Python blocks.graph.apply_dropout() Examples

The following are 2 code examples of blocks.graph.apply_dropout(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module blocks.graph , or try the search function .
Example #1
Source File: pacgan_task.py    From PacGAN with MIT License 4 votes vote down vote up 
def create_models(self):
        gan = self.create_model_brick()
        x = tensor.matrix('features')
        zs = []
        for i in range(self._config["num_packing"]):
            z = circle_gaussian_mixture(num_modes=self._config["num_zmode"], num_samples=x.shape[0], dimension=self._config["num_zdim"], r=self._config["z_mode_r"], std=self._config["z_mode_std"])
            zs.append(z)

        def _create_model(with_dropout):
            cg = ComputationGraph(gan.compute_losses(x, zs))
            if with_dropout:
                inputs = VariableFilter(
                    bricks=gan.discriminator.children[1:],
                    roles=[INPUT])(cg.variables)
                cg = apply_dropout(cg, inputs, 0.5)
                inputs = VariableFilter(
                    bricks=[gan.discriminator],
                    roles=[INPUT])(cg.variables)
                cg = apply_dropout(cg, inputs, 0.2)
            return Model(cg.outputs)

        model = _create_model(with_dropout=False)
        with batch_normalization(gan):
            bn_model = _create_model(with_dropout=False)

        pop_updates = list(set(get_batch_normalization_updates(bn_model, allow_duplicates=True)))
            
        # merge same variables
        names = []
        counts = []
        pop_update_merges = []
        pop_update_merges_finals = []
        for pop_update in pop_updates:
            b = False
            for i in range(len(names)):
                if (pop_update[0].auto_name == names[i]):
                    counts[i] += 1
                    pop_update_merges[i][1] += pop_update[1]
                    b = True
                    break
            if not b:
                names.append(pop_update[0].auto_name)
                counts.append(1)
                pop_update_merges.append([pop_update[0], pop_update[1]])
        for i in range(len(pop_update_merges)):
            pop_update_merges_finals.append((pop_update_merges[i][0], pop_update_merges[i][1] / counts[i]))
        
        bn_updates = [(p, m * 0.05 + p * 0.95) for p, m in pop_update_merges_finals]

        return model, bn_model, bn_updates
Example #2
Source File: pacgan_task.py    From PacGAN with MIT License 4 votes vote down vote up 
def create_models(self):
        gan = self.create_model_brick()
        x = tensor.matrix('features')
        zs = []
        for i in range(self._config["num_packing"]):
            z = circle_gaussian_mixture(num_modes=self._config["num_zmode"], num_samples=x.shape[0], dimension=self._config["num_zdim"], r=self._config["z_mode_r"], std=self._config["z_mode_std"])
            zs.append(z)

        def _create_model(with_dropout):
            cg = ComputationGraph(gan.compute_losses(x, zs))
            if with_dropout:
                inputs = VariableFilter(
                    bricks=gan.discriminator.children[1:],
                    roles=[INPUT])(cg.variables)
                cg = apply_dropout(cg, inputs, 0.5)
                inputs = VariableFilter(
                    bricks=[gan.discriminator],
                    roles=[INPUT])(cg.variables)
                cg = apply_dropout(cg, inputs, 0.2)
            return Model(cg.outputs)

        model = _create_model(with_dropout=False)
        with batch_normalization(gan):
            bn_model = _create_model(with_dropout=False)

        pop_updates = list(set(get_batch_normalization_updates(bn_model, allow_duplicates=True)))
            
        # merge same variables
        names = []
        counts = []
        pop_update_merges = []
        pop_update_merges_finals = []
        for pop_update in pop_updates:
            b = False
            for i in range(len(names)):
                if (pop_update[0].auto_name == names[i]):
                    counts[i] += 1
                    pop_update_merges[i][1] += pop_update[1]
                    b = True
                    break
            if not b:
                names.append(pop_update[0].auto_name)
                counts.append(1)
                pop_update_merges.append([pop_update[0], pop_update[1]])
        for i in range(len(pop_update_merges)):
            pop_update_merges_finals.append((pop_update_merges[i][0], pop_update_merges[i][1] / counts[i]))
        
        bn_updates = [(p, m * 0.05 + p * 0.95) for p, m in pop_update_merges_finals]

        return model, bn_model, bn_updates