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models.py
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# -*- coding: utf-8 -*-
""" Tried Models. """
import torch
from torch import nn
from torch import optim
from torch.autograd import Variable
from modelWrapper import modelWrapper
def prepare_data(data):
X, y = Variable(data.X), Variable(data.y)
return X, y
# ------------------- 2D convolution + MaxPool -------------------
class CNN2D_MaxPool(modelWrapper):
def __init__(self, **kwargs):
super(CNN2D_MaxPool, self).__init__(**kwargs)
self.features = [
nn.Conv2d(1, 32, kernel_size=(3, 7), padding=(1, 3)),
nn.MaxPool2d(2),
self.setting["activation"](),
nn.Dropout(self.setting["dropout"])
]
for i in range(self.setting["nb_layers"]):
self.features += [
nn.Conv2d(32, 32, kernel_size=3, padding=1),
self.setting["activation"](),
nn.Dropout(self.setting["dropout"])
]
self.features += [
nn.MaxPool2d((2, 5)),
nn.Conv2d(32, 32, kernel_size=5, padding=2),
nn.MaxPool2d(2, padding=(1, 1)),
self.setting["activation"](),
nn.Dropout(self.setting["dropout"])
]
self.features = nn.Sequential(*self.features)
self.num_features = 384
self.classifier = nn.Sequential(
nn.Linear(self.num_features, self.setting["nb_hidden"]),
self.setting["activation"](),
nn.Linear(self.setting["nb_hidden"], 2)
)
self.criterion = nn.CrossEntropyLoss()
self.optimizer = self.setting["optimizer"](self.parameters(), weight_decay=self.setting["weight_decay"])
def prepare_data(data):
X, y = Variable(data.X.clone().unsqueeze(1)), Variable(data.y)
return X, y
# ---------------------------------------------------------
# ------------------- 1D convolution + dropout + MaxPool -------------------
class CNN_1D_MaxPool(modelWrapper):
def __init__(self, **kwargs):
super(CNN_1D_MaxPool, self).__init__(**kwargs)
self.features = [
nn.Conv1d(28, 32, kernel_size=5, padding=2),
self.setting["activation"](),
nn.Dropout(self.setting["dropout"])
]
for i in range(self.setting["nb_layers"]):
self.features += [
nn.Conv1d(32, 32, kernel_size=5, padding=2),
self.setting["activation"](),
nn.Dropout(self.setting["dropout"]),
nn.Conv1d(32, 32, kernel_size=5, padding=2),
self.setting["activation"](),
nn.Dropout(self.setting["dropout"]),
]
self.features +=[
nn.MaxPool1d(2, padding=1),
nn.Conv1d(32, 32, kernel_size=5, padding=2),
nn.MaxPool1d(2, padding=1),
self.setting["activation"](),
nn.Dropout(self.setting["dropout"]),
]
self.features = nn.Sequential(*self.features)
self.num_features = 448
self.classifier = nn.Sequential(
nn.Linear(self.num_features, self.setting["nb_hidden"]),
self.setting["activation"](),
nn.Linear(self.setting["nb_hidden"], 2)
)
self.criterion = nn.CrossEntropyLoss()
self.optimizer = self.setting["optimizer"](self.parameters(), weight_decay=self.setting["weight_decay"])
def prepare_data(data):
return prepare_data(data)
# ---------------------------------------------------------
# ------------------- 1D convoution + dropout + MaxPool1d + batchnorm -------------------
# same structure as before but with batchnorm
class CNN_1D_BatchNorm(modelWrapper):
def __init__(self, **kwargs):
super(CNN_1D_BatchNorm, self).__init__(**kwargs)
self.features = [
nn.BatchNorm1d(28),
nn.Conv1d(28, 32, kernel_size=5, padding=2),
nn.BatchNorm1d(32),
self.setting["activation"](),
nn.Dropout(self.setting["dropout"])
]
for i in range(self.setting["nb_layers"]):
self.features += [
nn.Conv1d(32, 32, kernel_size=5, padding=2),
nn.BatchNorm1d(32),
self.setting["activation"](),
nn.Dropout(self.setting["dropout"]),
nn.Conv1d(32, 32, kernel_size=5, padding=2),
nn.BatchNorm1d(32),
self.setting["activation"](),
nn.Dropout(self.setting["dropout"])
]
self.features += [
nn.MaxPool1d(2, padding=1),
nn.Conv1d(32, 32, kernel_size=5, padding=2),
nn.BatchNorm1d(32),
nn.MaxPool1d(2),
self.setting["activation"](),
nn.Dropout(self.setting["dropout"])
]
self.features = nn.Sequential(*self.features)
self.num_features = 32*13
self.classifier = nn.Sequential(
nn.Linear(self.num_features, self.setting["nb_hidden"]),
self.setting["activation"](),
nn.Linear(self.setting["nb_hidden"], 2)
)
self.criterion = nn.CrossEntropyLoss()
self.optimizer = self.setting["optimizer"](self.parameters(), weight_decay=self.setting["weight_decay"])
def prepare_data(data):
return prepare_data(data)
# ---------------------------------------------------------
# ------------------- 1D dialated convolution + dropout + batch norm -------------------
class CNN_1D_BatchNorm_Dial(modelWrapper):
def __init__(self, **kwargs):
super(CNN_1D_BatchNorm_Dial, self).__init__(**kwargs)
self.features = [
nn.BatchNorm1d(28),
nn.Conv1d(28, 32, kernel_size=3, padding=2, dilation=2),
nn.BatchNorm1d(32),
self.setting["activation"](),
nn.Dropout(self.setting["dropout"])
]
for i in range(self.setting["nb_layers"]):
self.features += [
nn.Conv1d(32, 32, kernel_size=5, padding=4, dilation=2),
nn.BatchNorm1d(32),
self.setting["activation"](),
nn.Dropout(self.setting["dropout"]),
nn.Conv1d(32, 32, kernel_size=5, padding=2),
nn.BatchNorm1d(32),
self.setting["activation"](),
nn.Dropout(self.setting["dropout"])
]
self.features += [
nn.Conv1d(32, 16, kernel_size=3, padding=1),
nn.BatchNorm1d(16),
self.setting["activation"](),
nn.Dropout(self.setting["dropout"])
]
self.features = nn.Sequential(*self.features)
self.num_features = 16*50
self.classifier = nn.Sequential(
nn.Linear(self.num_features, self.setting["nb_hidden"]),
self.setting["activation"](),
nn.Linear(self.setting["nb_hidden"], 2)
)
self.criterion = nn.CrossEntropyLoss()
self.optimizer = self.setting["optimizer"](self.parameters(), weight_decay=self.setting["weight_decay"])
def prepare_data(data):
return prepare_data(data)
# ---------------------------------------------------------
# --------------- 1D convolution residual network with aggregated modules + batchnorm ---------------
class residual_block(nn.Module):
def __init__(self, activation=nn.ReLU):
super(residual_block, self).__init__()
self.features = nn.Sequential(
nn.Conv1d(32, 32, kernel_size=3, padding=2, dilation=2),
nn.BatchNorm1d(32),
activation(),
nn.Conv1d(32, 32, kernel_size=3, padding=1),
nn.BatchNorm1d(32),
activation(),
nn.Conv1d(32, 32, kernel_size=3, padding=1),
nn.BatchNorm1d(32),
activation()
)
def forward(self, x):
return x+self.features(x)
class aggregated_residual_blocks(nn.Module):
def __init__(self, n_residual_blocks=2, activation=nn.ReLU, dropout=0):
super(aggregated_residual_blocks, self).__init__()
self.residual_blocks = nn.ModuleList()
for i in range(n_residual_blocks):
self.residual_blocks.append(residual_block(activation=activation))
self.residual_blocks.append(nn.Dropout(dropout))
def forward(self, x):
out = []
for block in self.residual_blocks:
out.append(block(x))
return sum(out)+x
class CNN_1D_Residual(modelWrapper):
def __init__(self, n_aggregated_residual_blocks=3, n_residual_blocks=2, **kwargs):
# n_aggregated_residual_blocks: number of aggregated residual blocks (aggregated_residual_blocks)
# n_residual_blocks: number of residual blocks per aggregated residual block
super(CNN_1D_Residual, self).__init__()
self.n_aggregated_residual_blocks = n_aggregated_residual_blocks
self.n_residual_blocks = n_residual_blocks
self.features = [
nn.BatchNorm1d(28),
nn.Conv1d(28, 32, kernel_size=3, padding=2, dilation=2),
nn.BatchNorm1d(32),
self.setting["activation"](),
]
for i in range(self.setting["nb_layers"]):
#for i in range(n_aggregated_residual_blocks):
self.features +=[
aggregated_residual_blocks(n_residual_blocks),
nn.Dropout(self.setting["dropout"])
]
self.features += [
nn.Conv1d(32, 16, kernel_size=3, padding=1),
nn.BatchNorm1d(16),
nn.MaxPool1d(2),
self.setting["activation"](),
nn.Dropout(self.setting["dropout"])
]
self.features = nn.Sequential(*self.features)
self.num_features = 16*25
self.classifier = nn.Sequential(
nn.Linear(self.num_features, self.setting["nb_hidden"]),
self.setting["activation"](),
nn.Linear(self.setting["nb_hidden"], 2)
)
self.criterion = nn.CrossEntropyLoss()
self.optimizer = self.setting["optimizer"](self.parameters(), weight_decay=self.setting["weight_decay"])
def prepare_data(data):
return prepare_data(data)
def clear(self):
device = next(self.parameters()).device
self.__init__(n_aggregated_residual_blocks = self.n_aggregated_residual_blocks,
n_residual_blocks = self.n_residual_blocks,
**self.setting
)
self.to(device)
# ---------------------------------------------------------------------------