Source code for pygan.discriminativemodel.nn_model

# -*- coding: utf-8 -*-
import numpy as np
from logging import getLogger

from pygan.discriminative_model import DiscriminativeModel

from pydbm.nn.neural_network import NeuralNetwork
from pydbm.nn.nn_layer import NNLayer
from pydbm.optimization.opt_params import OptParams
from pydbm.verification.interface.verificatable_result import VerificatableResult
from pydbm.loss.interface.computable_loss import ComputableLoss

from pydbm.cnn.layerablecnn.convolution_layer import ConvolutionLayer
from pydbm.synapse.nn_graph import NNGraph

# Loss function.
from pydbm.loss.mean_squared_error import MeanSquaredError
# Adam as a optimizer.
from pydbm.optimization.optparams.adam import Adam
# Verification.
from pydbm.verification.verificate_function_approximation import VerificateFunctionApproximation


[docs]class NNModel(DiscriminativeModel): ''' Neural Network as a Discriminator. ''' def __init__( self, batch_size, nn_layer_list, learning_rate=1e-05, computable_loss=None, opt_params=None, verificatable_result=None, nn=None, feature_matching_layer=0 ): ''' Init. Args: batch_size: Batch size in mini-batch. nn_layer_list: `list` of `NNLayer`. learning_rate: Learning rate. computable_loss: is-a `ComputableLoss`. This parameters will be refered only when `nn` is `None`. opt_params: is-a `OptParams`. This parameters will be refered only when `nn` is `None`. verificatable_result: is-a `VerificateFunctionApproximation`. This parameters will be refered only when `nn` is `None`. nn: is-a `NeuralNetwork` as a model in this class. If not `None`, `self.__nn` will be overrided by this `nn`. If `None`, this class initialize `NeuralNetwork` by default hyper parameters. feature_matching_layer: Key of layer number for feature matching forward/backward. ''' if nn is None: if computable_loss is None: computable_loss = MeanSquaredError() if isinstance(computable_loss, ComputableLoss) is False: raise TypeError() if verificatable_result is None: verificatable_result = VerificateFunctionApproximation() if isinstance(verificatable_result, VerificatableResult) is False: raise TypeError() if opt_params is None: opt_params = Adam() opt_params.weight_limit = 1e+10 opt_params.dropout_rate = 0.0 if isinstance(opt_params, OptParams) is False: raise TypeError() nn = NeuralNetwork( # The `list` of `ConvolutionLayer`. nn_layer_list=nn_layer_list, # The number of epochs in mini-batch training. epochs=200, # The batch size. batch_size=batch_size, # Learning rate. learning_rate=learning_rate, # Loss function. computable_loss=computable_loss, # Optimizer. opt_params=opt_params, # Verification. verificatable_result=verificatable_result, # Pre-learned parameters. pre_learned_path_list=None, # Others. learning_attenuate_rate=0.1, attenuate_epoch=50 ) self.__nn = nn self.__batch_size = batch_size self.__learning_rate = learning_rate self.__q_shape = None self.__loss_list = [] self.__feature_matching_layer = feature_matching_layer self.__epoch_counter = 0 logger = getLogger("pygan") self.__logger = logger
[docs] def inference(self, observed_arr): ''' Draws samples from the `true` distribution. Args: observed_arr: `np.ndarray` of observed data points. Returns: `np.ndarray` of inferenced. ''' if observed_arr.ndim != 2: observed_arr = observed_arr.reshape((observed_arr.shape[0], -1)) return self.__nn.inference(observed_arr)
[docs] def learn(self, grad_arr, fix_opt_flag=False): ''' Update this Discriminator by ascending its stochastic gradient. Args: grad_arr: `np.ndarray` of gradients. fix_opt_flag: If `False`, no optimization in this model will be done. Returns: `np.ndarray` of delta or gradients. ''' if grad_arr.ndim != 2: grad_arr = grad_arr.reshape((grad_arr.shape[0], -1)) delta_arr = self.__nn.back_propagation(grad_arr) if fix_opt_flag is False: self.__nn.optimize(self.__learning_rate, self.__epoch_counter) self.__epoch_counter += 1 return delta_arr
[docs] def feature_matching_forward(self, observed_arr): ''' Forward propagation in only first or intermediate layer for so-called Feature matching. Args: observed_arr: `np.ndarray` of observed data points. Returns: `np.ndarray` of outputs. ''' if observed_arr.ndim != 2: observed_arr = observed_arr.reshape((observed_arr.shape[0], -1)) if self.__feature_matching_layer == 0: return self.__nn.nn_layer_list[0].forward_propagate(observed_arr) else: for i in range(self.__feature_matching_layer): observed_arr = self.__nn.nn_layer_list[i].forward_propagate(observed_arr) return observed_arr
[docs] def feature_matching_backward(self, grad_arr): ''' Back propagation in only first or intermediate layer for so-called Feature matching. Args: observed_arr: `np.ndarray` of observed data points. Returns: `np.ndarray` of outputs. ''' if grad_arr.ndim != 2: grad_arr = grad_arr.reshape((grad_arr.shape[0], -1)) if self.__feature_matching_layer == 0: return np.dot(grad_arr, self.__nn.nn_layer_list[0].graph.weight_arr.T) else: nn_layer_list = self.__nn.nn_layer_list[:self.__feature_matching_layer][::-1] for i in range(len(nn_layer_list)): grad_arr = np.dot(grad_arr, nn_layer_list[i].graph.weight_arr.T) return grad_arr
[docs] def get_nn(self): ''' getter ''' return self.__nn
[docs] def set_nn(self, value): ''' setter ''' raise TypeError("This property must be read-only.")
nn = property(get_nn, set_nn)