Source code for pygan.discriminativemodel.cnn_model

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

from pygan.discriminative_model import DiscriminativeModel

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.convolutional_neural_network import ConvolutionalNeuralNetwork
from pydbm.cnn.layerable_cnn import LayerableCNN
from pydbm.synapse.cnn_graph import CNNGraph as ConvGraph1
from pydbm.synapse.cnn_graph import CNNGraph as ConvGraph2
from pydbm.activation.relu_function import ReLuFunction
from pydbm.activation.tanh_function import TanhFunction
from pydbm.activation.logistic_function import LogisticFunction
from pydbm.loss.mean_squared_error import MeanSquaredError
from pydbm.optimization.optparams.adam import Adam
from pydbm.optimization.optparams.sgd import SGD
from pydbm.verification.verificate_function_approximation import VerificateFunctionApproximation


[docs]class CNNModel(DiscriminativeModel): ''' Convolutional Neural Network as a Discriminator. ''' def __init__( self, batch_size, layerable_cnn_list, cnn_output_graph, learning_rate=1e-05, computable_loss=None, opt_params=None, verificatable_result=None, cnn=None, feature_matching_layer=0 ): ''' Init. Args: batch_size: Batch size in mini-batch. layerable_cnn_list: `list` of `LayerableCNN`. cnn_output_graph: is-a `CNNOutputGraph`. learning_rate: Learning rate. computable_loss: is-a `ComputableLoss`. This parameters will be refered only when `cnn` is `None`. opt_params: is-a `OptParams`. This parameters will be refered only when `cnn` is `None`. verificatable_result: is-a `VerificateFunctionApproximation`. This parameters will be refered only when `cnn` is `None`. cnn: is-a `ConvolutionalNeuralNetwork` as a model in this class. If not `None`, `self.__cnn` will be overrided by this `cnn`. If `None`, this class initialize `ConvolutionalNeuralNetwork` by default hyper parameters. feature_matching_layer: Key of layer number for feature matching forward/backward. ''' for layerable_cnn in layerable_cnn_list: if isinstance(layerable_cnn, LayerableCNN) is False: raise TypeError() self.__layerable_cnn_list = layerable_cnn_list self.__learning_rate = learning_rate self.__opt_params = opt_params if cnn 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() cnn = ConvolutionalNeuralNetwork( layerable_cnn_list=layerable_cnn_list, computable_loss=computable_loss, opt_params=opt_params, verificatable_result=verificatable_result, epochs=100, batch_size=batch_size, learning_rate=learning_rate, learning_attenuate_rate=0.1, test_size_rate=0.3, tol=1e-15, tld=100.0, save_flag=False, pre_learned_path_list=None ) cnn.setup_output_layer(cnn_output_graph) self.__cnn = cnn self.__batch_size = batch_size self.__computable_loss = computable_loss 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. ''' return self.__cnn.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.__cnn.back_propagation(grad_arr) if fix_opt_flag is False: self.__cnn.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 self.__feature_matching_layer == 0: return self.__cnn.layerable_cnn_list[0].forward_propagate(observed_arr) else: for i in range(self.__feature_matching_layer): observed_arr = self.__cnn.layerable_cnn_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 self.__feature_matching_layer == 0: return self.__cnn.layerable_cnn_list[0].deconvolve(grad_arr) else: cnn_layer_list = self.__cnn.layerable_cnn_list[:self.__feature_matching_layer][::-1] for i in range(len(cnn_layer_list)): grad_arr = cnn_layer_list[i].deconvolve(grad_arr) return grad_arr
[docs] def get_cnn(self): ''' getter ''' return self.__cnn
[docs] def set_cnn(self, value): ''' setter ''' raise TypeError("This property must be read-only.")
cnn = property(get_cnn, set_cnn)