Python tensor.inv函数代码示例

    def loss(self, n_samples, regularization_strength, mix, mu, sigma):
        log_sum_loss = -tensor.sum(tensor.log(
                            tensor.sum(mix * tensor.inv(np.sqrt(2 * np.pi) * sigma) *
                                       tensor.exp(tensor.neg(tensor.sqr(mu - self.target_vector)) *
                                                  tensor.inv(2 * tensor.sqr(sigma))), axis=0)
        ))

        # reg_loss = tensor.sum(tensor.sqr(self.layers.values()[0].W))
        # for layer in self.layers.values()[1:]:
        #     reg_loss += tensor.sum(tensor.sqr(layer.W))

        # regularization = 1/n_samples * regularization_strength/2 * reg_loss

        return log_sum_loss #+ regularization

    def get_output_for(self, input, deterministic=False,
                       batch_norm_use_averages=None,
                       batch_norm_update_averages=None, **kwargs):

        self.count = self.count + 1
        self.alpha = 5.0 / (10 + self.count)
        # self.alpha = 1.0 / (self.count^2)

        input_mean = input.mean(self.axes)
        input_inv_std = T.inv(T.sqrt(input.var(self.axes) + self.epsilon))

        # Decide whether to use the stored averages or mini-batch statistics
        if batch_norm_use_averages is None:
            batch_norm_use_averages = deterministic
        use_averages = batch_norm_use_averages

        if use_averages:
            mean = self.mean
            inv_std = self.inv_std
        else:
            mean = input_mean
            inv_std = input_inv_std

        # Decide whether to update the stored averages
        if batch_norm_update_averages is None:
            batch_norm_update_averages = not deterministic
        update_averages = batch_norm_update_averages

        if update_averages:
            # Trick: To update the stored statistics, we create memory-aliased
            # clones of the stored statistics:
            running_mean = theano.clone(self.mean, share_inputs=False)
            running_inv_std = theano.clone(self.inv_std, share_inputs=False)
            # set a default update for them:
            running_mean.default_update = ((1 - self.alpha) * running_mean +
                                           self.alpha * input_mean)
            running_inv_std.default_update = ((1 - self.alpha) *
                                              running_inv_std +
                                              self.alpha * input_inv_std)
            # and make sure they end up in the graph without participating in
            # the computation (this way their default_update will be collected
            # and applied, but the computation will be optimized away):
            mean += 0 * running_mean    
            inv_std += 0 * running_inv_std

        # prepare dimshuffle pattern inserting broadcastable axes as needed
        param_axes = iter(range(input.ndim - len(self.axes)))
        pattern = ['x' if input_axis in self.axes
                   else next(param_axes)
                   for input_axis in range(input.ndim)]

        # apply dimshuffle pattern to all parameters
        beta = 0 if self.beta is None else self.beta.dimshuffle(pattern)
        gamma = 1 if self.gamma is None else self.gamma.dimshuffle(pattern)
        mean = mean.dimshuffle(pattern)
        inv_std = inv_std.dimshuffle(pattern)

        # normalize
        normalized = (input - mean) * (gamma * inv_std) + beta
        return normalized

def normal_log_likelihood_per_component(x, mu, sigma, mixing):
     return (
        MINUS_HALF_LOG_2PI
        - T.log(sigma)
        - 0.5 * T.inv(sigma**2) * (x - mu)**2
        + T.log(mixing)
    )

def __init():
    dataset = T.matrix("dataset", dtype=config.globalFloatType())
    trans_dataset = T.transpose(dataset)
    dot_mul = T.dot(dataset, trans_dataset)
    l2 = T.sqrt(T.sum(T.square(dataset), axis=1))
    
#     p =printing.Print("l2")
#     l2 = p(l2)
    
    l2_inv2 = T.inv(l2).dimshuffle(['x', 0])
#     p =printing.Print("l2_inv2")
#     l2_inv2 = p(l2_inv2)
    
    l2_inv1 = T.transpose(l2_inv2)
#     p =printing.Print("l2_inv1")
#     l2_inv1 = p(l2_inv1)
    
    l2_inv = T.dot(l2_inv1, l2_inv2)
    
#     p =printing.Print("l2_inv")
#     l2_inv = p(l2_inv)
    
    affinty = (T.mul(dot_mul, l2_inv) + 1) / 2
    globals()['__affinty_fun'] = theano.function(
             [dataset],
             [affinty],
             allow_input_downcast=True
             )

def set_generator_update_function(generator_rnn_model,
                                  generator_mean_model,
                                  generator_std_model,
                                  generator_optimizer,
                                  grad_clipping):

    # input data (time length * num_samples * input_dims)
    source_data = tensor.tensor3(name='source_data',
                                 dtype=floatX)

    target_data = tensor.tensor3(name='target_data',
                                 dtype=floatX)

    # set generator input data list
    generator_input_data_list = [source_data,]

    # get generator hidden data
    hidden_data = generator_rnn_model[0].forward(generator_input_data_list, is_training=True)[0]

    # get generator output data
    output_mean_data = get_tensor_output(input=hidden_data,
                                         layers=generator_mean_model,
                                         is_training=True)
    output_std_data = get_tensor_output(input=hidden_data,
                                        layers=generator_std_model,
                                        is_training=True)

    generator_cost  = -0.5*tensor.inv(2.0*tensor.sqr(output_std_data))*tensor.sqr(output_mean_data-target_data)
    generator_cost += -0.5*tensor.log(2.0*tensor.sqr(output_std_data)*numpy.pi)

    # set generator update
    generator_updates_cost = generator_cost.mean()
    generator_updates_dict = get_model_updates(layers=generator_rnn_model+generator_mean_model+generator_std_model,
                                               cost=generator_updates_cost,
                                               optimizer=generator_optimizer,
                                               use_grad_clip=grad_clipping)

    gradient_dict  = get_model_gradients(generator_rnn_model+generator_mean_model+generator_std_model, generator_updates_cost)
    gradient_norm  = 0.
    for grad in gradient_dict:
        gradient_norm += tensor.sum(grad**2)
        gradient_norm  = tensor.sqrt(gradient_norm)

    # set generator update inputs
    generator_updates_inputs  = [source_data,
                                 target_data,]

    # set generator update outputs
    generator_updates_outputs = [generator_cost, gradient_norm]

    # set generator update function
    generator_updates_function = theano.function(inputs=generator_updates_inputs,
                                                 outputs=generator_updates_outputs,
                                                 updates=generator_updates_dict,
                                                 on_unused_input='ignore')

    return generator_updates_function

def set_generator_update_function(
    generator_rnn_model, generator_mean_model, generator_std_model, generator_optimizer, grad_clipping
):

    # input data (time length * num_samples * input_dims)
    source_data = tensor.tensor3(name="source_data", dtype=floatX)

    target_data = tensor.tensor3(name="target_data", dtype=floatX)

    # set generator input data list
    generator_input_data_list = [source_data]

    # get generator hidden data
    hidden_data = generator_rnn_model[0].forward(generator_input_data_list, is_training=True)[0]
    hidden_data = hidden_data.dimshuffle(0, 2, 1, 3).flatten(3)

    # get generator output data
    output_mean_data = get_tensor_output(input=hidden_data, layers=generator_mean_model, is_training=True)
    # output_std_data = get_tensor_output(input=hidden_data,
    #                                     layers=generator_std_model,
    #                                     is_training=True)
    output_std_data = 0.22
    # get generator cost (time_length x num_samples x hidden_size)
    generator_cost = 0.5 * tensor.inv(2.0 * tensor.sqr(output_std_data)) * tensor.sqr(output_mean_data - target_data)
    generator_cost += tensor.log(output_std_data) + 0.5 * tensor.log(2.0 * numpy.pi)
    generator_cost = tensor.sum(generator_cost, axis=2)

    # set generator update
    generator_updates_cost = generator_cost.mean()
    generator_updates_dict = get_model_updates(
        layers=generator_rnn_model + generator_mean_model,
        cost=generator_updates_cost,
        optimizer=generator_optimizer,
        use_grad_clip=grad_clipping,
    )

    gradient_dict = get_model_gradients(generator_rnn_model + generator_mean_model, generator_updates_cost)
    gradient_norm = 0.0
    for grad in gradient_dict:
        gradient_norm += tensor.sum(grad ** 2)
    gradient_norm = tensor.sqrt(gradient_norm)

    # set generator update inputs
    generator_updates_inputs = [source_data, target_data]

    # set generator update outputs
    generator_updates_outputs = [generator_cost, gradient_norm]

    # set generator update function
    generator_updates_function = theano.function(
        inputs=generator_updates_inputs,
        outputs=generator_updates_outputs,
        updates=generator_updates_dict,
        on_unused_input="ignore",
    )

    return generator_updates_function

 def energy_function(feature_data, is_train=True):
     # feature-wise std
     feature_std_inv = T.inv(T.nnet.softplus(feature_std)+1e-10)
     # energy hidden-feature
     e = softplus(T.dot(feature_data*feature_std_inv, linear_w0)+linear_b0)
     e = T.sum(-e, axis=1)
     # energy feature prior
     e += 0.5*T.sum(T.sqr(feature_std_inv)*T.sqr(feature_data-feature_mean), axis=1)
     return e

    def logsum_loss(self, n_samples, l1_regularization_strength, l2_regularization_strength):
        log_sum_loss = -tensor.sum(tensor.log(
                            tensor.sum(self.mix * tensor.inv(np.sqrt(2 * np.pi) * self.sigma) *
                                       tensor.exp(tensor.neg(tensor.sqr(self.mu - self.target_vector)) *
                                                  tensor.inv(2 * tensor.sqr(self.sigma))), axis=0)
        ))

        l1_reg_loss = tensor.sum(np.abs(self.layers.values()[0].W))
        for layer in self.layers.values()[1:]:
            l1_reg_loss += tensor.sum(np.abs(layer.W))

        l2_reg_loss = tensor.sum(tensor.sqr(self.layers.values()[0].W))
        for layer in self.layers.values()[1:]:
            l2_reg_loss += tensor.sum(tensor.sqr(layer.W))

        l1_regularization = 1/n_samples * l1_regularization_strength/2 * l1_reg_loss

        l2_regularization = 1/n_samples * l2_regularization_strength/2 * l2_reg_loss

        return log_sum_loss + l1_regularization + l2_regularization

	def __spectral_matrix(self, covariance):
		egvalues, egmatrix = T.nlinalg.eig(covariance)
		egmatrix_inv = T.nlinalg.matrix_inverse(egmatrix)
		diag_sqr_inv = T.nlinalg.alloc_diag(
			T.inv(
				T.sqrt(
					T.switch(T.eq(egvalues,0), 0.001, egvalues)
				)
			)
		)
		return egmatrix.dot(diag_sqr_inv).dot(egmatrix_inv)

def standardize(layer, offset, scale, shared_axes):
    """
    Convenience function for standardizing inputs by applying a fixed offset
    and scale.  This is usually useful when you want the input to your network
    to, say, have zero mean and unit standard deviation over the feature
    dimensions.  This layer allows you to include the appropriate statistics to
    achieve this normalization as part of your network, and applies them to its
    input.  The statistics are supplied as the `offset` and `scale` parameters,
    which are applied to the input by subtracting `offset` and dividing by
    `scale`, sharing dimensions as specified by the `shared_axes` argument.

    Parameters
    ----------
    layer : a :class:`Layer` instance or a tuple
        The layer feeding into this layer, or the expected input shape.
    offset : Theano shared variable, expression, or numpy array
        The offset to apply (via subtraction) to the axis/axes being
        standardized.
    scale : Theano shared variable, expression or numpy array
        The scale to apply (via division) to the axis/axes being standardized.
    shared_axes : 'auto', int or tuple of int
        The axis or axes to share the offset and scale over. If ``'auto'`` (the
        default), share over all axes except for the second: this will share
        scales over the minibatch dimension for dense layers, and additionally
        over all spatial dimensions for convolutional layers.

    Examples
    --------
    Assuming your training data exists in a 2D numpy ndarray called
    ``training_data``, you can use this function to scale input features to the
    [0, 1] range based on the training set statistics like so:

    >>> import lasagne
    >>> import numpy as np
    >>> training_data = np.random.standard_normal((100, 20))
    >>> input_shape = (None, training_data.shape[1])
    >>> l_in = lasagne.layers.InputLayer(input_shape)
    >>> offset = training_data.min(axis=0)
    >>> scale = training_data.max(axis=0) - training_data.min(axis=0)
    >>> l_std = standardize(l_in, offset, scale, shared_axes=0)

    Alternatively, to z-score your inputs based on training set statistics, you
    could set ``offset = training_data.mean(axis=0)`` and
    ``scale = training_data.std(axis=0)`` instead.
    """
    # Subtract the offset
    layer = BiasLayer(layer, -offset, shared_axes)
    # Do not optimize the offset parameter
    layer.params[layer.b].remove('trainable')
    # Divide by the scale
    layer = ScaleLayer(layer, T.inv(scale), shared_axes)
    # Do not optimize the scales parameter
    layer.params[layer.scales].remove('trainable')
    return layer

def test_dnn_batchnorm_train():
    if not dnn.dnn_available(test_ctx_name):
        raise SkipTest(dnn.dnn_available.msg)
    if dnn.version(raises=False) < 5000:
        raise SkipTest("batch normalization requires cudnn v5+")
    utt.seed_rng()

    for mode in ('per-activation', 'spatial'):
        for vartype in (T.ftensor4, T.ftensor3, T.fmatrix, T.fvector):
            x, scale, bias = (vartype(n) for n in ('x', 'scale', 'bias'))
            ndim = x.ndim
            eps = 5e-3  # some non-standard value to test if it's used

            # forward pass
            out, x_mean, x_invstd = dnn.dnn_batch_normalization_train(
                x, scale, bias, mode, eps)
            # reference forward pass
            if mode == 'per-activation':
                axes = (0,)
            elif mode == 'spatial':
                axes = (0,) + tuple(range(2, ndim))
            x_mean2 = x.mean(axis=axes, keepdims=True)
            x_invstd2 = T.inv(T.sqrt(x.var(axis=axes, keepdims=True) + eps))
            scale2 = T.addbroadcast(scale, *axes)
            bias2 = T.addbroadcast(bias, *axes)
            out2 = (x - x_mean2) * (scale2 * x_invstd2) + bias2
            # backward pass
            dy = vartype('dy')
            grads = T.grad(None, wrt=[x, scale, bias], known_grads={out: dy})
            # reference backward pass
            grads2 = T.grad(None, wrt=[x, scale, bias], known_grads={out2: dy})
            # compile
            f = theano.function([x, scale, bias, dy],
                                [out, x_mean, x_invstd, out2, x_mean2, x_invstd2] +
                                grads + grads2, mode=mode_with_gpu)
            # run
            for data_shape in ((10, 20, 30, 40), (4, 3, 1, 1), (1, 1, 5, 5)):
                data_shape = data_shape[:ndim]
                param_shape = tuple(1 if d in axes else s
                                    for d, s in enumerate(data_shape))
                X = 4 + 3 * numpy.random.randn(*data_shape).astype('float32')
                Dy = -1 + 2 * numpy.random.randn(*data_shape).astype('float32')
                Scale = numpy.random.randn(*param_shape).astype('float32')
                Bias = numpy.random.randn(*param_shape).astype('float32')
                outputs = f(X, Scale, Bias, Dy)
                # compare outputs
                utt.assert_allclose(outputs[0], outputs[0 + 3])  # out
                utt.assert_allclose(outputs[1], outputs[1 + 3])  # mean
                utt.assert_allclose(outputs[2], outputs[2 + 3])  # invstd
                # compare gradients
                utt.assert_allclose(outputs[6], outputs[6 + 3])  # dx
                utt.assert_allclose(outputs[7], outputs[7 + 3], rtol=3e-3)  # dscale
                utt.assert_allclose(outputs[8], outputs[8 + 3])  # dbias

    def get_symbolic_thermal_hmm_params(log_prior_c: types.TheanoVector,
                                        log_trans_tcc: types.TheanoTensor3,
                                        log_emission_tc: types.TheanoMatrix,
                                        temperature: tt.scalar):
        inv_temperature = tt.inv(temperature)

        thermal_log_prior_c = inv_temperature * log_prior_c
        thermal_log_prior_c -= pm.math.logsumexp(thermal_log_prior_c)
        thermal_log_trans_tcc = inv_temperature * log_trans_tcc
        thermal_log_trans_tcc -= pm.math.logsumexp(thermal_log_trans_tcc, axis=-1)
        thermal_log_emission_tc = inv_temperature * log_emission_tc

        return thermal_log_prior_c, thermal_log_trans_tcc, thermal_log_emission_tc

    def predict(self, X1, y1, X2):
   
        cov_train = self.compute_cov_s(X1,self.N)
        cov_test  = self.compute_cov_s(X2,self.M)
        cov_te_tr = self.compute_cov(X1,X2,self.N,self.M)     
        cov_tr_te = cov_te_tr.T

        arg0  = T.inv(cov_train+self.noise**2 *T.identity_like(cov_train))
        #arg0  = T.inv(cov_train)
        arg1  = T.dot(cov_te_tr, arg0)
        mu    = T.dot(arg1,y1)
        sigma = cov_test - T.dot(arg1, cov_tr_te) 

        return mu,T.diag(sigma)

def logp(X):
    '''
    logp de la probabilidad de muchas gaussianas
    '''
#    print(X.shape.eval(), mu.shape.eval())
    err = T.reshape(X, (-1,2)) - T.reshape(mu, (-1,2))  # shaped as (n*m,2)

    S = T.inv(cov)  # np.linalg.inv(cov)

    E = (T.reshape(err, (-1, 2, 1)) *
         S *
         T.reshape(err, (-1, 1, 2))
         ).sum()

    return - E / 2

    def _whiten_input(self, n): 
        X = T.matrix('X', dtype=theano.config.floatX)

        cov = T.dot(X.T, X) / (n - 1)
        
        eigenvalues, eigenvectors = T.nlinalg.eig(cov)

        V = eigenvectors
        D = eigenvalues
        D_prime = T.nlinalg.alloc_diag(T.inv(T.sqrt(D + self.e_zca)))
                
        M = T.dot(V, T.dot(D_prime, V.T))

        # now the input has been rotated: each column is a sample
        return theano.function(inputs=[X], outputs=T.dot(M, X.T))