Python chainer.functions 模块，crelu() 实例源码

def decode(self,z):
        # pdb.set_trace()
        a = self.a_enc

        # If this function is coming from the sampling call, the batch size of z and a won't match. Manually handle that here.
        if (a.shape[0]!=z.shape[0]):
            a.volatile = 'ON'
            batch_size = z.shape[0]
            a.data = a.data[0:batch_size,:]

        net_input = F.concat((z,a), axis=1)

        h = F.crelu(self.plinx0(net_input))

        for i in range(self.num_layers-1):
            layer_name = 'plinx' + str(i+1)
            h = F.crelu(self[layer_name](h))

        self.pmu = self.plinx_mu(h)
        self.pln_var = self.plinx_ln_var(h)

        return self.pmu, self.pln_var

def encode_a(self, x):
        a_params = self.qlina0(x)
        a_params = self.qlina_batch_norm_0(a_params)
        a_params = F.crelu(a_params)

        for i in range(self.num_layers-1):
            layer_name = 'qlina' + str(i+1)
            a_params = self[layer_name](a_params)
            layer_name = 'qlina_batch_norm_' + str(i+1)
            a_params = self[layer_name](a_params)
            a_params = F.crelu(a_params)

        self.qmu_a = self.qlina_mu(a_params)
        self.qln_var_a = self.qlina_ln_var(a_params)

        return self.qmu_a, self.qln_var_a

def encode_z(self, x, a):
        # a = F.gaussian(self.qmu_a, self.qln_var_a) # This should be outside the encoding function. Pass the function a. 
        net_input = F.concat((x,a), axis=1)

        h = self.qlinz0(net_input)
        h = self.qlinz_batch_norm_0(h)
        h = F.crelu(h)

        for i in range(self.num_layers-1):
            layer_name = 'qlinz' + str(i+1)
            h = self[layer_name](h)
            layer_name = 'qlinz_batch_norm_' + str(i+1)
            h = self[layer_name](h)
            h = F.crelu(h)

        self.qmu_z = self.qlinz_mu(h)
        self.qln_var_z = self.qlinz_ln_var(h)

        return self.qmu_z, self.qln_var_z

def decode_a(self, z, x):
        net_input = F.concat((x,z), axis=1)

        h = self.plina0(net_input)
        h = self.plina_batch_norm_0(h)
        h = F.crelu(h)

        for i in range(self.num_layers-1):
            layer_name = 'plina' + str(i+1)
            h = self[layer_name](h)
            layer_name = 'plina_batch_norm_' + str(i+1)
            h = self[layer_name](h)
            h = F.crelu(h)

        self.pmu_a = self.plina_mu(h)
        self.pln_var_a = self.plina_ln_var(h)

        return self.pmu_a, self.pln_var_a

def encode_a(self, x):
        a_params = self.qlina0(x)
        a_params = self.qlina_batch_norm_0(a_params)
        a_params = F.crelu(a_params)

        for i in range(self.num_layers-1):
            layer_name = 'qlina' + str(i+1)
            a_params = self[layer_name](a_params)
            layer_name = 'qlina_batch_norm_' + str(i+1)
            a_params = self[layer_name](a_params)
            a_params = F.crelu(a_params)

        self.qmu_a = self.qlina_mu(a_params)
        self.qln_var_a = self.qlina_ln_var(a_params)

        return self.qmu_a, self.qln_var_a

def encode_z(self, x, a):
        # a = F.gaussian(self.qmu_a, self.qln_var_a) # This should be outside the encoding function. Pass the function a. 
        net_input = F.concat((x,a), axis=1)

        h = self.qlinz0(net_input)
        h = self.qlinz_batch_norm_0(h)
        h = F.crelu(h)

        for i in range(self.num_layers-1):
            layer_name = 'qlinz' + str(i+1)
            h = self[layer_name](h)
            layer_name = 'qlinz_batch_norm_' + str(i+1)
            h = self[layer_name](h)
            h = F.crelu(h)

        self.qmu_z = self.qlinz_mu(h)
        self.qln_var_z = self.qlinz_ln_var(h)

        return self.qmu_z, self.qln_var_z

def decode_a(self, z):
        # net_input = F.concat((x,z), axis=1)

        h = self.plina0(z)
        h = self.plina_batch_norm_0(h)
        h = F.crelu(h)

        for i in range(self.num_layers-1):
            layer_name = 'plina' + str(i+1)
            h = self[layer_name](h)
            layer_name = 'plina_batch_norm_' + str(i+1)
            h = self[layer_name](h)
            h = F.crelu(h)

        self.pmu_a = self.plina_mu(h)
        self.pln_var_a = self.plina_ln_var(h)

        return self.pmu_a, self.pln_var_a

def encode(self, x):
        h = self.qlin0(x)
        h = self.qlin_batch_norm_0(h)
        h = F.crelu(h)

        for i in range(self.num_layers-1):
            layer_name = 'qlin' + str(i+1)
            h = self[layer_name](h)

            layer_name = 'qlin_batch_norm_' + str(i+1)
            h = self[layer_name](h)
            h = F.crelu(h)

        self.qmu = self.qlin_mu(h)
        self.qln_var = self.qlin_ln_var(h)
        self.qh_vec_0 = self.qlin_h_vec_0(h)

        return self.qmu, self.qln_var, self.qh_vec_0

def decode(self, z):
        h = self.plin0(z)
        h = self.plin_batch_norm_0(h)
        h = F.crelu(h)

        for i in range(self.num_layers-1):
            layer_name = 'plin' + str(i+1)
            h = self[layer_name](h)

            layer_name = 'plin_batch_norm_' + str(i+1)
            h = self[layer_name](h)
            h = F.crelu(h)        

        self.p_ber_prob_logit = self.plin_ber_prob(h)

        return self.p_ber_prob_logit

def to_function(self):
        if self.nonlinearity.lower() == "clipped_relu":
            return clipped_relu()
        if self.nonlinearity.lower() == "crelu":
            return crelu()
        if self.nonlinearity.lower() == "elu":
            return elu()
        if self.nonlinearity.lower() == "hard_sigmoid":
            return hard_sigmoid()
        if self.nonlinearity.lower() == "leaky_relu":
            return leaky_relu()
        if self.nonlinearity.lower() == "relu":
            return relu()
        if self.nonlinearity.lower() == "sigmoid":
            return sigmoid()
        if self.nonlinearity.lower() == "softmax":
            return softmax()
        if self.nonlinearity.lower() == "softplus":
            return softplus()
        if self.nonlinearity.lower() == "tanh":
            return tanh()
        if self.nonlinearity.lower() == "bst":
            return bst()
        raise NotImplementedError()

def to_function(self):
        if self.nonlinearity.lower() == "clipped_relu":
            return clipped_relu()
        if self.nonlinearity.lower() == "crelu":
            return crelu()
        if self.nonlinearity.lower() == "elu":
            return elu()
        if self.nonlinearity.lower() == "hard_sigmoid":
            return hard_sigmoid()
        if self.nonlinearity.lower() == "leaky_relu":
            return leaky_relu()
        if self.nonlinearity.lower() == "relu":
            return relu()
        if self.nonlinearity.lower() == "sigmoid":
            return sigmoid()
        if self.nonlinearity.lower() == "softmax":
            return softmax()
        if self.nonlinearity.lower() == "softplus":
            return softplus()
        if self.nonlinearity.lower() == "tanh":
            return tanh()
        raise NotImplementedError()

def to_function(self):
        if self.nonlinearity.lower() == "clipped_relu":
            return clipped_relu()
        if self.nonlinearity.lower() == "crelu":
            return crelu()
        if self.nonlinearity.lower() == "elu":
            return elu()
        if self.nonlinearity.lower() == "hard_sigmoid":
            return hard_sigmoid()
        if self.nonlinearity.lower() == "leaky_relu":
            return leaky_relu()
        if self.nonlinearity.lower() == "relu":
            return relu()
        if self.nonlinearity.lower() == "sigmoid":
            return sigmoid()
        if self.nonlinearity.lower() == "softmax":
            return softmax()
        if self.nonlinearity.lower() == "softplus":
            return softplus()
        if self.nonlinearity.lower() == "tanh":
            return tanh()
        raise NotImplementedError()

def to_function(self):
        if self.nonlinearity.lower() == "clipped_relu":
            return clipped_relu()
        if self.nonlinearity.lower() == "crelu":
            return crelu()
        if self.nonlinearity.lower() == "elu":
            return elu()
        if self.nonlinearity.lower() == "hard_sigmoid":
            return hard_sigmoid()
        if self.nonlinearity.lower() == "leaky_relu":
            return leaky_relu()
        if self.nonlinearity.lower() == "relu":
            return relu()
        if self.nonlinearity.lower() == "sigmoid":
            return sigmoid()
        if self.nonlinearity.lower() == "softmax":
            return softmax()
        if self.nonlinearity.lower() == "softplus":
            return softplus()
        if self.nonlinearity.lower() == "tanh":
            return tanh()
        raise NotImplementedError()

def encode(self, x):
        h = F.crelu(self.qlin0(x))

        for i in range(self.num_layers-1):
            layer_name = 'qlin' + str(i+1)
            h = F.crelu(self[layer_name](h))

        self.qmu = self.qlin_mu(h)
        self.qln_var = self.qlin_ln_var(h)
        self.qh = self.qlin_h(h)

def decode(self, z):
        h = F.crelu(self.plin0(z))

        for i in range(self.num_layers-1):
            layer_name = 'plin' + str(i+1)
            h = F.crelu(self[layer_name](h))        

        self.p_ber_prob_logit = self.plin_ber_prob(h)

def iaf(self, z, h, lin1, lin2):
        ms = F.crelu(lin1(F.concat((z, h), axis=1)))
        ms = lin2(ms)
        m, s = F.split_axis(ms, 2, axis=1)
        s = F.sigmoid(s)
        z = s*z + (1-s)*m
        # pdb.set_trace()
        return z, -F.sum(F.log(s), axis=1)

def encode_a(self, x):
        a_params = F.crelu(self.qlina0(x))

        for i in range(self.num_layers-1):
            layer_name = 'qlina' + str(i+1)
            a_params = F.crelu(self[layer_name](a_params))

        self.qmu_a = self.qlina_mu(a_params)
        self.qln_var_a = self.qlina_ln_var(a_params)

        return self.qmu_a, self.qln_var_a

def encode_z(self, x, a):
        # a = F.gaussian(self.qmu_a, self.qln_var_a) # This should be outside the encoding function. Pass the function a. 
        net_input = F.concat((x,a), axis=1)

        h = F.crelu(self.qlinz0(net_input))
        for i in range(self.num_layers-1):
            layer_name = 'qlinz' + str(i+1)
            h = F.crelu(self[layer_name](h))

        self.qmu_z = self.qlinz_mu(h)
        self.qln_var_z = self.qlinz_ln_var(h)

        return self.qmu_z, self.qln_var_z

def decode(self,z):
        h = F.crelu(self.plinx0(z))

        for i in range(self.num_layers-1):
            layer_name = 'plinx' + str(i+1)
            h = F.crelu(self[layer_name](h))

        self.p_ber_prob_logit = self.plinx_ber_prob(h)

        return self.p_ber_prob_logit

def encode_a(self, x):
        a_params = F.crelu(self.qlina0(x))

        for i in range(self.num_layers-1):
            layer_name = 'qlina' + str(i+1)
            a_params = F.crelu(self[layer_name](a_params))

        self.qmu_a = self.qlina_mu(a_params)
        self.qln_var_a = self.qlina_ln_var(a_params)

        return self.qmu_a, self.qln_var_a

def encode_z(self, x, a):
        # a = F.gaussian(self.qmu_a, self.qln_var_a) # This should be outside the encoding function. Pass the function a. 
        net_input = F.concat((x,a), axis=1)

        h = F.crelu(self.qlinz0(net_input))
        for i in range(self.num_layers-1):
            layer_name = 'qlinz' + str(i+1)
            h = F.crelu(self[layer_name](h))

        self.qmu_z = self.qlinz_mu(h)
        self.qln_var_z = self.qlinz_ln_var(h)

        return self.qmu_z, self.qln_var_z

def decode_a(self, z, x):
        net_input = F.concat((x,z), axis=1)

        h = F.crelu(self.plina0(net_input))

        for i in range(self.num_layers-1):
            layer_name = 'plina' + str(i+1)
            h = F.crelu(self[layer_name](h))        

        self.pmu_a = self.plina_mu(h)
        self.pln_var_a = self.plina_ln_var(h)

        return self.pmu_a, self.pln_var_a

def decode(self,z):
        h = F.crelu(self.plinx0(z))

        for i in range(self.num_layers-1):
            layer_name = 'plinx' + str(i+1)
            h = F.crelu(self[layer_name](h))

        self.pmu = self.plinx_mu(h)
        self.pln_var = self.plinx_ln_var(h)

        return self.pmu, self.pln_var

def decode(self, z):
        h = F.crelu(self.plin0(z))

        for i in range(self.num_layers-1):
            layer_name = 'plin' + str(i+1)
            h = F.crelu(self[layer_name](h))        

        self.p_ber_prob_logit = self.plin_ber_prob(h)

def encode_a(self, x):
        a_params = F.crelu(self.qlina0(x))

        for i in range(self.num_layers-1):
            layer_name = 'qlina' + str(i+1)
            a_params = F.crelu(self[layer_name](a_params))

        self.qmu_a = self.qlina_mu(a_params)
        self.qln_var_a = self.qlina_ln_var(a_params)

        return self.qmu_a, self.qln_var_a

def encode_z(self, x, a):
        # a = F.gaussian(self.qmu_a, self.qln_var_a) # This should be outside the encoding function. Pass the function a. 
        net_input = F.concat((x,a), axis=1)

        h = F.crelu(self.qlinz0(net_input))
        for i in range(self.num_layers-1):
            layer_name = 'qlinz' + str(i+1)
            h = F.crelu(self[layer_name](h))

        self.qmu_z = self.qlinz_mu(h)
        self.qln_var_z = self.qlinz_ln_var(h)

        return self.qmu_z, self.qln_var_z

def decode_a(self, z):
        # net_input = F.concat((x,z), axis=1)

        h = F.crelu(self.plina0(z))

        for i in range(self.num_layers-1):
            layer_name = 'plina' + str(i+1)
            h = F.crelu(self[layer_name](h))        

        self.pmu_a = self.plina_mu(h)
        self.pln_var_a = self.plina_ln_var(h)

        return self.pmu_a, self.pln_var_a

def decode(self, z):
        h = F.crelu(self.plin0(z))

        for i in range(self.num_layers-1):
            layer_name = 'plin' + str(i+1)
            h = F.crelu(self[layer_name](h))        

        self.pmu = self.plin_mu(h)
        self.pln_var = self.plin_ln_var(h)

        return self.pmu, self.pln_var

def decode(self,z):
        h = self.plinx0(z)
        h = self.plinx_batch_norm_0(h)
        h = F.crelu(h)

        for i in range(self.num_layers-1):
            layer_name = 'plinx' + str(i+1)
            h = self[layer_name](h)
            layer_name = 'plinx_batch_norm_' + str(i+1)
            h = self[layer_name](h)
            h = F.crelu(h)

        self.p_ber_prob_logit = self.plinx_ber_prob(h)

        return self.p_ber_prob_logit

def decode(self, z):
        h = F.crelu(self.plin0(z))

        for i in range(self.num_layers-1):
            layer_name = 'plin' + str(i+1)
            h = F.crelu(self[layer_name](h))        

        self.pmu = self.plin_mu(h)
        self.pln_var = self.plin_ln_var(h)

        return self.pmu, self.qln_var

def encode(self, x):
        h = F.crelu(self.qlin0(x))

        for i in range(self.num_layers-1):
            layer_name = 'qlin' + str(i+1)
            h = F.crelu(self[layer_name](h))

        self.qmu = self.qlin_mu(h)
        self.qln_var = self.qlin_ln_var(h)

def decode(self, z):
        h = F.crelu(self.plin0(z))

        for i in range(self.num_layers-1):
            layer_name = 'plin' + str(i+1)
            h = F.crelu(self[layer_name](h))

        self.p_ber_prob_logit = self.plin_ber_prob(h)

def encode(self, x):
        h = F.crelu(self.qlin0(x))

        for i in range(self.num_layers-1):
            layer_name = 'qlin' + str(i+1)
            h = F.crelu(self[layer_name](h))

        self.qmu = self.qlin_mu(h)
        self.qln_var = self.qlin_ln_var(h)
        self.qh = self.qlin_h(h)

def decode(self, z):
        h = F.crelu(self.plin0(z))

        for i in range(self.num_layers-1):
            layer_name = 'plin' + str(i+1)
            h = F.crelu(self[layer_name](h))        

        self.pmu = self.plin_mu(h)
        self.pln_var = self.plin_ln_var(h)

def encode(self, x):
        h = F.crelu(self.qlin0(x))

        for i in range(self.num_layers-1):
            layer_name = 'qlin' + str(i+1)
            h = F.crelu(self[layer_name](h))

        self.qmu = self.qlin_mu(h)
        self.qln_var = self.qlin_ln_var(h)

def decode(self, z):
        h = F.crelu(self.plin0(z))

        for i in range(self.num_layers-1):
            layer_name = 'plin' + str(i+1)
            h = F.crelu(self[layer_name](h))        

        self.pmu = self.plin_mu(h)
        self.pln_var = self.plin_ln_var(h)

def encode(self, x):
        h = self.qlin0(x)
        h = self.qlin_batch_norm_0(h)
        h = F.crelu(h)

        for i in range(self.num_layers-1):
            layer_name = 'qlin' + str(i+1)
            h = self[layer_name](h)

            layer_name = 'qlin_batch_norm_' + str(i+1)
            h = self[layer_name](h)
            h = F.crelu(h)

        self.qmu = self.qlin_mu(h)
        self.qln_var = self.qlin_ln_var(h)

def decode(self, z):
        h = self.plin0(z)
        h = self.plin_batch_norm_0(h)
        h = F.crelu(h)

        for i in range(self.num_layers-1):
            layer_name = 'plin' + str(i+1)
            h = self[layer_name](h)

            layer_name = 'plin_batch_norm_' + str(i+1)
            h = self[layer_name](h)
            h = F.crelu(h)        

        self.p_ber_prob_logit = self.plin_ber_prob(h)

def encode_a(self, x):
        a_params = F.crelu(self.qlina0(x))

        for i in range(self.num_layers-1):
            layer_name = 'qlina' + str(i+1)
            a_params = F.crelu(self[layer_name](a_params))

        self.qmu_a = self.qlina_mu(a_params)
        self.qln_var_a = self.qlina_ln_var(a_params)

        return self.qmu_a, self.qln_var_a

def encode_z(self, x, a):
        # a = F.gaussian(self.qmu_a, self.qln_var_a) # This should be outside the encoding function. Pass the function a. 
        net_input = F.concat((x,a), axis=1)

        h = F.crelu(self.qlinz0(net_input))
        for i in range(self.num_layers-1):
            layer_name = 'qlinz' + str(i+1)
            h = F.crelu(self[layer_name](h))

        self.qmu_z = self.qlinz_mu(h)
        self.qln_var_z = self.qlinz_ln_var(h)

        return self.qmu_z, self.qln_var_z

def decode_a(self, z):
        # net_input = F.concat((x,z), axis=1)

        h = F.crelu(self.plina0(z))

        for i in range(self.num_layers-1):
            layer_name = 'plina' + str(i+1)
            h = F.crelu(self[layer_name](h))        

        self.pmu_a = self.plina_mu(h)
        self.pln_var_a = self.plina_ln_var(h)

        return self.pmu_a, self.pln_var_a

def encode(self, x):
        h = F.crelu(self.qlin0(x))

        for i in range(self.num_layers-1):
            layer_name = 'qlin' + str(i+1)
            h = F.crelu(self[layer_name](h))

        self.qmu = self.qlin_mu(h)
        self.qln_var = self.qlin_ln_var(h)
        self.qh_vec_0 = self.qlin_h_vec_0(h)

        return self.qmu, self.qln_var, self.qh_vec_0

def decode(self, z):
        h = F.crelu(self.plin0(z))

        for i in range(self.num_layers-1):
            layer_name = 'plin' + str(i+1)
            h = F.crelu(self[layer_name](h))        

        self.p_ber_prob_logit = self.plin_ber_prob(h)

        return self.p_ber_prob_logit

def encode(self, x):
        h = F.crelu(self.qlin0(x))

        for i in range(self.num_layers-1):
            layer_name = 'qlin' + str(i+1)
            h = F.crelu(self[layer_name](h))

        self.qmu = self.qlin_mu(h)
        self.qln_var = self.qlin_ln_var(h)

        return self.qmu, self.qln_var

def encode(self, x):
        h = self.qlin0(x)
        h = self.qlin_batch_norm_0(h)
        h = F.crelu(h)

        for i in range(self.num_layers-1):
            layer_name = 'qlin' + str(i+1)
            h = self[layer_name](h)

            layer_name = 'qlin_batch_norm_' + str(i+1)
            h = self[layer_name](h)
            h = F.crelu(h)

        self.qmu = self.qlin_mu(h)
        self.qln_var = self.qlin_ln_var(h)

def decode(self, z):
        h = self.plin0(z)
        h = self.plin_batch_norm_0(h)
        h = F.crelu(h)

        for i in range(self.num_layers-1):
            layer_name = 'plin' + str(i+1)
            h = self[layer_name](h)

            layer_name = 'plin_batch_norm_' + str(i+1)
            h = self[layer_name](h)
            h = F.crelu(h)        

        self.p_ber_prob_logit = self.plin_ber_prob(h)

def encode(self, x):
       h = F.crelu(self.qlin0(x))

       for i in range(self.num_layers-1):
           layer_name = 'qlin' + str(i+1)
           h = F.crelu(self[layer_name](h))

       self.qmu = self.qlin_mu(h)
       self.qln_var = self.qlin_ln_var(h)

       return self.qmu, self.qln_var

def decode(self, z):
        h = F.crelu(self.plin0(z))

        for i in range(self.num_layers-1):
            layer_name = 'plin' + str(i+1)
            h = F.crelu(self[layer_name](h))

        self.p_ber_prob_logit = self.plin_ber_prob(h)

        return self.p_ber_prob_logit

def __call__(self, x):
        return functions.crelu(x, self.axis)

def __init__(self, axis=1):
        self._function = "crelu"
        self.axis = axis