custom_layers.py 文件源码

def call(self, x, mask=None):
        input_dim = self.input_dim
        input_type='real'
        out_every_t=False
        loss_function='MSE'
        output_type='real'
        flag_feed_forward=False
        flag_use_mask=False
        hidden_bias_mean=np.float32(0.0)
        hidden_bias_init='zero'
        Wimpl=self.unitary_impl
        if ('full' in Wimpl):
            Wimpl='full'
        elif (Wimpl=='ASB2016'):
            Wimpl='adhoc'
            #hidden_bias_init='rand'
        elif (Wimpl=='ASB2016_fast'):
            Wimpl='adhoc_fast'
        n_layers=1
        seed=1234
        x_spec=K.permute_dimensions(x,(1,0,2))
        inputs, parameters, costs = models.complex_RNN(input_dim, self.hidden_dim, self.output_dim, input_type=input_type,out_every_t=out_every_t, loss_function=loss_function,output_type=output_type,flag_feed_forward=flag_feed_forward,flag_return_lin_output=True,x_spec=x_spec,flag_use_mask=flag_use_mask,hidden_bias_mean=hidden_bias_mean,Wimpl=Wimpl,flag_return_hidden_states=True,n_layers=n_layers,seed=seed,hidden_bias_init=hidden_bias_init)

        lin_output=costs[2]
        #self.hidden_states=costs[3]

        if (self.unitary_impl=='full'):
            # just use lrng for learning rate on this parameter
            parameters[-1].name+='full_natGrad'
        elif (self.unitary_impl=='full_natGrad'):
            # use fixed lrng with natural gradient update
            parameters[-1].name+='_natGrad_unitaryAug'
        elif (self.unitary_impl=='full_natGradRMS'):
            # use fixed lrng with natural gradient update and RMSprop-style gradient adjustment
            parameters[-1].name+='_natGradRMS_unitaryAug'
        elif (self.unitary_impl=='full_enforceComplex'):
            # swap out 2Nx2N augmented unitary matrix for Nx2N, which ensures the 
            # complex number constraint is satisfied 
            parameters[-1].name+='full_natGrad'
            Waug=parameters[-1]
            WReIm=K.variable(value=Waug[:Waug.shape[1]/2,:].eval(),name=Waug.name)
            WaugFull=K.concatenate( (WReIm, K.concatenate((-WReIm[:,WReIm.shape[1]/2:],WReIm[:,:WReIm.shape[1]/2]),axis=1)),axis=0 )
            lin_output_new = theano.clone(lin_output,replace={parameters[-1]:WaugFull})
            lin_output = lin_output_new
            parameters[-1]=WReIm

        self.trainable_weights = parameters

        return lin_output