Source code for pygan.generativemodel.deconvolution_model

# -*- coding: utf-8 -*-
import numpy as np
from logging import getLogger
from pygan.generative_model import GenerativeModel
from pygan.true_sampler import TrueSampler

from pydbm.cnn.convolutionalneuralnetwork.convolutional_auto_encoder import ConvolutionalAutoEncoder as CAE
from pydbm.synapse.cnn_graph import CNNGraph as ConvGraph1
from pydbm.synapse.cnn_graph import CNNGraph as ConvGraph2

from pydbm.cnn.layerablecnn.convolutionlayer.deconvolution_layer import DeconvolutionLayer
from pydbm.synapse.cnn_graph import CNNGraph as DeCNNGraph

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.opt_params import OptParams
from pydbm.verification.verificate_function_approximation import VerificateFunctionApproximation
from pydbm.synapse.cnn_output_graph import CNNOutputGraph


[docs]class DeconvolutionModel(GenerativeModel): ''' So-called Deconvolutional Neural Network as a `GenerativeModel`. Deconvolution also called transposed convolutions "work by swapping the forward and backward passes of a convolution." (Dumoulin, V., & Visin, F. 2016, p20.) References: - Dumoulin, V., & V,kisin, F. (2016). A guide to convolution arithmetic for deep learning. arXiv preprint arXiv:1603.07285. ''' # Computation graph which is-a `CNNOutputGraph` to compute parameters in output layer. __cnn_output_graph = None def __init__( self, deconvolution_layer_list, computable_loss=None, cnn_output_graph=None, opt_params=None, learning_rate=1e-05 ): ''' Init. Args: deconvolution_layer_list: `list` of `DeconvolutionLayer`. computable_loss: Loss function. cnn_output_graph: is-a `CNNOutputGraph`. opt_params: is-a `OptParams`. If `None`, this value will be `Adam`. learning_rate: Learning rate. ''' for deconvolution_layer in deconvolution_layer_list: if isinstance(deconvolution_layer, DeconvolutionLayer) is False: raise TypeError() if cnn_output_graph is not None and isinstance(cnn_output_graph, CNNOutputGraph) is False: raise TypeError("The type of `cnn_output_graph` must be `CNNOutputGraph`.") 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() self.__deconvolution_layer_list = deconvolution_layer_list self.__computable_loss = computable_loss self.__cnn_output_graph = cnn_output_graph self.__learning_rate = learning_rate self.__attenuate_epoch = 50 self.__opt_params = opt_params self.__epoch_counter = 0 logger = getLogger("pygan") self.__logger = logger
[docs] def draw(self): ''' Draws samples from the `fake` distribution. Returns: `np.ndarray` of samples. ''' observed_arr = self.noise_sampler.generate() return self.inference(observed_arr)
[docs] def inference(self, observed_arr): ''' Draws samples from the `fake` distribution. Args: observed_arr: `np.ndarray` of observed data points. Returns: `np.ndarray` of inferenced. ''' for i in range(len(self.__deconvolution_layer_list)): try: observed_arr = self.__deconvolution_layer_list[i].forward_propagate(observed_arr) except: self.__logger.debug("Error raised in Deconvolution layer " + str(i + 1)) raise if self.__cnn_output_graph is not None: return self.output_forward_propagate(observed_arr) else: return observed_arr
[docs] def output_forward_propagate(self, pred_arr): ''' Forward propagation in output layer. Args: pred_arr: `np.ndarray` of predicted data points. Returns: `np.ndarray` of propagated data points. ''' if self.__cnn_output_graph is not None: _pred_arr = self.__cnn_output_graph.activating_function.activate( np.dot(pred_arr.reshape((pred_arr.shape[0], -1)), self.__cnn_output_graph.weight_arr) + self.__cnn_output_graph.bias_arr ) self.__cnn_output_graph.hidden_arr = pred_arr self.__cnn_output_graph.output_arr = _pred_arr return _pred_arr else: return pred_arr
[docs] def learn(self, grad_arr): ''' Update this Generator by ascending its stochastic gradient. Args: grad_arr: `np.ndarray` of gradients. Returns: `np.ndarray` of delta or gradients. ''' if self.__cnn_output_graph is not None: if grad_arr.ndim != 2: grad_arr = grad_arr.reshape((grad_arr.shape[0], -1)) grad_arr, output_grads_list = self.output_back_propagate( self.__cnn_output_graph.output_arr, grad_arr ) grad_arr = grad_arr.reshape(( self.__cnn_output_graph.hidden_arr.shape[0], self.__cnn_output_graph.hidden_arr.shape[1], self.__cnn_output_graph.hidden_arr.shape[2], self.__cnn_output_graph.hidden_arr.shape[3] )) self.__cnn_output_graph.output_grads_list = output_grads_list deconvolution_layer_list = self.__deconvolution_layer_list[::-1] for i in range(len(deconvolution_layer_list)): try: grad_arr = deconvolution_layer_list[i].back_propagate(grad_arr) except: self.__logger.debug("Error raised in Convolution layer " + str(i + 1)) raise self.__optimize(self.__learning_rate, self.__epoch_counter) self.__epoch_counter += 1 return grad_arr
[docs] def output_back_propagate(self, pred_arr, delta_arr): ''' Back propagation in output layer. Args: pred_arr: `np.ndarray` of predicted data points. delta_output_arr: Delta. Returns: Tuple data. - `np.ndarray` of Delta, - `list` of gradations. ''' _delta_arr = np.dot( delta_arr, self.__cnn_output_graph.weight_arr.T ) delta_weights_arr = np.dot(pred_arr.T, _delta_arr).T delta_bias_arr = np.sum(delta_arr, axis=0) grads_list = [ delta_weights_arr, delta_bias_arr ] return (_delta_arr, grads_list)
def __optimize(self, learning_rate, epoch): ''' Back propagation. Args: learning_rate: Learning rate. epoch: Now epoch. ''' params_list = [] grads_list = [] if self.__cnn_output_graph is not None: params_list.append(self.__cnn_output_graph.weight_arr) params_list.append(self.__cnn_output_graph.bias_arr) grads_list.append(self.__cnn_output_graph.output_grads_list[0]) grads_list.append(self.__cnn_output_graph.output_grads_list[1]) for i in range(len(self.__deconvolution_layer_list)): if self.__deconvolution_layer_list[i].delta_weight_arr.shape[0] > 0: params_list.append(self.__deconvolution_layer_list[i].graph.weight_arr) grads_list.append(self.__deconvolution_layer_list[i].delta_weight_arr) for i in range(len(self.__deconvolution_layer_list)): if self.__deconvolution_layer_list[i].delta_bias_arr.shape[0] > 0: params_list.append(self.__deconvolution_layer_list[i].graph.bias_arr) grads_list.append(self.__deconvolution_layer_list[i].delta_bias_arr) params_list = self.__opt_params.optimize( params_list, grads_list, learning_rate ) if self.__cnn_output_graph is not None: self.__cnn_output_graph.weight_arr = params_list.pop(0) self.__cnn_output_graph.bias_arr = params_list.pop(0) i = 0 for i in range(len(self.__deconvolution_layer_list)): if self.__deconvolution_layer_list[i].delta_weight_arr.shape[0] > 0: self.__deconvolution_layer_list[i].graph.weight_arr = params_list.pop(0) if ((epoch + 1) % self.__attenuate_epoch == 0): self.__deconvolution_layer_list[i].graph.weight_arr = self.__opt_params.constrain_weight( self.__deconvolution_layer_list[i].graph.weight_arr ) for i in range(len(self.__deconvolution_layer_list)): if self.__deconvolution_layer_list[i].delta_bias_arr.shape[0] > 0: self.__deconvolution_layer_list[i].graph.bias_arr = params_list.pop(0) for i in range(len(self.__deconvolution_layer_list)): if self.__deconvolution_layer_list[i].delta_weight_arr.shape[0] > 0: if self.__deconvolution_layer_list[i].delta_bias_arr.shape[0] > 0: self.__deconvolution_layer_list[i].reset_delta()
[docs] def switch_inferencing_mode(self, inferencing_mode=True): ''' Set inferencing mode in relation to concrete regularizations. Args: inferencing_mode: Inferencing mode or not. ''' self.__opt_params.inferencing_mode = inferencing_mode
[docs] def get_deconvolution_layer_list(self): ''' getter ''' return self.__deconvolution_layer_list
[docs] def set_deconvolution_layer_list(self, value): ''' setter ''' raise TypeError("This property must be read-only.")
deconvolution_layer_list = property(get_deconvolution_layer_list, set_deconvolution_layer_list)