlm.py

示例11: grad_check

 def grad_check(self, x, y, outfd=sys.stderr, **kwargs):
        """
        Wrapper for gradient check on RNNs;
        ensures that backprop-through-time is run to completion,
        computing the full gradient for the loss as summed over
        the input sequence and predictions.

        Do not modify this function!
        """
        NNBase.grad_check(self, x, y, outfd=outfd, **kwargs)
开发者ID:nishithbsk,项目名称:SentenceGeneration,代码行数:10,代码来源:brnnlm.py

示例12: __init__

 def __init__(self, wv, windowsize=3,
                 dims=[None, 100, 5],
                 reg=0.001, alpha=0.01, rseed=10):
        """
        Initialize classifier model.

        Arguments:
        wv : initial word vectors (array |V| x n)
            note that this is the transpose of the n x |V| matrix L
            described in the handout; you'll want to keep it in
            this |V| x n form for efficiency reasons, since numpy
            stores matrix rows continguously.
        windowsize : int, size of context window
        dims : dimensions of [input, hidden, output]
            input dimension can be computed from wv.shape
        reg : regularization strength (lambda)
        alpha : default learning rate
        rseed : random initialization seed
        """

        # Set regularization
        self.lreg = float(reg)
        self.alpha = alpha # default training rate

        dims[0] = windowsize * wv.shape[1] # input dimension
        param_dims = dict(W=(dims[1], dims[0]),
                          b1=(dims[1],),
                          U=(dims[2], dims[1]),
                          b2=(dims[2],),
                          )
        param_dims_sparse = dict(L=wv.shape)

        # initialize parameters: don't change this line
        NNBase.__init__(self, param_dims, param_dims_sparse)

        random.seed(rseed) # be sure to seed this for repeatability!
        #### YOUR CODE HERE ####

        # any other initialization you need
        #self.sparams, self.grads, self.param, self.sgrads
        #where are they defined?
        #为什么可以直接可以使用?
        self.sparams.L = wv.copy()
        #self.sparam.L = wv.copy()
        self.params.U = random_weight_matrix(*param_dims["U"])
        #self.param.U = random_weight_matrix(param_dims["U"])
        self.params.W = random_weight_matrix(*param_dims["W"])
        #self.param.b1 = zeros(param_dims["b1"])
        #self.param.b2 = zeros(param_dims["b2"])
        self.windowSize = windowsize
        self.wordVecLen = wv.shape[1]
        self.wordVecNum = wv.shape[0]
开发者ID:NeighborhoodWang,项目名称:CS224D-problem-set2,代码行数:52,代码来源:nerwindow.py

示例13: __init__

 def __init__(self, wv, windowsize=3,
                 dims=[None, 100, 5],
                 reg=0.001, alpha=0.01, rseed=10):
        """
        Initialize classifier model.

        Arguments:
        wv : initial word vectors (array |V| x n)
            note that this is the transpose of the n x |V| matrix L
            described in the handout; you'll want to keep it in
            this |V| x n form for efficiency reasons, since numpy
            stores matrix rows continguously.
        windowsize : int, size of context window
        dims : dimensions of [input, hidden, output]
            input dimension can be computed from wv.shape
        reg : regularization strength (lambda)
        alpha : default learning rate
        rseed : random initialization seed
        """

        # Set regularization
        self.lreg = float(reg)
        self.alpha = alpha # default training rate

        dims[0] = windowsize * wv.shape[1]         # input dimension
        param_dims = dict(W1=(dims[1], dims[0]),   # 100 x 150
                          b2=(dims[1],),           # 100 x 1
                          W2=(dims[2], dims[1]),   # 5 X 100
                          b3=(dims[2],),           # 5 x 1
                          )

        param_dims_sparse = dict(L=wv.shape)       # |V| x 50

        # initialize parameters: don't change this line
        NNBase.__init__(self, param_dims, param_dims_sparse)

        random.seed(rseed) # be sure to seed this for repeatability!
        #### YOUR CODE HERE ####
        self.sparams.L = wv.copy();

        self.params.W1 = random_weight_matrix(param_dims['W1'][0], param_dims['W1'][1])

        self.params.b2 = append([], random_weight_matrix(param_dims['b2'][0], 1))
        self.params.b3 = append([], random_weight_matrix(param_dims['b3'][0], 1))
        self.params.W2 = random_weight_matrix(param_dims['W2'][0], param_dims['W2'][1])
        self.n = wv.shape[1]

        # informational
        self.windowsize = windowsize
        self.hidden_units = dims[1]
开发者ID:kireet,项目名称:cs224d,代码行数:50,代码来源:nerwindow.py

示例14: grad_check

 def grad_check(self, x, y, outfd=sys.stderr, **kwargs):
     """
     Wrapper for gradient check on RNNs;
     ensures that backprop-through-time is run to completion,
     computing the full gradient for the loss as summed over
     the input sequence and predictions.
     Do not modify this function!
     """
     bptt_old = self.bptt
     self.bptt = len(y)
     print >> outfd, "NOTE: temporarily setting self.bptt = len(y) = %d to compute true gradient." % self.bptt
     NNBase.grad_check(self, x, y, outfd=outfd, **kwargs)
     self.bptt = bptt_old
     print >> outfd, "Reset self.bptt = %d" % self.bptt
开发者ID:ryu577,项目名称:base,代码行数:14,代码来源:msushkov_rnnlm.py

示例15: __init__

 def __init__(self, wv, windowsize=3,
                 dims=[None, 100, 5],
                 reg=0.001, alpha=0.01, rseed=10):
        """
        Initialize classifier model.

        Arguments:
        wv : initial word vectors (array |V| x n)
            note that this is the transpose of the n x |V| matrix L
            described in the handout; you'll want to keep it in
            this |V| x n form for efficiency reasons, since numpy
            stores matrix rows continguously.
        windowsize : int, size of context window
        dims : dimensions of [input, hidden, output]
            input dimension can be computed from wv.shape
        reg : regularization strength (lambda)
        alpha : default learning rate
        rseed : random initialization seed
        """

        # Set regularization
        self.lreg = float(reg)
        self.alpha = alpha # default training rate

        #wv.shape: (100232,50)
        dims[0] = windowsize * wv.shape[1] # input dimension 3*50=150
        param_dims = dict(W=(dims[1], dims[0]),# W(100,150)
                          b1=(dims[1],),#b(100)
                          U=(dims[2], dims[1]),#U(5,100)
                          b2=(dims[2],),#(5,)
                          )
        param_dims_sparse = dict(L=wv.shape) #L(100232,50)

        # initialize parameters: don't change this line
        NNBase.__init__(self, param_dims, param_dims_sparse)

        random.seed(rseed) # be sure to seed this for repeatability!
        #### YOUR CODE HERE ####

        # any other initialization you need
        self.sparams.L = wv.copy() # store own representations,100232,50 matrix
        self.params.W = random_weight_matrix(*self.params.W.shape)
        self.params.U = random_weight_matrix(*self.params.U.shape)

        self.window_size = windowsize#3
        self.word_vec_size = wv.shape[1]#50
开发者ID:Tang7,项目名称:rnn224,代码行数:46,代码来源:nerwindow.py

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