Source code for sconce.models.multilayer_perceptron

from .layers import FullyConnectedLayer
from sconce.models.base import Model
from torch import nn
from torch.nn import functional as F

import numpy as np
import yaml


[docs]class MultilayerPerceptron(Model): """ A basic 2D image multi-layer perceptron built up of a number of densly connected layers. Loss: This model uses cross-entropy for the loss. Metrics: classification_accuracy: [0.0, 1.0] the fraction of correctly predicted labels. Arguments: image_height (int): image height in pixels. image_width (int): image width in pixels. image_channels (int): number of channels in the input images. layer_kwargs (list[dict]): a list of dictionaries describing layers. See :py:class:`~sconce.models.layers.fully_connected_layer.FullyConnectedLayer` for details. num_categories (int): [2, inf) the number of different image classes. """ def __init__(self, image_height, image_width, image_channels, layer_kwargs, num_categories=10): super().__init__() in_channels = image_channels h = image_height w = image_width fc_layers = [] num_channels = in_channels fc_size = w * h * num_channels for kwargs in layer_kwargs: layer = FullyConnectedLayer(in_size=fc_size, activation=nn.ReLU(), **kwargs) fc_layers.append(layer) fc_size = kwargs['out_size'] self.non_final_layers = nn.ModuleList(fc_layers) self.final_layer = FullyConnectedLayer(fc_size, num_categories, with_batchnorm=False, activation=nn.LogSoftmax(dim=-1))
[docs] @classmethod def new_from_yaml_filename(cls, yaml_filename): """ Construct a new MultilayerPerceptron from a yaml file. Arguments: filename (path): the filename of a yaml file. See :py:meth:`~sconce.models.basic_classifier.BasicClassifier.new_from_yaml_file` for more details. """ with open(yaml_filename) as yaml_file: return cls.new_from_yaml_file(yaml_file)
[docs] @classmethod def new_from_yaml_file(cls, yaml_file): """ Construct a new MultilayerPerceptron from a yaml file. Arguments: yaml_file (file): a file-like object that yaml contents can be read from. Example yaml file contents: .. code-block:: yaml --- # Values for MNIST and FashionMNIST image_height: 28 image_width: 28 image_channels: 1 num_categories: 10 layer_attributes: ['out_size', 'dropout', 'with_batchnorm'] layer_values: [ # ====== ========= ================ [100, 0.4, true], [100, 0.8, true], ] """ yaml_data = yaml.load(yaml_file) layer_kwargs = [] keys = yaml_data.pop('layer_attributes') for values in yaml_data.pop('layer_values'): kwargs = dict(zip(keys, values)) layer_kwargs.append(kwargs) return cls(layer_kwargs=layer_kwargs, **yaml_data)
[docs] def forward(self, inputs, **kwargs): x = inputs.view(inputs.size()[0], -1) for layer in self.non_final_layers: x = layer(x) outputs = self.final_layer(x) return {'outputs': outputs}
[docs] def calculate_loss(self, targets, outputs, **kwargs): return {'loss': F.nll_loss(input=outputs, target=targets)}
[docs] def calculate_metrics(self, targets, outputs, **kwargs): y_out = np.argmax(outputs.cpu().data.numpy(), axis=1) y_in = targets.cpu().data.numpy() num_correct = (y_out - y_in == 0).sum() classification_accuracy = num_correct / len(y_in) return {'classification_accuracy': classification_accuracy}