Python tensor.log方法代码示例

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import log [as 别名]
def ctc_update_log_p(skip_idxs, zeros, active, log_p_curr, log_p_prev):
    active_skip_idxs = skip_idxs[(skip_idxs < active).nonzero()]
    active_next = T.cast(T.minimum(
        T.maximum(
            active + 1,
            T.max(T.concatenate([active_skip_idxs, [-1]])) + 2 + 1
        ), log_p_curr.shape[0]), 'int32')

    common_factor = T.max(log_p_prev[:active])
    p_prev = T.exp(log_p_prev[:active] - common_factor)
    _p_prev = zeros[:active_next]
    # copy over
    _p_prev = T.set_subtensor(_p_prev[:active], p_prev)
    # previous transitions
    _p_prev = T.inc_subtensor(_p_prev[1:], _p_prev[:-1])
    # skip transitions
    _p_prev = T.inc_subtensor(_p_prev[active_skip_idxs + 2], p_prev[active_skip_idxs])
    updated_log_p_prev = T.log(_p_prev) + common_factor

    log_p_next = T.set_subtensor(
        zeros[:active_next],
        log_p_curr[:active_next] + updated_log_p_prev
    )
    return active_next, log_p_next 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import log [as 别名]
def reduce_log_sum(tensor, axis=None, guaranteed_finite=False):
    """
    Sum probabilities in the log domain, i.e return
        log(e^vec[0] + e^vec[1] + ...)
        = log(e^x e^(vec[0]-x) + e^x e^(vec[1]-x) + ...)
        = log(e^x [e^(vec[0]-x) + e^(vec[1]-x) + ...])
        = log(e^x) + log(e^(vec[0]-x) + e^(vec[1]-x) + ...)
        = x + log(e^(vec[0]-x) + e^(vec[1]-x) + ...)
    For numerical stability, we choose x = max(vec)
    Note that if x is -inf, that means all values are -inf,
    so the answer should be -inf. In this case, choose x = 0
    """
    maxval = T.max(tensor, axis)
    maxval_full = T.max(tensor, axis, keepdims=True)
    if not guaranteed_finite:
        maxval = T.switch(T.isfinite(maxval), maxval, T.zeros_like(maxval))
        maxval_full = T.switch(T.isfinite(maxval_full), maxval_full, T.zeros_like(maxval_full))
    reduced_sum = T.sum(T.exp(tensor - maxval_full), axis)
    logsum = maxval + T.log(reduced_sum)
    return logsum 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import log [as 别名]
def get_output_for(self, input, init=False, **kwargs):
        if input.ndim > 2:
            # if the input has more than two dimensions, flatten it into a
            # batch of feature vectors.
            input = input.flatten(2)
        
        activation = T.tensordot(input, self.W, [[1], [0]])
        abs_dif = (T.sum(abs(activation.dimshuffle(0,1,2,'x') - activation.dimshuffle('x',1,2,0)),axis=2)
                    + 1e6 * T.eye(input.shape[0]).dimshuffle(0,'x',1))

        if init:
            mean_min_abs_dif = 0.5 * T.mean(T.min(abs_dif, axis=2),axis=0)
            abs_dif /= mean_min_abs_dif.dimshuffle('x',0,'x')
            self.init_updates = [(self.log_weight_scale, self.log_weight_scale-T.log(mean_min_abs_dif).dimshuffle(0,'x'))]
        
        f = T.sum(T.exp(-abs_dif),axis=2)

        if init:
            mf = T.mean(f,axis=0)
            f -= mf.dimshuffle('x',0)
            self.init_updates.append((self.b, -mf))
        else:
            f += self.b.dimshuffle('x',0)

        return T.concatenate([input, f], axis=1) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import log [as 别名]
def __init__(self,convolutional_layers,feature_maps,filter_shapes,poolsize,feedforward_layers,feedforward_nodes,classes,learning_rate,regularization):
        self.input = T.tensor4()
        self.convolutional_layers = []
        self.convolutional_layers.append(convolutional_layer(self.input,feature_maps[1],feature_maps[0],filter_shapes[0][0],filter_shapes[0][1],poolsize[0]))
        for i in range(1,convolutional_layers):
            self.convolutional_layers.append(convolutional_layer(self.convolutional_layers[i-1].output,feature_maps[i+1],feature_maps[i],filter_shapes[i][0],filter_shapes[i][1],poolsize[i]))
        self.feedforward_layers = []
        self.feedforward_layers.append(feedforward_layer(self.convolutional_layers[-1].output.flatten(2),flattened,feedforward_nodes[0]))
        for i in range(1,feedforward_layers):
            self.feedforward_layers.append(feedforward_layer(self.feedforward_layers[i-1].output,feedforward_nodes[i-1],feedforward_nodes[i]))
        self.output_layer = feedforward_layer(self.feedforward_layers[-1].output,feedforward_nodes[-1],classes)
        self.params = []
        for l in self.convolutional_layers + self.feedforward_layers:
            self.params.extend(l.get_params())
        self.params.extend(self.output_layer.get_params())
        self.target = T.matrix()
        self.output = self.output_layer.output
        self.cost = -self.target*T.log(self.output)-(1-self.target)*T.log(1-self.output)
        self.cost = self.cost.mean()
        for i in range(convolutional_layers+feedforward_layers+1):
            self.cost += regularization*(self.params[2*i]**2).mean()
        self.gparams = [T.grad(self.cost, param) for param in self.params]
        self.propogate = theano.function([self.input,self.target],self.cost,updates=[(param,param-learning_rate*gparam) for param,gparam in zip(self.params,self.gparams)],allow_input_downcast=True)
        self.classify = theano.function([self.input],self.output,allow_input_downcast=True) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import log [as 别名]
def __init__(self,classes,hidden_layers,features,nodes_per_hidden_layer,learning_rate,regularization):
        self.hidden_layers = []
        self.hidden_layers.append(layer(features,nodes_per_hidden_layer))
        for i in range(hidden_layers-1):
            self.hidden_layers.append(layer(nodes_per_hidden_layer,nodes_per_hidden_layer))
        self.output_layer = layer(nodes_per_hidden_layer,classes)
        self.params = []
        for l in self.hidden_layers:
            self.params.extend(l.get_params())
        self.params.extend(self.output_layer.get_params())
        self.A = T.matrix()
        self.t = T.matrix()
        self.s = 1/(1+T.exp(-T.dot(self.A,self.params[0])-self.params[1]))
        for i in range(hidden_layers):
            self.s = 1/(1+T.exp(-T.dot(self.s,self.params[2*(i+1)])-self.params[2*(i+1)+1]))
        self.cost = -self.t*T.log(self.s)-(1-self.t)*T.log(1-self.s)
        self.cost = self.cost.mean()
        for i in range(hidden_layers+1):
            self.cost += regularization*(self.params[2*i]**2).mean()
        self.gparams = [T.grad(self.cost, param) for param in self.params]
        self.propogate = theano.function([self.A,self.t],self.cost,updates=[(param,param-learning_rate*gparam) for param,gparam in zip(self.params,self.gparams)],allow_input_downcast=True)
        self.classify = theano.function([self.A],self.s,allow_input_downcast=True) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import log [as 别名]
def __init__(self,convolutional_layers,feature_maps,filter_shapes,poolsize,feedforward_layers,feedforward_nodes,classes,regularization):
        self.input = T.tensor4()
        self.convolutional_layers = []
        self.convolutional_layers.append(convolutional_layer(self.input,feature_maps[1],feature_maps[0],filter_shapes[0][0],filter_shapes[0][1],poolsize[0]))
        for i in range(1,convolutional_layers):
            self.convolutional_layers.append(convolutional_layer(self.convolutional_layers[i-1].output,feature_maps[i+1],feature_maps[i],filter_shapes[i][0],filter_shapes[i][1],poolsize[i]))
        self.feedforward_layers = []
        self.feedforward_layers.append(feedforward_layer(self.convolutional_layers[-1].output.flatten(2),flattened,feedforward_nodes[0]))
        for i in range(1,feedforward_layers):
            self.feedforward_layers.append(feedforward_layer(self.feedforward_layers[i-1].output,feedforward_nodes[i-1],feedforward_nodes[i]))
        self.output_layer = feedforward_layer(self.feedforward_layers[-1].output,feedforward_nodes[-1],classes)
        self.params = []
        for l in self.convolutional_layers + self.feedforward_layers:
            self.params.extend(l.get_params())
        self.params.extend(self.output_layer.get_params())
        self.target = T.matrix()
        self.output = self.output_layer.output
        self.cost = -self.target*T.log(self.output)-(1-self.target)*T.log(1-self.output)
        self.cost = self.cost.mean()
        for i in range(convolutional_layers+feedforward_layers+1):
            self.cost += regularization*(self.params[2*i]**2).mean()
        self.updates = self.adam(self.cost,self.params)
        self.propogate = theano.function([self.input,self.target],self.cost,updates=self.updates,allow_input_downcast=True)
        self.classify = theano.function([self.input],self.output,allow_input_downcast=True) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import log [as 别名]
def __init__(self,convolutional_layers,feature_maps,filter_shapes,feedforward_layers,feedforward_nodes,classes):
        self.input = T.tensor4()        
        self.convolutional_layers = []
        self.convolutional_layers.append(convolutional_layer(self.input,feature_maps[1],feature_maps[0],filter_shapes[0][0],filter_shapes[0][1]))
        for i in range(1,convolutional_layers):
            if i==2 or i==4:
                self.convolutional_layers.append(convolutional_layer(self.convolutional_layers[i-1].output,feature_maps[i+1],feature_maps[i],filter_shapes[i][0],filter_shapes[i][1],maxpool=(2,2)))
            else:
                self.convolutional_layers.append(convolutional_layer(self.convolutional_layers[i-1].output,feature_maps[i+1],feature_maps[i],filter_shapes[i][0],filter_shapes[i][1]))
        self.feedforward_layers = []
        self.feedforward_layers.append(feedforward_layer(self.convolutional_layers[-1].output.flatten(2),20480,feedforward_nodes[0]))
        for i in range(1,feedforward_layers):
            self.feedforward_layers.append(feedforward_layer(self.feedforward_layers[i-1].output,feedforward_nodes[i-1],feedforward_nodes[i]))
        self.output_layer = feedforward_layer(self.feedforward_layers[-1].output,feedforward_nodes[-1],classes)
        self.params = []
        for l in self.convolutional_layers + self.feedforward_layers:
            self.params.extend(l.get_params())
        self.params.extend(self.output_layer.get_params())
        self.target = T.matrix()
        self.output = self.output_layer.output
        self.cost = -self.target*T.log(self.output)-(1-self.target)*T.log(1-self.output)
        self.cost = self.cost.mean()
        self.updates = self.adam(self.cost, self.params)
        self.propogate = theano.function([self.input,self.target],self.cost,updates=self.updates,allow_input_downcast=True)
        self.classify = theano.function([self.input],self.output,allow_input_downcast=True) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import log [as 别名]
def __init__(self,convolutional_layers,feature_maps,filter_shapes,feedforward_layers,feedforward_nodes,classes):
        self.input = T.tensor4()        
        self.convolutional_layers = []
        self.convolutional_layers.append(convolutional_layer(self.input,feature_maps[1],feature_maps[0],filter_shapes[0][0],filter_shapes[0][1]))
        for i in range(1,convolutional_layers):
            if i==3:
                self.convolutional_layers.append(convolutional_layer(self.convolutional_layers[i-1].output,feature_maps[i+1],feature_maps[i],filter_shapes[i][0],filter_shapes[i][1],maxpool=(2,2)))
            else:
                self.convolutional_layers.append(convolutional_layer(self.convolutional_layers[i-1].output,feature_maps[i+1],feature_maps[i],filter_shapes[i][0],filter_shapes[i][1]))
        self.feedforward_layers = []
        self.feedforward_layers.append(feedforward_layer(self.convolutional_layers[-1].output.flatten(2),40000,feedforward_nodes[0]))
        for i in range(1,feedforward_layers):
            self.feedforward_layers.append(feedforward_layer(self.feedforward_layers[i-1].output,feedforward_nodes[i-1],feedforward_nodes[i]))
        self.output_layer = feedforward_layer(self.feedforward_layers[-1].output,feedforward_nodes[-1],classes)
        self.params = []
        for l in self.convolutional_layers + self.feedforward_layers:
            self.params.extend(l.get_params())
        self.params.extend(self.output_layer.get_params())
        self.target = T.matrix()
        self.output = self.output_layer.output
        self.cost = -self.target*T.log(self.output)-(1-self.target)*T.log(1-self.output)
        self.cost = self.cost.mean()
        self.updates = self.adam(self.cost, self.params)
        self.propogate = theano.function([self.input,self.target],self.cost,updates=self.updates,allow_input_downcast=True)
        self.classify = theano.function([self.input],self.output,allow_input_downcast=True) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import log [as 别名]
def test_local_softmax_grad_optimization_and_big_input(self):
        """Test the Logsoftmax's grad substitution.

        Check that Log(Softmax(x))'s grad is substituted with Logsoftmax(x)'s
        grad and that the new operation does not explode for big inputs.
        Note that only the grad is checked.
        """
        m = theano.config.mode
        m = theano.compile.get_mode(m)
        m.check_isfinite = False
        # some inputs that are large to make the gradient explode in the non
        # optimized case
        a = numpy.exp(10 * numpy.random.rand(5, 10).astype(theano.config.floatX))

        def myfunc(x):
            sm = tensor.nnet.softmax(x)
            logsm = tensor.log(sm)
            return logsm
        # We set step to 0.1 because for big values we need a big epsilon
        utt.verify_grad(myfunc, [a], eps=0.1, mode=m)
        f = theano.function([], myfunc(a))
        self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace')) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import log [as 别名]
def test_stabilize_log_softmax():
    mode = theano.compile.mode.get_default_mode()
    mode = mode.including('local_log_softmax', 'specialize')

    x = matrix()
    y = softmax(x)
    z = theano.tensor.log(y)

    f = theano.function([x], z, mode=mode)
    assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')

    # check that the softmax has been optimized out
    for node in f.maker.fgraph.toposort():
        assert not isinstance(node.op, y.owner.op.__class__)

    # call the function so debug mode can verify the optimized
    # version matches the unoptimized version
    rng = numpy.random.RandomState([2012, 8, 22])
    f(numpy.cast[config.floatX](rng.randn(2, 3))) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import log [as 别名]
def test_grad_log1msigm(self):
        # At some point, this returned nan, because (1 - sigm(x)) was
        # on both the numerator and the denominator of a fraction,
        # but the two nodes in question had not been merged.
        x = tensor.matrix('x')
        lr = tensor.scalar('lr')

        s = sigmoid(x)
        l = T.log(1 - s)
        c = l.mean()
        ux = x - lr * theano.grad(c, x)

        # Before the optimization, inf and NaN will be produced in the graph,
        # and DebugMode will complain. Everything is fine afterwards.
        mode = self.get_mode()
        if not isinstance(mode, theano.compile.DebugMode):
            f = theano.function([x, lr], ux, mode=mode)
            assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
            ux_v = f([[50]], 0.1)
            assert not numpy.isnan(ux_v) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import log [as 别名]
def compute_log_ei(self, x, incumbent):

        Kzz = compute_kernel(self.lls, self.lsf, self.z, self.z) + T.eye(self.z.shape[ 0 ]) * self.jitter * T.exp(self.lsf)
        KzzInv = T.nlinalg.MatrixInversePSD()(Kzz)
        LLt = T.dot(self.LParamPost, T.transpose(self.LParamPost))
        covCavityInv = KzzInv + LLt * casting(self.n_points - self.set_for_training) / casting(self.n_points)
        covCavity = T.nlinalg.MatrixInversePSD()(covCavityInv)
        meanCavity = T.dot(covCavity, casting(self.n_points - self.set_for_training) / casting(self.n_points) * self.mParamPost)
        KzzInvcovCavity = T.dot(KzzInv, covCavity)
        KzzInvmeanCavity = T.dot(KzzInv, meanCavity)
        Kxz = compute_kernel(self.lls, self.lsf, x, self.z)
        B = T.dot(KzzInvcovCavity, KzzInv) - KzzInv 
        v_out = T.exp(self.lsf) + T.dot(Kxz * T.dot(Kxz, B), T.ones_like(self.z[ : , 0 : 1 ])) # + T.exp(self.lvar_noise)
        m_out = T.dot(Kxz, KzzInvmeanCavity)
        s = (incumbent - m_out) / T.sqrt(v_out)

        log_ei = T.log((incumbent - m_out) * ratio(s) + T.sqrt(v_out)) + log_n_pdf(s)

        return log_ei 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import log [as 别名]
def softmaxReScale(self,energy_,threshould):
#{{{
        #in energy_, the goundthrud should be max
        assert energy_.ndim==1;
        #convert threshould from percentage to energy_;
        threshould_=T.log(T.exp(energy_-energy_.max()).sum())+T.log(threshould)+energy_.max()
        energy=self.reScale(energy_,threshould_);
        return T.nnet.softmax(energy);
    #}}} 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import log [as 别名]
def log_sum_exp(x, axis=None):
    """
    Sum probabilities in the log-space.
    """
    xmax = x.max(axis=axis, keepdims=True)
    xmax_ = x.max(axis=axis)
    return xmax_ + T.log(T.exp(x - xmax).sum(axis=axis)) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import log [as 别名]
def log(x):
    return T.log(x)