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ATL_Python - first commit
2019-12-04 14:18:56 +08:00
# Marcus Vinicius Sousa Leite de Carvalho
# marcus.decarvalho@ntu.edu.sg
# ivsucram@gmail.com
#
# NANYANG TECHNOLOGICAL UNIVERSITY - NTUITIVE PTE LTD Dual License Agreement
# Non-Commercial Use Only
# This NTUITIVE License Agreement, including all exhibits ("NTUITIVE-LA") is a legal agreement between you and NTUITIVE (or “we”) located at 71 Nanyang Drive, NTU Innovation Centre, #01-109, Singapore 637722, a wholly owned subsidiary of Nanyang Technological University (“NTU”) for the software or data identified above, which may include source code, and any associated materials, text or speech files, associated media and "online" or electronic documentation and any updates we provide in our discretion (together, the "Software").
#
# By installing, copying, or otherwise using this Software, found at https://github.com/Ivsucram/ATL_Matlab, you agree to be bound by the terms of this NTUITIVE-LA. If you do not agree, do not install copy or use the Software. The Software is protected by copyright and other intellectual property laws and is licensed, not sold. If you wish to obtain a commercial royalty bearing license to this software please contact us at marcus.decarvalho@ntu.edu.sg.
#
# SCOPE OF RIGHTS:
# You may use, copy, reproduce, and distribute this Software for any non-commercial purpose, subject to the restrictions in this NTUITIVE-LA. Some purposes which can be non-commercial are teaching, academic research, public demonstrations and personal experimentation. You may also distribute this Software with books or other teaching materials, or publish the Software on websites, that are intended to teach the use of the Software for academic or other non-commercial purposes.
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#
# Copyright (c) NTUITIVE. All rights reserved.
from MyUtil import MyUtil as MyUtil
from ElasticNodes import ElasticNodes
from MySingletons import MyDevice
import AGMM
import numpy as np
import torch
class NeuralNetwork(ElasticNodes):
layers = None
layer_value = None
output_layer_value = None
weight = None
bias = None
momentum = None
bias_momentum = None
output_weight = None
output_bias = None
output_momentum = None
output_bias_momentum = None
activation_function = None
output_activation_function = None
loss_function = None
learning_rate = 0.01
momentum_rate = 0.95
###
error_value = None
loss_value = None
classification_rate = None
###
###
output_beta = None
output_beta_decreasing_factor = None
###
__Eh = None
__Eh2 = None
###
###
is_agmm_able = False
agmm = None
###
@property
def number_hidden_layers(self):
return len(self.layers) - 2
@property
def input_size(self):
return self.layers[0]
@property
def output_size(self):
return self.layers[-1]
@property
def output(self):
return self.output_layer_value
@property
def raw_output(self):
return torch.max(self.output, axis=1)
@property
def outputed_classes(self):
return torch.argmax(self.output, axis=1)
@property
def residual_error(self):
return 1 - self.raw_output.values
ACTIVATION_FUNCTION_AFFINE = 1
ACTIVATION_FUNCTION_SIGMOID = ACTIVATION_FUNCTION_AFFINE + 1
ACTIVATION_FUNCTION_TANH = ACTIVATION_FUNCTION_SIGMOID + 1
ACTIVATION_FUNCTION_RELU = ACTIVATION_FUNCTION_TANH + 1
ACTIVATION_FUNCTION_LINEAR = ACTIVATION_FUNCTION_RELU + 1
ACTIVATION_FUNCTION_SOFTMAX = ACTIVATION_FUNCTION_LINEAR + 1
LOSS_FUNCTION_MSE = ACTIVATION_FUNCTION_SOFTMAX + 1
LOSS_FUNCTION_CROSS_ENTROPY = LOSS_FUNCTION_MSE + 1
PRUNE_NODE_STRATEGY_SINGLE = LOSS_FUNCTION_CROSS_ENTROPY + 1
PRUNE_NODE_STRATEGY_MULTIPLE = PRUNE_NODE_STRATEGY_SINGLE + 1
def __init__(self, layers: list):
self.layers = layers
self.weight = []
self.bias = []
self.momentum = []
self.bias_momentum = []
self.activation_function = []
for i in range(self.number_hidden_layers):
nodes_before = layers[i]
nodes_after = layers[i + 1]
self.weight.append(self.xavier_weight_initialization(nodes_after, nodes_before))
self.bias.append(self.xavier_weight_initialization(1, nodes_after))
self.momentum.append(torch.zeros(self.weight[i].shape, dtype=torch.float, device=MyDevice().get()))
self.bias_momentum.append(torch.zeros(self.bias[i].shape, dtype=torch.float, device=MyDevice().get()))
self.activation_function.append(self.ACTIVATION_FUNCTION_SIGMOID)
nodes_before = layers[-2]
nodes_after = layers[-1]
self.output_weight = self.xavier_weight_initialization(nodes_after, nodes_before)
self.output_bias = self.xavier_weight_initialization(1, nodes_after)
self.output_momentum = torch.zeros(self.output_weight.shape, dtype=torch.float, device=MyDevice().get())
self.output_bias_momentum = torch.zeros(self.output_bias.shape, dtype=torch.float, device=MyDevice().get())
self.output_activation_function = self.ACTIVATION_FUNCTION_SOFTMAX
self.loss_function = self.LOSS_FUNCTION_CROSS_ENTROPY
ElasticNodes.__init__(self, len(self.layers))
##### Weight initializations #####
def xavier_weight_initialization(self, n_out: int, n_in: int, uniform: bool = False):
if uniform:
return torch.nn.init.xavier_uniform(tensor=torch.zeros(int(n_out), int(n_in), dtype=torch.float, requires_grad=True, device=MyDevice().get()))
return torch.nn.init.xavier_normal_(tensor=torch.zeros(int(n_out), int(n_in), dtype=torch.float, requires_grad=True, device=MyDevice().get()))
def he_weight_initialization(self, n_out, n_in, shape=None):
#TODO
mean = 0.0
sigma = np.sqrt(2 / n_in)
if shape is None:
shape = (n_out, n_in)
return np.random.normal(mean, sigma, shape)
##### Noise #####
def masking_noise(self, x: torch.tensor, noise_ratio=0.0):
return x.clone().masked_fill(torch.rand(x.shape, device=MyDevice().get()) <= noise_ratio, 0)
##### Activation functions #####
@staticmethod
def sigmoid(z: torch.tensor):
return torch.sigmoid(z)
@staticmethod
def tanh(z):
return torch.tanh(z)
@staticmethod
def relu(z):
return torch.nn.functional.relu(z)
@staticmethod
def linear(layer_value: torch.tensor, weight: torch.tensor, bias: torch.tensor):
return torch.nn.functional.linear(layer_value, weight, bias)
@staticmethod
def softmax(z, axis: int = 1):
return torch.nn.functional.softmax(z, dim=axis)
def reset_grad(self):
for i in range(self.number_hidden_layers):
self.weight[i] = self.weight[i].detach()
self.bias[i] = self.bias[i].detach()
self.weight[i].requires_grad = True
self.bias[i].requires_grad = True
self.output_weight = self.output_weight.detach()
self.output_bias = self.output_bias.detach()
self.output_weight.requires_grad = True
self.output_bias.requires_grad = True
def feedforward(self, x: torch.Tensor, y: torch.Tensor, train: bool = False):
return self.forward_pass(x, train=train).calculate_error(y)
def backpropagate(self):
self.loss_value.backward()
return self
def test(self, x: torch.Tensor, y: torch.Tensor, is_beta_updatable: bool = False):
self.feedforward(x=x, y=y)
m = y.shape[0]
true_classes = torch.argmax(y, axis=1)
misclassified = torch.sum(torch.ne(self.outputed_classes, true_classes)).item()
self.classification_rate = 1 - misclassified / m
if is_beta_updatable:
class_label = self.output_layer_value.max(axis=2)
for i in range(m):
if self.true_classes[i] == class_label[i]:
self.output_beta = np.max(self.output_beta * self.output_beta_decreasing_factor, 0)
self.output_beta_decreasing_factor = np.max(self.output_beta_decreasing_factor - 0.01, 0)
else:
self.output_beta = max(self.output_beta * (1 + self.output_beta_decreasing_factor), 1)
self.output_beta_decreasing_factor = max(self.output_beta_decreasing_factor + 0.01, 1)
def train(self, x: torch.Tensor, y: torch.Tensor, weight_no: int = None):
self.feedforward(x=x, y=y, train=True).backpropagate()
if weight_no is None:
for i in range(self.number_hidden_layers, -1, -1):
self.update_weight(i)
else:
self.update_weight(weight_no)
def update_weight(self, weight_no: int):
if weight_no >= self.number_hidden_layers:
dW: torch.Tensor = self.learning_rate * self.output_weight.grad
db: torch.Tensor = self.learning_rate * self.output_bias.grad
if self.momentum_rate > 0:
self.output_momentum: torch.Tensor = self.momentum_rate * self.output_momentum + dW
self.output_bias_momentum: torch.Tensor = self.momentum_rate * self.output_bias_momentum + db
dW: torch.Tensor = self.output_momentum
db: torch.Tensor = self.output_bias_momentum
self.output_weight: torch.Tensor = self.output_weight - dW
self.output_bias: torch.Tensor = self.output_bias - db
else:
dW: torch.Tensor = self.learning_rate * self.weight[weight_no].grad
db: torch.Tensor = self.learning_rate * self.bias[weight_no].grad
if self.momentum_rate > 0:
self.momentum[weight_no]: torch.Tensor = self.momentum_rate * self.momentum[weight_no] + dW
self.bias_momentum[weight_no]: torch.Tensor = self.momentum_rate * self.bias_momentum[weight_no] + db
dW: torch.Tensor = self.momentum[weight_no]
db: torch.Tensor = self.bias_momentum[weight_no]
self.weight[weight_no]: torch.Tensor = self.weight[weight_no] - dW
self.bias[weight_no]: torch.Tensor = self.bias[weight_no] - db
def forward_pass(self, x: torch.Tensor, train: bool = False):
if train:
self.reset_grad()
self.layer_value = []
self.layer_value.append(x)
for i in range(self.number_hidden_layers):
if self.activation_function[i] == self.ACTIVATION_FUNCTION_AFFINE:
self.layer_value.append(self.linear(self.layer_value[i], self.weight[i], self.bias[i]))
elif self.activation_function[i] == self.ACTIVATION_FUNCTION_SIGMOID:
self.layer_value.append(self.sigmoid(self.linear(self.layer_value[i], self.weight[i], self.bias[i])))
elif self.activation_function[i] == self.ACTIVATION_FUNCTION_TANH:
self.layer_value.append(self.tanh(self.linear(self.layer_value[i], self.weight[i], self.bias[i])))
elif self.activation_function[i] == self.ACTIVATION_FUNCTION_RELU:
self.layer_value.append(self.relu(self.linear(self.layer_value[i], self.weight[i], self.bias[i])))
elif self.activation_function[i] == self.ACTIVATION_FUNCTION_LINEAR:
raise TypeError('Not implemented')
elif self.activation_function[i] == self.ACTIVATION_FUNCTION_SOFTMAX:
self.layer_value.append(self.softmax(self.linear(self.layer_value[i], self.weight[i], self.bias[i])))
raise TypeError('Not implemented')
if self.output_activation_function == self.ACTIVATION_FUNCTION_AFFINE:
self.output_layer_value = self.linear(self.layer_value[-1], self.output_weight, self.output_bias)
elif self.output_activation_function == self.ACTIVATION_FUNCTION_SIGMOID:
self.output_layer_value = self.sigmoid(self.linear(self.layer_value[-1], self.output_weight, self.output_bias))
elif self.output_activation_function == self.ACTIVATION_FUNCTION_TANH:
self.output_layer_value = self.tanh(self.linear(self.layer_value[-1], self.output_weight, self.output_bias))
elif self.output_activation_function == self.ACTIVATION_FUNCTION_RELU:
self.output_layer_value = self.relu(self.linear(self.layer_value[-1], self.output_weight, self.output_bias))
elif self.output_activation_function == self.ACTIVATION_FUNCTION_SOFTMAX:
self.output_layer_value = self.softmax(self.linear(self.layer_value[-1], self.output_weight, self.output_bias), axis=1)
return self
def calculate_error(self, y: torch.tensor):
self.error_value = y - self.output_layer_value
if self.loss_function == self.LOSS_FUNCTION_MSE:
self.loss_value = torch.nn.functional.mse_loss(self.output_layer_value, y)
elif self.loss_function == self.LOSS_FUNCTION_CROSS_ENTROPY:
self.loss_value = torch.nn.functional.cross_entropy(self.output_layer_value, torch.argmax(y, 1))
return self
def compute_expected_values(self, in_place: bool = False):
if in_place:
data_mean, data_var, data_std = self.data_mean, self.data_variance, self.data_standard_deviation
self.number_samples_feed += 1
self.data_mean, self.data_variance, self.data_standard_deviation = \
MyUtil.recursive_mean_standard_deviation(self.layer_value[0],
self.data_mean,
self.data_variance,
self.number_samples_feed)
if self.is_agmm_able and self.agmm.M() > 0:
self.Eh, self.Eh2 = 0, 0
for gmm in self.agmm.gmm_array:
tempEh, tempEh2 = self.compute_inbound_expected_values(gmm=gmm)
self.Eh, self.Eh2 = self.Eh + tempEh, self.Eh2 + tempEh2
else:
self.Eh, self.Eh2 = self.compute_inbound_expected_values()
if in_place:
self.Eh, self.Eh2 = self.compute_inbound_expected_values()
self.data_mean, self.data_variance, self.data_standard_deviation = data_mean, data_var, data_std
self.number_samples_feed -= 1
def compute_inbound_expected_values(self, number_hidden_layer: int = None, gmm: AGMM.GMM = None):
nhl = number_hidden_layer # readability
if nhl is None:
nhl = self.number_hidden_layers - 1
if nhl == 0:
inference, center, std = (1, self.data_mean, self.data_standard_deviation) if gmm is None else (gmm.weight, gmm.center, gmm.std)
py = MyUtil.probit(center, std)
Eh = inference * self.sigmoid(self.linear(self.weight[0], py, self.bias[0].T))
else:
Eh, _ = self.compute_inbound_expected_values(number_hidden_layer=nhl - 1, gmm=gmm)
weight, bias = (self.weight[nhl], self.bias[nhl]) if nhl < self.number_hidden_layers + 1 else (self.output_weight, self.output_bias)
Eh = self.sigmoid(self.linear(weight, Eh.T, bias.T))
return Eh, Eh ** 2
@property
def Eh(self):
return self.__Eh
@Eh.setter
def Eh(self, value: torch.tensor):
self.__Eh = value
@property
def Eh2(self):
return self.__Eh2
@Eh2.setter
def Eh2(self, value: torch.tensor):
self.__Eh2 = value
@property
def Ey(self):
return self.softmax(self.linear(self.output_weight, self.Eh.T, self.output_bias.T), axis=0)
@property
def Ey2(self):
return self.softmax(self.linear(self.output_weight, self.Eh2.T, self.output_bias.T), axis=0)
@property
def network_variance(self):
return MyUtil.frobenius_norm(self.Ey2 - self.Ey ** 2)
def compute_bias(self, y):
return MyUtil.frobenius_norm((self.Ey.T - y) ** 2)
def set_agmm(self, agmm: AGMM.GMM):
self.is_agmm_able = True
self.agmm = agmm
def get_agmm(self):
return self.agmm
def run_agmm(self, x: torch.tensor, y: torch.tensor):
self.compute_expected_values(in_place=True)
self.agmm.run(x, self.compute_bias(y))
return self.agmm
def width_adaptation_stepwise(self, y, prune_strategy: int = None):
if prune_strategy is None:
prune_strategy = self.PRUNE_NODE_STRATEGY_MULTIPLE
nhl: int = self.number_hidden_layers
self.number_samples_feed = self.number_samples_feed + 1
self.number_samples_layer[nhl] = self.number_samples_layer[nhl] + 1
self.compute_expected_values()
self.bias_mean[nhl], self.bias_variance[nhl], self.bias_standard_deviation[nhl] = \
MyUtil.recursive_mean_standard_deviation(self.compute_bias(y),
self.bias_mean[nhl],
self.bias_variance[nhl],
self.number_samples_feed)
self.var_mean[nhl], self.var_variance[nhl], self.var_standard_deviation[nhl] = \
MyUtil.recursive_mean_standard_deviation(self.network_variance,
self.var_mean[nhl],
self.var_variance[nhl],
self.number_samples_feed)
if self.number_samples_layer[nhl] <= 1 or self.growable[nhl]:
self.minimum_bias_mean[nhl] = self.bias_mean[nhl]
self.minimum_bias_standard_deviation[nhl] = self.bias_standard_deviation[nhl]
else:
self.minimum_bias_mean[nhl] = np.min([self.minimum_bias_mean[nhl], self.bias_mean[nhl]])
self.minimum_bias_standard_deviation[nhl] = np.min([self.minimum_bias_standard_deviation[nhl], self.bias_standard_deviation[nhl]])
if self.number_samples_layer[nhl] <= self.input_size + 1 or self.prunable[nhl][0] != -1:
self.minimum_var_mean[nhl] = self.var_mean[nhl]
self.minimum_var_standard_deviation[nhl] = self.var_standard_deviation[nhl]
else:
self.minimum_var_mean[nhl] = np.min([self.minimum_var_mean[nhl], self.var_mean[nhl]])
self.minimum_var_standard_deviation[nhl] = np.min([self.minimum_var_standard_deviation[nhl], self.var_standard_deviation[nhl]])
self.BIAS.append(self.bias_mean[nhl])
self.VAR.append(self.var_mean[nhl])
self.growable[nhl] = self.is_growable(self.compute_bias(y))
self.prunable[nhl] = self.is_prunable(prune_strategy)
def is_growable(self, bias: torch.tensor, alpha_1: float = 1.25, alpha_2: float = 0.75):
nhl = self.number_hidden_layers # readability
current = self.bias_mean[nhl] + self.bias_standard_deviation[nhl]
biased_min = self.minimum_bias_mean[nhl] \
+ (alpha_1 * torch.exp(-bias) + alpha_2) * self.minimum_bias_standard_deviation[nhl]
if self.number_samples_layer[nhl] > 1 and current >= biased_min:
return True
return False
def is_prunable(self, prune_strategy: int = None, alpha_1: float = 2.5, alpha_2: float = 1.5):
if prune_strategy is None:
prune_strategy = self.PRUNE_NODE_STRATEGY_MULTIPLE
nhl = self.number_hidden_layers # readability
current = self.var_mean[nhl] + self.var_standard_deviation[nhl]
biased_min = self.minimum_var_mean[nhl] \
+ (alpha_1 * torch.exp(-self.network_variance) + alpha_2) * self.minimum_var_standard_deviation[nhl]
if not self.growable[nhl] \
and self.layers[nhl] > 1 \
and self.number_samples_layer[nhl] > self.input_size + 1 \
and current >= biased_min:
if prune_strategy == self.PRUNE_NODE_STRATEGY_SINGLE:
return torch.argmin(self.Eh)
elif prune_strategy == self.PRUNE_NODE_STRATEGY_MULTIPLE:
nodes_to_prune = torch.where(self.Eh < torch.abs(torch.mean(self.Eh) - torch.var(self.Eh)))
if len(nodes_to_prune[0]):
return nodes_to_prune[0]
else:
return torch.argmin(self.Eh)
return [-1]
def grow_node(self, layer_number: int):
self.layers[layer_number] += 1
if layer_number >= 0:
self.grow_weight_row(layer_number - 1)
self.grow_bias(layer_number - 1)
if layer_number <= self.number_hidden_layers:
self.grow_weight_column(layer_number)
def grow_weight_row(self, layer_number: int):
def add_element(tensor_data: torch.tensor, momentum_tensor_data: torch.tensor, n_out: int):
tensor_data = torch.cat((tensor_data, self.xavier_weight_initialization(1, n_out)), axis=0)
momentum_tensor_data = torch.cat((momentum_tensor_data, torch.zeros(1, n_out, dtype=torch.float, device=MyDevice().get())), axis=0)
return tensor_data, momentum_tensor_data
if layer_number >= len(self.weight):
[_, n_out] = self.output_weight.shape
self.output_weight, self.output_momentum = add_element(self.output_weight, self.output_momentum, n_out)
else:
[_, n_out] = self.weight[layer_number].shape
self.weight[layer_number], self.momentum[layer_number] = add_element(self.weight[layer_number], self.momentum[layer_number], n_out)
def grow_weight_column(self, layer_number: int):
def add_element(tensor_data: torch.tensor, momentum_tensor_data: torch.tensor, n_out: int):
tensor_data = torch.cat((tensor_data, self.xavier_weight_initialization(n_out, 1)), axis=1)
momentum_tensor_data = torch.cat((momentum_tensor_data, torch.zeros(n_out, 1, dtype=torch.float, device=MyDevice().get())), axis=1)
return tensor_data, momentum_tensor_data
if layer_number >= len(self.weight):
[n_out, _] = self.output_weight.shape
self.output_weight, self.output_momentum = add_element(self.output_weight, self.output_momentum, n_out)
else:
[n_out, _] = self.weight[layer_number].shape
self.weight[layer_number], self.momentum[layer_number] = add_element(self.weight[layer_number], self.momentum[layer_number], n_out)
def grow_bias(self, layer_number):
def add_element(tensor_data: torch.tensor, momentum_tensor_data: torch.tensor, n_out: int):
tensor_data = torch.cat((tensor_data, self.xavier_weight_initialization(1, n_out)), axis=1)
momentum_tensor_data = torch.cat((momentum_tensor_data, torch.zeros(1, n_out, dtype=torch.float, device=MyDevice().get())), axis=1)
return tensor_data, momentum_tensor_data
if layer_number >= len(self.bias):
[n_out, _] = self.output_bias.shape
self.output_bias, self.output_bias_momentum = add_element(self.output_bias, self.output_bias_momentum, n_out)
else:
[n_out, _] = self.bias[layer_number].shape
self.bias[layer_number], self.bias_momentum[layer_number] = add_element(self.bias[layer_number], self.bias_momentum[layer_number], n_out)
pass
def prune_node(self, layer_number: int, node_number: int):
self.layers[layer_number] -= 1
if layer_number >= 0:
self.prune_weight_row(layer_number - 1, node_number)
self.prune_bias(layer_number - 1, node_number)
if layer_number <= self.number_hidden_layers:
self.prune_weight_column(layer_number, node_number)
def prune_weight_row(self, layer_number: int, node_number: int):
def remove_nth_row(tensor_data: torch.tensor, n: int):
return torch.cat([tensor_data[:n], tensor_data[n+1:]])
if layer_number >= len(self.weight):
self.output_weight = remove_nth_row(self.output_weight, node_number)
self.output_momentum = remove_nth_row(self.output_momentum, node_number)
else:
self.weight[layer_number] = remove_nth_row(self.weight[layer_number], node_number)
self.momentum[layer_number] = remove_nth_row(self.momentum[layer_number], node_number)
def prune_weight_column(self, layer_number: int, node_number: int):
def remove_nth_column(weight_tensor: torch.tensor, n: int):
return torch.cat([weight_tensor.T[:n], weight_tensor.T[n+1:]]).T
if layer_number >= len(self.weight):
self.output_weight = remove_nth_column(self.output_weight, node_number)
self.output_momentum = remove_nth_column(self.output_momentum, node_number)
else:
self.weight[layer_number] = remove_nth_column(self.weight[layer_number], node_number)
self.momentum[layer_number] = remove_nth_column(self.momentum[layer_number], node_number)
def prune_bias(self, layer_number: int, node_number: int):
def remove_nth_element(bias_tensor: torch.tensor, n: int):
bias_tensor = torch.cat([bias_tensor[0][:n], bias_tensor[0][n+1:]])
return bias_tensor.view(1, bias_tensor.shape[0])
if layer_number >= len(self.bias):
self.output_bias = remove_nth_element(self.output_bias, node_number)
self.output_bias_momentum = remove_nth_element(self.output_bias_momentum, node_number)
else:
self.bias[layer_number] = remove_nth_element(self.bias[layer_number], node_number)
self.bias_momentum[layer_number] = remove_nth_element(self.bias_momentum[layer_number], node_number)