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jinglescode / ssvep-multi-task-learning.ipynb
Created September 9, 2020 06:42
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@jinglescode
jinglescode / generate_signal.py
Last active September 15, 2020 06:31
import numpy as np
import matplotlib.pyplot as plt
def generate_signal(length_seconds, sampling_rate, frequencies_list, func="sin", add_noise=0, plot=True):
r"""
Generate a `length_seconds` seconds signal at `sampling_rate` sampling rate. See torchsignal (https://github.com/jinglescode/torchsignal) for more info.
Args:
length_seconds : int
@jinglescode
jinglescode / conv1d-kernel1.py
Created November 2, 2020 09:24
class TestConv1d(nn.Module):
def __init__(self):
super(TestConv1d, self).__init__()
self.conv = nn.Conv1d(in_channels=1, out_channels=1, kernel_size=1, bias=False)
self.init_weights()
def forward(self, x):
return self.conv(x)
def init_weights(self):
@jinglescode
jinglescode / conv1d-kernel2.py
Created November 2, 2020 09:25
class TestConv1d(nn.Module):
def __init__(self):
super(TestConv1d, self).__init__()
self.conv = nn.Conv1d(in_channels=1, out_channels=1, kernel_size=2, bias=False)
self.init_weights()
def forward(self, x):
return self.conv(x)
def init_weights(self):
@jinglescode
jinglescode / conv1d-kernel3.py
Created November 2, 2020 09:26
class TestConv1d(nn.Module):
def __init__(self):
super(TestConv1d, self).__init__()
self.conv = nn.Conv1d(in_channels=1, out_channels=1, kernel_size=3, bias=False)
self.init_weights()
def forward(self, x):
return self.conv(x)
def init_weights(self):
@jinglescode
jinglescode / conv1d-padding.py
Created November 2, 2020 09:27
class TestConv1d(nn.Module):
def __init__(self):
super(TestConv1d, self).__init__()
self.conv = nn.Conv1d(in_channels=1, out_channels=1, kernel_size=3, padding=1, bias=False)
self.init_weights()
def forward(self, x):
return self.conv(x)
def init_weights(self):
@jinglescode
jinglescode / conv1d-padding2.py
Created November 2, 2020 09:30
class TestConv1d(nn.Module):
def __init__(self):
super(TestConv1d, self).__init__()
self.conv = nn.Conv1d(in_channels=1, out_channels=1, kernel_size=5, padding=2, bias=False)
self.init_weights()
def forward(self, x):
return self.conv(x)
def init_weights(self):
@jinglescode
jinglescode / conv1d-stride.py
Last active November 2, 2020 09:31
class TestConv1d(nn.Module):
def __init__(self):
super(TestConv1d, self).__init__()
self.conv = nn.Conv1d(in_channels=1, out_channels=1, kernel_size=3, stride=3, bias=False)
self.init_weights()
def forward(self, x):
return self.conv(x)
def init_weights(self):
@jinglescode
jinglescode / conv1d-dilation.py
Created November 2, 2020 09:32
class TestConv1d(nn.Module):
def __init__(self):
super(TestConv1d, self).__init__()
self.conv = nn.Conv1d(in_channels=1, out_channels=1, kernel_size=3, dilation=2, bias=False)
self.init_weights()
def forward(self, x):
return self.conv(x)
def init_weights(self):
@jinglescode
jinglescode / conv1d-group.py
Last active November 2, 2020 09:33
class TestConv1d(nn.Module):
def __init__(self):
super(TestConv1d, self).__init__()
self.conv = nn.Conv1d(in_channels=2, out_channels=2, kernel_size=1, groups=2, bias=False)
self.init_weights()
def forward(self, x):
return self.conv(x)
def init_weights(self):