Python theano.clone函数代码示例

 def get_output_for(self, input, deterministic=False, **kwargs):
     if deterministic:
         # use stored mean and std
         mean = self.mean
         std = self.std
     else:
         # use this batch's mean and std
         mean = input.mean(self.axes, keepdims=True)
         std = input.std(self.axes, keepdims=True)
         # and update the stored mean and std:
         # we create (memory-aliased) clones of the stored mean and std
         running_mean = theano.clone(self.mean, share_inputs=False)
         running_std = theano.clone(self.std, share_inputs=False)
         # set a default update for them
         running_mean.default_update = (1 - self.alpha) * running_mean + self.alpha * mean
         running_std.default_update = (1 - self.alpha) * running_std + self.alpha * std
         # and include them in the graph so their default updates will be
         # applied (although the expressions will be optimized away later)
         mean += 0 * running_mean
         std += 0 * running_std
     std += self.epsilon
     mean = T.addbroadcast(mean, *self.axes)
     std = T.addbroadcast(std, *self.axes)
     beta = T.addbroadcast(self.beta, *self.axes)
     gamma = T.addbroadcast(self.gamma, *self.axes)
     normalized = (input - mean) * (gamma / std) + beta
     return self.nonlinearity(normalized)

    def __init__(self, freq, activation, input, target_idx, task_loss, surrogate_loss,
                 hyperparameter, learning_rate, batch_generator, n_batches,
                 factor=1.5, n_updates=10):
        Extension.__init__(self, 'adapt_zloss', freq)

        self.batch_generator = batch_generator
        self.n_batches = n_batches
        self.learning_rate = learning_rate
        self.hyperparameter = hyperparameter
        self.factor = factor
        self.n_updates = n_updates

        # grad = theano.grad(surrogate_loss, activation)
        # new_activation = activation - learning_rate * grad
        self.fun_activation = theano.function([input], activation)

        activation_bis = tensor.matrix()
        surr_loss_bis = theano.clone(surrogate_loss,
                                     replace={activation: activation_bis})
        grad = theano.grad(surr_loss_bis, activation_bis)
        new_activation = activation_bis - 100*learning_rate * grad

        task_loss_bis = theano.clone(task_loss,
                                     replace={activation: new_activation})

        self.fun_update_task_loss = theano.function(
                [activation_bis, target_idx], [task_loss_bis, new_activation])

def filter_and_prob(inpt, transition, emission,
           visible_noise_mean, visible_noise_cov,
           hidden_noise_mean, hidden_noise_cov,
           initial_hidden, initial_hidden_cov):
    step = forward_step(
        transition, emission,
        visible_noise_mean, visible_noise_cov,
        hidden_noise_mean, hidden_noise_cov)

    hidden_mean_0 = T.zeros_like(hidden_noise_mean).dimshuffle('x', 0)
    hidden_cov_0 = T.zeros_like(hidden_noise_cov).dimshuffle('x', 0, 1)
    f0, F0, ll0 = step(inpt[0], hidden_mean_0, hidden_cov_0)
    replace = {hidden_noise_mean: initial_hidden, 
               hidden_noise_cov: initial_hidden_cov}
    f0 = theano.clone(f0, replace)
    F0 = theano.clone(F0, replace)
    ll0 = theano.clone(ll0, replace)

    (f, F, ll), _ = theano.scan(
        step,
        sequences=inpt[1:],
        outputs_info=[f0, F0, None])

    ll = ll.sum(axis=0)

    f = T.concatenate([T.shape_padleft(f0), f])
    F = T.concatenate([T.shape_padleft(F0), F])
    ll += ll0

    return f, F, ll

    def forward(self,input_org,train=True,update_batch_stat=True,finetune=False):
        print "Layer/BatchNormalization"
        ldim,cdim,rdim = self._internal_shape(input_org)
        input = input_org.reshape((ldim,cdim,rdim))
        if (train):
            mean = T.mean(input, axis=(0, 2), keepdims=True )
            var = T.mean((input-mean)**2, axis=(0, 2), keepdims=True)

            if(update_batch_stat):
                finetune_N = theano.clone(self.finetune_N, share_inputs=False)
                if(finetune):
                    finetune_N.default_update = finetune_N+1
                    ratio = T.cast(1-1.0/(finetune_N+1),theano.config.floatX)
                else:
                    finetune_N.default_update = 0
                    ratio = self.moving_avg_ratio
                m = ldim*rdim
                scale = T.cast(m/(m-1.0),theano.config.floatX)
                est_mean = theano.clone(self.est_mean, share_inputs=False)
                est_var = theano.clone(self.est_var, share_inputs=False)
                est_mean.default_update = T.cast(ratio*self.est_mean + (1-ratio)*mean,theano.config.floatX)
                est_var.default_update = T.cast(ratio*self.est_var + (1-ratio)*scale*var,theano.config.floatX)
                mean += 0 * est_mean
                var += 0 * est_var
            output = self._pbc(self.gamma) * (input - self._pbc(mean)) \
                     / T.sqrt(1e-6+self._pbc(var)) + self._pbc(self.beta)

        else:
            output = self._pbc(self.gamma) * (input - self._pbc(self.est_mean)) \
                     / T.sqrt(1e-6+self._pbc(self.est_var)) + self._pbc(self.beta)

        return output.reshape(input_org.shape)

    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 _make_loss_functions(self, mode=None):
        """Return pair (f_loss, f_d_loss) of functions.

         - f_loss returns the current loss,
         - f_d_loss returns the gradient of that loss wrt parameters,
        """
        rng = T.shared_randomstreams.RandomStreams()

        # Drop out inpts.
        inpt = self.exprs['inpt']
        inpt_dropped_out = corrupt.mask(inpt, self.p_dropout_inpt, rng)
        givens = {inpt: inpt_dropped_out}
        loss = theano.clone(self.exprs['loss'], givens)

        n_layers = len(self.n_hiddens)
        for i in range(n_layers - 1):
            # Drop out hidden.
            hidden = self.exprs['hidden_%i' % i]
            hidden_dropped_out = corrupt.mask(hidden, self.p_dropout_hidden, rng)
            givens = {hidden: hidden_dropped_out}
            loss = theano.clone(loss, givens)

        d_loss = T.grad(loss, self.parameters.flat)

        f_loss = self.function(['inpt', 'target'], loss, explicit_pars=True,
                               mode=mode)
        f_d_loss = self.function(['inpt', 'target'], d_loss, explicit_pars=True,
                                 mode=mode)
        return f_loss, f_d_loss

    def _init_exprs(self):
        # Here we need to replace the input with a corrupted version. If we do
        # so naively by calling clone on the loss, the targets (which are
        # identical to the inputs in thesense of identity in programming) the
        # targets will be replaced as well. Instead, we just want to thave the
        # inputs replaced. Thus we first clone the output of the model and
        # replace the input with the corrupted input. This will not change the
        # targets. Afterwards, we put that corruption into the loss as well.
        super(DenoisingAutoEncoder, self)._init_exprs()
        if self.noise_type == 'gauss':
            corrupted_inpt = corrupt.gaussian_perturb(
                self.exprs['inpt'], self.c_noise)
        elif self.noise_type == 'mask':
            corrupted_inpt = corrupt.mask(
                self.exprs['inpt'], self.c_noise)

        output_from_corrupt = theano.clone(
            self.exprs['output'],
            {self.exprs['inpt']: corrupted_inpt}
        )

        score = self.exprs['loss']
        loss = theano.clone(
            self.exprs['loss'],
            {self.exprs['output']: output_from_corrupt})

        self.exprs.update(get_named_variables(locals(), overwrite=True))

    def apply_replacements(self, node, deterministic=False,
                           include=None, exclude=None,
                           more_replacements=None):
        """
        Replace variables in graph with variational approximation. By default, replaces all variables

        Parameters
        ----------
        node : Theano Variables (or Theano expressions)
            node or nodes for replacements
        deterministic : bool
            whether to use zeros as initial distribution
            if True - zero initial point will produce constant latent variables
        include : list
            latent variables to be replaced
        exclude : list
            latent variables to be excluded for replacements
        more_replacements : dict
            add custom replacements to graph, e.g. change input source

        Returns
        -------
        node(s) with replacements
        """
        replacements = self.construct_replacements(
            include, exclude, more_replacements
        )
        node = theano.clone(node, replacements, strict=False)
        posterior = self.random(no_rand=deterministic)
        return theano.clone(node, {self.input: posterior}, strict=False)

def safe_clone(cost, replace):
    params = replace.keys()
    nw_vals = replace.values()
    dummy_params = [x.type() for x in params]
    dummy_cost = theano.clone(cost,
                              replace=dict(zip(params, dummy_params)))
    return theano.clone(dummy_cost,
                        replace=dict(zip(dummy_params, nw_vals)))

 def __call__(self, z):
     if z.ndim > 1:
         a = theano.scan(
             lambda z_: theano.clone(self.op.apply(self.tf), {self.op.input: z_}, strict=False),
             sequences=z, n_steps=z.shape[0])[0].mean()
     else:
         a = theano.clone(self.op.apply(self.tf), {self.op.input: z}, strict=False)
     return tt.abs_(a)

def _elbo_t_new(logp, uw_g, uw_l, inarray_g, inarray_l, 
                n_mcsamples, random_seed):
    """Return expression of approximate ELBO based on Monte Carlo sampling.
    """
    r = MRG_RandomStreams(seed=random_seed)

    if uw_l is not None:
        l_g = (uw_g.size/2).astype('int64')
        u_g = uw_g[:l_g]
        w_g = uw_g[l_g:]
        l_l = (uw_l.size/2).astype('int64')
        u_l = uw_l[:l_l]
        w_l = uw_l[l_l:]
        logp_ = lambda z_g, z_l: theano.clone(
            logp, {inarray_g: z_g, inarray_l: z_l}, strict=False
        )

        if n_mcsamples == 1:
            n_g = r.normal(size=inarray_g.tag.test_value.shape)
            z_g = n_g * tt.exp(w_g) + u_g
            n_l = r.normal(size=inarray_l.tag.test_value.shape)
            z_l = n_l * tt.exp(w_l) + u_l
            elbo = logp_(z_g, z_l) + \
                   tt.sum(w_g) + 0.5 * l_g * (1 + np.log(2.0 * np.pi)) + \
                   tt.sum(w_l) + 0.5 * l_l * (1 + np.log(2.0 * np.pi))
        else:
            ns_g = r.normal(size=inarray_g.tag.test_value.shape)
            zs_g = ns_g * tt.exp(w_g) + u_g
            ns_l = r.normal(size=inarray_l.tag.test_value.shape)
            zs_l = ns_l * tt.exp(w_l) + u_l
            logps, _ = theano.scan(fn=lambda z_g, z_l: logp_(z_g, z_l),
                                   outputs_info=None,
                                   sequences=zip(zs_g, zs_l))
            elbo = tt.mean(logps) + \
                   tt.sum(w_g) + 0.5 * l_g * (1 + np.log(2.0 * np.pi)) + \
                   tt.sum(w_l) + 0.5 * l_l * (1 + np.log(2.0 * np.pi))
    else:
        l_g = (uw_g.size/2).astype('int64')
        u_g = uw_g[:l_g]
        w_g = uw_g[l_g:]

        logp_ = lambda z_g: theano.clone(logp, {inarray_g: z_g}, strict=False)

        if n_mcsamples == 1:
            n_g = r.normal(size=inarray_g.tag.test_value.shape)
            z_g = n_g * tt.exp(w_g) + u_g
            elbo = logp_(z_g) + \
                   tt.sum(w_g) + 0.5 * l_g * (1 + np.log(2.0 * np.pi))
        else:
            n_g = r.normal(size=(n_mcsamples, u_g.tag.test_value.shape[0]))
            zs_g = n_g * tt.exp(w_g) + u_g
            logps, _ = theano.scan(fn=lambda q: logp_(q),
                                   outputs_info=None,
                                   sequences=[zs_g])
            elbo = tt.mean(logps) + \
                   tt.sum(w_g) + 0.5 * l_g * (1 + np.log(2.0 * np.pi))

    return elbo

        def step(input, mask, cumsum_grad_att, extra_grad_h, h, h_pre, update, grad_h, C,
                 *prev_grad_params):
            """
            A single timestep of the backward pass.

            Parameters
            ----------
            input: (batch_size, n_in)
            mask: (batch_size,)
            cumsum_grad_att: (batch_size, n_hidden)
            h: (batch_size, n_hidden)
            h_pre: (batch_size, n_hidden)
            update: (batch_size, n_hidden)
            grad_h: (batch_size, n_hidden)
            C: (batch_size, n_hidden, n_hidden)
            *prev_grad_params

            Returns
            -------
            grad_input: (batch_size, n_in)
            grad_h_pre: (batch_size, n_hidden)
            C_pre: (batch_size, n_hidden, n_hidden)
            gradients with respect to the params (both of the recurrent and the
             update rule)
            """
            C_pre = self.attention_update_rule.restore_previous_matrix(C, update)

            att_grads = theano.clone(
                output=[u_grad_h] + u_grad_params,
                replace={u_h: h,
                         u_mask: mask,
                         u_C_pre: C_pre,
                         u_grad_att: cumsum_grad_att,
                         u_query: h})

            grad_h_att = att_grads[0]
            grad_params_att = att_grads[1:]

            grad_h_att *= 1000 / T.sum(seq_mask, axis=0)[:, None]
            grad_h_att = T.switch(mask[:, None], grad_h_att, .0)

            rec_grads = theano.clone(
                output=[back_grad_input, back_grad_h_pre] + back_grad_params,
                replace={back_input: input,
                         back_mask: mask,
                         back_h_pre: h_pre,
                         back_grad_h: extra_grad_h + grad_h + grad_h_att})

            grad_input = rec_grads[0]
            grad_h_pre = rec_grads[1]
            grad_params_rec = rec_grads[2:]

            grad_params = grad_params_att + grad_params_rec
            scan_outputs = [grad_input, grad_h_pre, C_pre]
            for prev_grad, grad in zip(prev_grad_params, grad_params):
                scan_outputs.append(prev_grad + grad)

            return tuple(scan_outputs)

    def get_output_for(self, input, deterministic=False, **kwargs):
        input_mean = input.mean(self.axes)
        input_var = input.var(self.axes)

        # Decide whether to use the stored averages or mini-batch statistics
        use_averages = kwargs.get('batch_norm_use_averages',
                                  deterministic)
        if use_averages:
            mean = self.mean
            var = self.var
        else:
            mean = input_mean
            var = input_var

        # Decide whether to update the stored averages
        update_averages = kwargs.get('batch_norm_update_averages',
                                     not deterministic)
        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_var = theano.clone(self.var, share_inputs=False)
            # set a default update for them:
            running_mean.default_update = ((1 - self.alpha) * running_mean +
                                           self.alpha * input_mean)
            running_var.default_update = ((1 - self.alpha) * running_var +
                                          self.alpha * input_var)
            # 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
            var += 0 * running_var

        # prepare dimshuffle pattern inserting broadcastable axes as needed
        param_axes = iter(range(self.beta.ndim))
        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 = self.beta.dimshuffle(pattern)
        gamma = self.gamma.dimshuffle(pattern)
        mean = mean.dimshuffle(pattern)
        std = T.sqrt(var + self.epsilon)
        std = std.dimshuffle(pattern)

        # normalize
        # normalized = (input - mean) * (gamma / std) + beta
        normalized = T.nnet.batch_normalization(input, gamma=gamma, beta=beta,
                                                mean=mean, std=std,
                                                mode=self.mode)
        return self.nonlinearity(normalized)

    def _apply(self, x):
        import theano

        input_shape = K.shape(x)
        is_training = K.is_training(x)
        ndim = K.ndim(x)
        self.config(input_shape=input_shape)
        # ====== training mode ====== #
        input_mean = K.mean(x, self.axes)
        input_inv_std = K.inv(K.sqrt(K.var(x, self.axes) + self.epsilon))

        # Decide whether to use the stored averages or mini-batch statistics
        if not is_training:
            mean = self.mean
            inv_std = self.inv_std
        else: # update the stored averages
            mean = input_mean
            inv_std = input_inv_std
            # 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(ndim - len(self.axes)))
        pattern = ['x' if input_axis in self.axes
                   else next(param_axes)
                   for input_axis in range(ndim)]

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

        # normalize
        normalized = (x - mean) * (gamma * inv_std) + beta
        # set shape for output
        K.add_shape(normalized, input_shape)
        return self.activation(normalized)

    def get_output_for(self, input, deterministic=False, collect=False,
                       **kwargs):

        if collect:
            # use this batch's mean and var
            if self.stat_indices is None:
                mean = input.mean(self.axes, keepdims=True)
                var = input.var(self.axes, keepdims=True)
            else:
                mean = input[self.stat_indices].mean(self.axes, keepdims=True)
                var = input[self.stat_indices].var(self.axes, keepdims=True)
            # and update the stored mean and var:
            # we create (memory-aliased) clones of the stored mean and var
            running_mean = theano.clone(self.mean, share_inputs=False)
            running_var = theano.clone(self.var, share_inputs=False)
            # set a default update for them

            if self.alpha is not 'single_pass':
                running_mean.default_update = (
                    (1 - self.alpha) * running_mean + self.alpha * mean)
                running_var.default_update = (
                    (1 - self.alpha) * running_var + self.alpha * var)
            else:
                print "Collecting using single pass..."
                # this is ugly figure out what can be safely removed...
                running_mean.default_update = (0 * running_mean + 1.0 * mean)
                running_var.default_update = (0 * running_var + 1.0 * var)

            # and include them in the graph so their default updates will be
            # applied (although the expressions will be optimized away later)
            mean += 0 * running_mean
            var += 0 * running_var

        elif deterministic:
            # use stored mean and var
            mean = self.mean
            var = self.var
        else:
            # use this batch's mean and var
            mean = input.mean(self.axes, keepdims=True)
            var = input.var(self.axes, keepdims=True)

        mean = T.addbroadcast(mean, *self.axes)
        var = T.addbroadcast(var, *self.axes)
        normalized = (input - mean) / T.sqrt(var + self.epsilon)

        if self.return_stats:
            return [normalized, mean, var]
        else:
            return normalized