Exemplo n.º 1
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    def __init__(self, options):
        """Initializes the parameters for a LSTM layer of a recurrent neural network.

        :type options: dict
        :param options: a dictionary of training options
        """

        self.options = options

        # Initialize the parameters.
        self.init_params = OrderedDict()

        nin = self.options['dim_word']
        dim = self.options['dim']
        W = numpy.concatenate([normalized_weight(nin, dim),
                               normalized_weight(nin, dim),
                               normalized_weight(nin, dim)],
                              axis=1)
        self.init_params['encoder_W'] = W

        n_gates = 3
        self.init_params['encoder_b'] = numpy.zeros((n_gates * dim,)).astype('float32')

        U = numpy.concatenate([orthogonal_weight(dim),
                               orthogonal_weight(dim),
                               orthogonal_weight(dim)],
                              axis=1)
        self.init_params['encoder_U'] = U

        Wx = normalized_weight(nin, dim)
        self.init_params['encoder_Wx'] = Wx

        Ux = orthogonal_weight(dim)
        self.init_params['encoder_Ux'] = Ux
        self.init_params['encoder_bx'] = numpy.zeros((dim,)).astype('float32')
Exemplo n.º 2
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    def __init__(self, options):
        """Initializes the parameters for a feed-forward layer of a neural
        network.

        :type options: dict
        :param options: a dictionary of training options
        """

        # Create the parameters.
        self.init_params = OrderedDict()

        nin = options['dim']
        nout = options['dim_word']
        self.init_params['ff_logit_lstm_W'] = normalized_weight(nin, nout, scale=0.01, ortho=False)
        self.init_params['ff_logit_lstm_b'] = numpy.zeros((nout,)).astype('float32')

        nin = options['dim_word']
        nout = options['dim_word']
        self.init_params['ff_logit_prev_W'] = normalized_weight(nin, nout, scale=0.01, ortho=False)
        self.init_params['ff_logit_prev_b'] = numpy.zeros((nout,)).astype('float32')

        nin = options['dim_word']
        nout = options['n_words']
        self.init_params['ff_logit_W'] = normalized_weight(nin, nout, scale=0.01, ortho=True)
        self.init_params['ff_logit_b'] = numpy.zeros((nout,)).astype('float32')
Exemplo n.º 3
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    def __init__(self, options):
        """Initializes the parameters for a LSTM layer of a recurrent neural network.

        :type options: dict
        :param options: a dictionary of training options
        """

        self.options = options

        # Initialize the parameters.
        self.init_params = OrderedDict()

        nin = self.options['dim_word']
        dim = self.options['dim']
        W = numpy.concatenate([
            normalized_weight(nin, dim),
            normalized_weight(nin, dim),
            normalized_weight(nin, dim)
        ],
                              axis=1)
        self.init_params['encoder_W'] = W

        n_gates = 3
        self.init_params['encoder_b'] = numpy.zeros(
            (n_gates * dim, )).astype('float32')

        U = numpy.concatenate([
            orthogonal_weight(dim),
            orthogonal_weight(dim),
            orthogonal_weight(dim)
        ],
                              axis=1)
        self.init_params['encoder_U'] = U

        Wx = normalized_weight(nin, dim)
        self.init_params['encoder_Wx'] = Wx

        Ux = orthogonal_weight(dim)
        self.init_params['encoder_Ux'] = Ux
        self.init_params['encoder_bx'] = numpy.zeros((dim, )).astype('float32')
Exemplo n.º 4
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    def __init__(self, options):
        """Initializes the parameters for the first layer of a neural network
        language model, which creates the word embeddings.

        :type options: dict
        :param options: a dictionary of training options
        """

        # Initialize the parameters.
        self.init_params = OrderedDict()

        nin = options['n_words']
        nout = options['dim_word']
        self.init_params['Wemb'] = normalized_weight(nin, nout)
Exemplo n.º 5
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    def __init__(self, options):
        """Initializes the parameters for the first layer of a neural network
        language model, which creates the word embeddings.

        :type options: dict
        :param options: a dictionary of training options
        """

        # Initialize the parameters.
        self.init_params = OrderedDict()

        nin = options['n_words']
        nout = options['dim_word']
        self.init_params['Wemb'] = normalized_weight(nin, nout)
Exemplo n.º 6
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    def __init__(self, options):
        """Initializes the parameters for a feed-forward layer of a neural
        network.

        :type options: dict
        :param options: a dictionary of training options
        """

        # Create the parameters.
        self.init_params = OrderedDict()

        nin = options['dim']
        nout = options['dim_word']
        self.init_params['ff_logit_lstm_W'] = normalized_weight(nin,
                                                                nout,
                                                                scale=0.01,
                                                                ortho=False)
        self.init_params['ff_logit_lstm_b'] = numpy.zeros(
            (nout, )).astype('float32')

        nin = options['dim_word']
        nout = options['dim_word']
        self.init_params['ff_logit_prev_W'] = normalized_weight(nin,
                                                                nout,
                                                                scale=0.01,
                                                                ortho=False)
        self.init_params['ff_logit_prev_b'] = numpy.zeros(
            (nout, )).astype('float32')

        nin = options['dim_word']
        nout = options['n_words']
        self.init_params['ff_logit_W'] = normalized_weight(nin,
                                                           nout,
                                                           scale=0.01,
                                                           ortho=True)
        self.init_params['ff_logit_b'] = numpy.zeros(
            (nout, )).astype('float32')