Optimisation

In [0]:

import torch
import numpy as np
import matplotlib.pyplot as plt

In [76]:

x = torch.Tensor([i for i in range(10,20)])
x

Out[76]:

tensor([10., 11., 12., 13., 14., 15., 16., 17., 18., 19.])

In [0]:

y = x*1.609 

In [78]:

plt.scatter(x,y)

Out[78]:

<matplotlib.collections.PathCollection at 0x7f3718db57f0>

In [0]:

import torch.nn as nn

In [0]:

class LinearRegression(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(1,1,bias=False)
    def forward(self,x):
        return self.linear(x)

In [92]:

model = LinearRegression()
w1= model.parameters()
print(list(w1))

[Parameter containing:
tensor([[0.4194]], requires_grad=True)]

In [0]:

optimizer = torch.optim.SGD(model.parameters(), lr = 0.001)
criterion = nn.MSELoss()

In [94]:

loss_list = []
for epoch in range(20):
    y_pred = model.forward(x.reshape(-1,1))
    loss = criterion(y_pred, y.reshape(-1,1))
    loss.backward()
    print(f"Epoch={epoch} --> Loss = {loss.item()}")
    loss_list.append(loss.item())
    optimizer.step()
    optimizer.zero_grad()

Epoch=0 --> Loss = 309.2274169921875
Epoch=1 --> Loss = 98.01548767089844
Epoch=2 --> Loss = 31.06787109375
Epoch=3 --> Loss = 9.847551345825195
Epoch=4 --> Loss = 3.1213696002960205
Epoch=5 --> Loss = 0.9893785119056702
Epoch=6 --> Loss = 0.3136022984981537
Epoch=7 --> Loss = 0.09940174967050552
Epoch=8 --> Loss = 0.03150755539536476
Epoch=9 --> Loss = 0.009987023659050465
Epoch=10 --> Loss = 0.0031654895283281803
Epoch=11 --> Loss = 0.0010033544385805726
Epoch=12 --> Loss = 0.00031806365586817265
Epoch=13 --> Loss = 0.00010081244545290247
Epoch=14 --> Loss = 3.195372846676037e-05
Epoch=15 --> Loss = 1.0126556844625156e-05
Epoch=16 --> Loss = 3.2112154713104246e-06
Epoch=17 --> Loss = 1.019165210891515e-06
Epoch=18 --> Loss = 3.2389544912803103e-07
Epoch=19 --> Loss = 1.0280818685259874e-07

In [95]:

list(model.parameters())

Out[95]:

[Parameter containing:
 tensor([[1.6090]], requires_grad=True)]

In [0]:

In [104]:

model = LinearRegression()
w1= model.parameters()
print(list(w1))

[Parameter containing:
tensor([[-0.0252]], requires_grad=True)]

In [0]:

optimizer = torch.optim.Adam(model.parameters(), lr = 0.01)
criterion = nn.MSELoss()

In [106]:

loss_list = []
for epoch in range(20):
    y_pred = model.forward(x.reshape(-1,1))
    loss = criterion(y_pred, y.reshape(-1,1))
    loss.backward()
    print(f"Epoch={epoch} --> Loss = {loss.item()}")
    loss_list.append(loss.item())
    optimizer.step()
    optimizer.zero_grad()

Epoch=0 --> Loss = 583.5047607421875
Epoch=1 --> Loss = 576.3853149414062
Epoch=2 --> Loss = 569.3107299804688
Epoch=3 --> Loss = 562.28173828125
Epoch=4 --> Loss = 555.2991333007812
Epoch=5 --> Loss = 548.3635864257812
Epoch=6 --> Loss = 541.4757690429688
Epoch=7 --> Loss = 534.636474609375
Epoch=8 --> Loss = 527.8462524414062
Epoch=9 --> Loss = 521.1057739257812
Epoch=10 --> Loss = 514.4155883789062
Epoch=11 --> Loss = 507.7763671875
Epoch=12 --> Loss = 501.1886291503906
Epoch=13 --> Loss = 494.65283203125
Epoch=14 --> Loss = 488.1695556640625
Epoch=15 --> Loss = 481.73919677734375
Epoch=16 --> Loss = 475.3621826171875
Epoch=17 --> Loss = 469.0389404296875
Epoch=18 --> Loss = 462.76983642578125
Epoch=19 --> Loss = 456.55517578125

In [0]:

In [107]:

import os
os.listdir()

Out[107]:

['.config', 'diabetes.csv.gz', 'sample_data']

In [0]:

import pandas as pd

In [0]:

data = pd.read_csv('diabetes.csv.gz', compression='gzip', header=None, dtype=np.float32)

In [110]:

data.head()

Out[110]:

	0	1	2	3	4	5	6	7	8
0	-0.294118	0.487437	0.180328	-0.292929	0.000000	0.001490	-0.531170	-0.033333	0.0
1	-0.882353	-0.145729	0.081967	-0.414141	0.000000	-0.207153	-0.766866	-0.666667	1.0
2	-0.058823	0.839196	0.049180	0.000000	0.000000	-0.305514	-0.492741	-0.633333	0.0
3	-0.882353	-0.105528	0.081967	-0.535354	-0.777778	-0.162444	-0.923997	0.000000	1.0
4	0.000000	0.376884	-0.344262	-0.292929	-0.602837	0.284650	0.887276	-0.600000	0.0

In [111]:

data.shape

Out[111]:

(759, 9)

In [0]:

X = data.iloc[:,:-1].values
y = data.iloc[:,-1].values

In [0]:

X = torch.from_numpy(X)
y = torch.from_numpy(y)

In [0]:

class LinearRegression(nn.Module):
    def __init__(self):
        super().__init__()
        self.l1 = nn.Linear(8,1)
    def forward(self,x):
        #x = torch.relu(self.l1(x))
        x = torch.sigmoid(self.l1(x))
        #x = torch.sigmoid(self.l3(x))
        return x

In [130]:

model = LinearRegression()
optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)
criterion = nn.BCELoss()

for epoch in range(150):
    y_pred = model(X)
    loss = criterion(y_pred, y)
    loss.backward()
    print(loss.item())
    optimizer.step()
    optimizer.zero_grad()

0.6467465162277222
0.6463687419891357
0.6459944844245911
0.6456233859062195
0.6452562212944031
0.6448914408683777
0.644530177116394
0.6441720128059387
0.6438170671463013
0.6434657573699951
0.6431170105934143
0.6427711844444275
0.6424285769462585
0.6420890092849731
0.6417526006698608
0.6414187550544739
0.6410880088806152
0.6407593488693237
0.6404342651367188
0.6401126980781555
0.6397926211357117
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0.6391616463661194
0.6388500928878784
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0.6379315853118896
0.6376306414604187
0.6373316049575806
0.637035608291626
0.6367424726486206
0.6364511847496033
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0.6137142181396484

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py:498: UserWarning: Using a target size (torch.Size([759])) that is different to the input size (torch.Size([759, 1])) is deprecated. Please ensure they have the same size.
  return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)

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