Important: This notebook will only work with fastai-0.7.x. Do not try to run any fastai-1.x code from this path in the repository because it will load fastai-0.7.x

Table of Contents¶

1 Linear Regression problem

2 Gradient Descent

3 Gradient Descent - Classification

4 Gradient descent with numpy

In [ ]:

%matplotlib inline
from fastai.learner import *

In this part of the lecture we explain Stochastic Gradient Descent (SGD) which is an optimization method commonly used in neural networks. We will illustrate the concepts with concrete examples.

Linear Regression problem¶

The goal of linear regression is to fit a line to a set of points.

In [ ]:

# Here we generate some fake data
def lin(a,b,x): return a*x+b

def gen_fake_data(n, a, b):
    x = s = np.random.uniform(0,1,n) 
    y = lin(a,b,x) + 0.1 * np.random.normal(0,3,n)
    return x, y

x, y = gen_fake_data(50, 3., 8.)

In [ ]:

plt.scatter(x,y, s=8); plt.xlabel("x"); plt.ylabel("y"); 

You want to find parameters (weights) $a$ and $b$ such that you minimize the error between the points and the line $a\cdot x + b$. Note that here $a$ and $b$ are unknown. For a regression problem the most common error function or loss function is the mean squared error.

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def mse(y_hat, y): return ((y_hat - y) ** 2).mean()

Suppose we believe $a = 10$ and $b = 5$ then we can compute y_hat which is our prediction and then compute our error.

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y_hat = lin(10,5,x)
mse(y_hat, y)

Out[ ]:

4.1001300495563058

In [ ]:

def mse_loss(a, b, x, y): return mse(lin(a,b,x), y)

In [ ]:

mse_loss(10, 5, x, y)

Out[ ]:

4.1001300495563058

So far we have specified the model (linear regression) and the evaluation criteria (or loss function). Now we need to handle optimization; that is, how do we find the best values for $a$ and $b$? How do we find the best fitting linear regression.

Gradient Descent¶

For a fixed dataset $x$ and $y$ mse_loss(a,b) is a function of $a$ and $b$. We would like to find the values of $a$ and $b$ that minimize that function.

Gradient descent is an algorithm that minimizes functions. Given a function defined by a set of parameters, gradient descent starts with an initial set of parameter values and iteratively moves toward a set of parameter values that minimize the function. This iterative minimization is achieved by taking steps in the negative direction of the function gradient.

Here is gradient descent implemented in PyTorch.

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# generate some more data
x, y = gen_fake_data(10000, 3., 8.)
x.shape, y.shape

Out[ ]:

((10000,), (10000,))

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x,y = V(x),V(y)

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# Create random weights a and b, and wrap them in Variables.
a = V(np.random.randn(1), requires_grad=True)
b = V(np.random.randn(1), requires_grad=True)
a,b

Out[ ]:

(Variable containing:
 1.00000e-02 *
   2.9873
 [torch.FloatTensor of size 1], Variable containing:
  0.1116
 [torch.FloatTensor of size 1])

In [ ]:

learning_rate = 1e-3
for t in range(10000):
    # Forward pass: compute predicted y using operations on Variables
    loss = mse_loss(a,b,x,y)
    if t % 1000 == 0: print(loss.data[0])
    
    # Computes the gradient of loss with respect to all Variables with requires_grad=True.
    # After this call a.grad and b.grad will be Variables holding the gradient
    # of the loss with respect to a and b respectively
    loss.backward()
    
    # Update a and b using gradient descent; a.data and b.data are Tensors,
    # a.grad and b.grad are Variables and a.grad.data and b.grad.data are Tensors
    a.data -= learning_rate * a.grad.data
    b.data -= learning_rate * b.grad.data
    
    # Zero the gradients
    a.grad.data.zero_()
    b.grad.data.zero_()    

89.19391632080078
0.6885505318641663
0.11982045322656631
0.11007291823625565
0.10528462380170822
0.10161882638931274
0.09879907965660095
0.09662991762161255
0.09496115148067474
0.09367774426937103

Nearly all of deep learning is powered by one very important algorithm: stochastic gradient descent (SGD). SGD can be seeing as an approximation of gradient descent (GD). In GD you have to run through all the samples in your training set to do a single itaration. In SGD you use only one or a subset of training samples to do the update for a parameter in a particular iteration. The subset use in every iteration is called a batch or minibatch.

Gradient Descent - Classification¶

For a fixed dataset $x$ and $y$ mse_loss(a,b) is a function of $a$ and $b$. We would like to find the values of $a$ and $b$ that minimize that function.

Gradient descent is an algorithm that minimizes functions. Given a function defined by a set of parameters, gradient descent starts with an initial set of parameter values and iteratively moves toward a set of parameter values that minimize the function. This iterative minimization is achieved by taking steps in the negative direction of the function gradient.

Here is gradient descent implemented in PyTorch.

In [ ]:

def gen_fake_data2(n, a, b):
    x = s = np.random.uniform(0,1,n) 
    y = lin(a,b,x) + 0.1 * np.random.normal(0,3,n)
    return x, np.where(y>10, 1, 0).astype(np.float32)

In [ ]:

x,y = gen_fake_data2(10000, 3., 8.)
x,y = V(x),V(y)

In [ ]:

def nll(y_hat, y):
    y_hat = torch.clamp(y_hat, 1e-5, 1-1e-5)
    return (y*y_hat.log() + (1-y)*(1-y_hat).log()).mean()

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a = V(np.random.randn(1), requires_grad=True)
b = V(np.random.randn(1), requires_grad=True)

In [ ]:

learning_rate = 1e-2
for t in range(3000):
    p = (-lin(a,b,x)).exp()
    y_hat = 1/(1+p)
    loss = nll(y_hat,y)
    if t % 1000 == 0:
        print(loss.data[0], np.mean(to_np(y)==(to_np(y_hat)>0.5)))
#         print(y_hat)
    
    loss.backward()
    a.data -= learning_rate * a.grad.data
    b.data -= learning_rate * b.grad.data
    a.grad.data.zero_()
    b.grad.data.zero_()    

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
~/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py in _wrapfunc(obj, method, *args, **kwds)
     56     try:
---> 57         return getattr(obj, method)(*args, **kwds)
     58 

~/anaconda3/lib/python3.6/site-packages/torch/autograd/variable.py in __getattr__(self, name)
     64             return getattr(self.data, name)
---> 65         return object.__getattribute__(self, name)
     66 

AttributeError: 'Variable' object has no attribute 'clip'

During handling of the above exception, another exception occurred:

RuntimeError                              Traceback (most recent call last)
<ipython-input-67-b1668cd3a12f> in <module>()
      3     p = (-lin(a,b,x)).exp()
      4     y_hat = 1/(1+p)
----> 5     loss = nll(y_hat,y)
      6     if t % 1000 == 0:
      7         print(loss.data[0], np.mean(to_np(y)==(to_np(y_hat)>0.5)))

<ipython-input-64-fbf65a879f37> in nll(y_hat, y)
      1 def nll(y_hat, y):
----> 2     y_hat = np.clip(y_hat, 1e-5, 1-1e-5)
      3     return (y*y_hat.log() + (1-y)*(1-y_hat).log()).mean()

~/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py in clip(a, a_min, a_max, out)
   1705 
   1706     """
-> 1707     return _wrapfunc(a, 'clip', a_min, a_max, out=out)
   1708 
   1709 

~/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py in _wrapfunc(obj, method, *args, **kwds)
     65     # a downstream library like 'pandas'.
     66     except (AttributeError, TypeError):
---> 67         return _wrapit(obj, method, *args, **kwds)
     68 
     69 

~/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py in _wrapit(obj, method, *args, **kwds)
     45     except AttributeError:
     46         wrap = None
---> 47     result = getattr(asarray(obj), method)(*args, **kwds)
     48     if wrap:
     49         if not isinstance(result, mu.ndarray):

~/anaconda3/lib/python3.6/site-packages/torch/autograd/variable.py in __bool__(self)
    121             return False
    122         raise RuntimeError("bool value of Variable objects containing non-empty " +
--> 123                            torch.typename(self.data) + " is ambiguous")
    124 
    125     __nonzero__ = __bool__

RuntimeError: bool value of Variable objects containing non-empty torch.ByteTensor is ambiguous

Nearly all of deep learning is powered by one very important algorithm: stochastic gradient descent (SGD). SGD can be seeing as an approximation of gradient descent (GD). In GD you have to run through all the samples in your training set to do a single itaration. In SGD you use only one or a subset of training samples to do the update for a parameter in a particular iteration. The subset use in every iteration is called a batch or minibatch.

Gradient descent with numpy¶

In [ ]:

from matplotlib import rcParams, animation, rc
from ipywidgets import interact, interactive, fixed
from ipywidgets.widgets import *
rc('animation', html='html5')
rcParams['figure.figsize'] = 3, 3

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x, y = gen_fake_data(50, 3., 8.)

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a_guess,b_guess = -1., 1.
mse_loss(a_guess, b_guess, x, y)

Out[ ]:

65.167827371047636

In [ ]:

lr=0.01
def upd():
    global a_guess, b_guess
    y_pred = lin(a_guess, b_guess, x)
    dydb = 2 * (y_pred - y)
    dyda = x*dydb
    a_guess -= lr*dyda.mean()
    b_guess -= lr*dydb.mean()

In [ ]:

fig = plt.figure(dpi=100, figsize=(5, 4))
plt.scatter(x,y)
line, = plt.plot(x,lin(a_guess,b_guess,x))
plt.close()

def animate(i):
    line.set_ydata(lin(a_guess,b_guess,x))
    for i in range(30): upd()
    return line,

ani = animation.FuncAnimation(fig, animate, np.arange(0, 20), interval=100)
ani

Out[ ]:

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