Note

This sections assumes familiarity with the following Theano concepts: shared variables , basic arithmetic ops , T.grad , floatX. If you intend to run the code on GPU also read GPU.

Note

The code for this section is available for download here.

In this section, we show how Theano can be used to implement the most basic classifier: the logistic regression. We start off with a quick primer of the model, which serves both as a refresher but also to anchor the notation and show how mathematical expressions are mapped onto Theano graphs.

In the deepest of machine learning traditions, this tutorial will tackle the exciting problem of MNIST digit classification.

Logistic regression is a probabilistic, linear classifier. It is parametrized by a weight matrix and a bias vector . Classification is done by projecting data points onto a set of hyperplanes, the distance to which reflects a class membership probability.

Mathematically, this can be written as:

The output of the model or prediction is then done by taking the argmax of the vector whose i’th element is P(Y=i|x).

The code to do this in Theano is the following:

```
# generate symbolic variables for input (x and y represent a
# minibatch)
x = T.fmatrix('x')
y = T.lvector('y')
# allocate shared variables model params
b = theano.shared(numpy.zeros((10,)), name='b')
W = theano.shared(numpy.zeros((784, 10)), name='W')
# symbolic expression for computing the vector of
# class-membership probabilities
p_y_given_x = T.nnet.softmax(T.dot(x, W) + b)
# compiled Theano function that returns the vector of class-membership
# probabilities
get_p_y_given_x = theano.function(inputs=[x], outputs=p_y_given_x)
# print the probability of some example represented by x_value
# x_value is not a symbolic variable but a numpy array describing the
# datapoint
print 'Probability that x is of class %i is %f' % (i, get_p_y_given_x(x_value)[i])
# symbolic description of how to compute prediction as class whose probability
# is maximal
y_pred = T.argmax(p_y_given_x, axis=1)
# compiled theano function that returns this value
classify = theano.function(inputs=[x], outputs=y_pred)
```

We first start by allocating symbolic variables for the inputs . Since the parameters of the model must maintain a persistent state throughout training, we allocate shared variables for . This declares them both as being symbolic Theano variables, but also initializes their contents. The dot and softmax operators are then used to compute the vector . The resulting variable p_y_given_x is a symbolic variable of vector-type.

Up to this point, we have only defined the graph of computations which Theano
should perform. To get the actual numerical value of , we
must create a function `get_p_y_given_x`, which takes as input `x` and
returns `p_y_given_x`. We can then index its return value with the
index to get the membership probability of the th class.

Now let’s finish building the Theano graph. To get the actual model
prediction, we can use the `T.argmax` operator, which will return the index at
which `p_y_given_x` is maximal (i.e. the class with maximum probability).

Again, to calculate the actual prediction for a given input, we construct a
function `classify`. This function takes as argument a batch of inputs x (as a matrix),
and outputs a vector containing the predicted class for each example (row) in x.

Now of course, the model we have defined so far does not do anything useful yet, since its parameters are still in their initial random state. The following section will thus cover how to learn the optimal parameters.

Note

For a complete list of Theano ops, see: list of ops

Learning optimal model parameters involves minimizing a loss function. In the case of multi-class logistic regression, it is very common to use the negative log-likelihood as the loss. This is equivalent to maximizing the likelihood of the data set under the model parameterized by . Let us first start by defining the likelihood and loss :

While entire books are dedicated to the topic of minimization, gradient
descent is by far the simplest method for minimizing arbitrary non-linear
functions. This tutorial will use the method of stochastic gradient method with
mini-batches (MSGD). See *Stochastic Gradient Descent* for more details.

The following Theano code defines the (symbolic) loss for a given minibatch:

```
loss = -T.mean(T.log(p_y_given_x)[T.arange(y.shape[0]), y])
# note on syntax: T.arange(y.shape[0]) is a vector of integers [0,1,2,...,len(y)].
# Indexing a matrix M by the two vectors [0,1,...,K], [a,b,...,k] returns the
# elements M[0,a], M[1,b], ..., M[K,k] as a vector. Here, we use this
# syntax to retrieve the log-probability of the correct labels, y.
```

Note

Even though the loss is formally defined as the *sum*, over the data set,
of individual error terms, in practice, we use the *mean* (`T.mean`)
in the code. This allows for the learning rate choice to be less dependent
of the minibatch size.

We now have all the tools we need to define a `LogisticRegression` class, which
encapsulates the basic behaviour of logistic regression. The code is very
similar to what we have covered so far, and should be self explanatory.

```
class LogisticRegression(object):
def __init__(self, input, n_in, n_out):
""" Initialize the parameters of the logistic regression
:type input: theano.tensor.TensorType
:param input: symbolic variable that describes the input of the
architecture (e.g., one minibatch of input images)
:type n_in: int
:param n_in: number of input units, the dimension of the space in
which the datapoint lies
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the target lies
"""
# initialize with 0 the weights W as a matrix of shape (n_in, n_out)
self.W = theano.shared(value=numpy.zeros((n_in, n_out),
dtype=theano.config.floatX), name='W' )
# initialize the baises b as a vector of n_out 0s
self.b = theano.shared(value=numpy.zeros((n_out,),
dtype=theano.config.floatX), name='b' )
# compute vector of class-membership probabilities in symbolic form
self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b)
# compute prediction as class whose probability is maximal in
# symbolic form
self.y_pred=T.argmax(self.p_y_given_x, axis=1)
def negative_log_likelihood(self, y):
"""Return the mean of the negative log-likelihood of the prediction
of this model under a given target distribution.
.. math::
\frac{1}{|\mathcal{D}|} \mathcal{L} (\theta=\{W,b\}, \mathcal{D}) =
\frac{1}{|\mathcal{D}|} \sum_{i=0}^{|\mathcal{D}|} \log(P(Y=y^{(i)}|x^{(i)}, W,b)) \\
\ell (\theta=\{W,b\}, \mathcal{D})
:param y: corresponds to a vector that gives for each example the
correct label;
Note: we use the mean instead of the sum so that
the learning rate is less dependent on the batch size
"""
return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])
```

We instantiate this class as follows:

```
# allocate symbolic variables for the data
x = T.fmatrix() # the data is presented as rasterized images (each being a 1-D row vector in x)
y = T.lvector() # the labels are presented as 1D vector of [long int] labels
# construct the logistic regression class
classifier = LogisticRegression(
input=x.reshape((batch_size, 28 * 28)), n_in=28 * 28, n_out=10)
```

Note that the inputs x and y are defined outside the scope of the
`LogisticRegression` object. Since the class requires the input x to build its
graph however, it is passed as a parameter of the `__init__` function.
This is usefull in the case when you would want to concatenate such
classes to form a deep network (case in which the input is not a new
variable but the output of the layer below). While in this example we
will not do that, the tutorials are designed such that the code is as
similar as possible among them, making it easy to go from one tutorial
to the other.

The last step involves defining a (symbolic) cost variable to minimize, using
the instance method `classifier.negative_log_likelihood`.

```
cost = classifier.negative_log_likelihood(y)
```

Note how x is an implicit symbolic input to the symbolic definition of cost, here, because classifier.__init__ has defined its symbolic variables in terms of x.

To implement MSGD in most programming languages (C/C++, Matlab, Python), one would start by manually deriving the expressions for the gradient of the loss with respect to the parameters: in this case , and , This can get pretty tricky for complex models, as expressions for can get fairly complex, especially when taking into account problems of numerical stability.

With Theano, this work is greatly simplified as it performs automatic differentiation and applies certain math transforms to improve numerical stability.

To get the gradients and in Theano, simply do the following:

```
# compute the gradient of cost with respect to theta = (W,b)
g_W = T.grad(cost, classifier.W)
g_b = T.grad(cost, classifier.b)
```

`g_W` and `g_b` are again symbolic variables, which can be used as part of a
computation graph. Performing one-step of gradient descent can then be done as
follows:

```
# compute the gradient of cost with respect to theta = (W,b)
g_W = T.grad(cost=cost, wrt=classifier.W)
g_b = T.grad(cost=cost, wrt=classifier.b)
# specify how to update the parameters of the model as a list of
# (variable, update expression) pairs
updates = [(classifier.W, classifier.W - learning_rate * g_W),
(classifier.b, classifier.b - learning_rate * g_b)]
# compiling a Theano function `train_model` that returns the cost, but in
# the same time updates the parameter of the model based on the rules
# defined in `updates`
train_model = theano.function(inputs=[index],
outputs=cost,
updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size]})
```

The `updates` list contains, for each parameter, the
stochastic gradient update operation. The `givens` dictionary indicates with
what to replace certain variables of the graph. The function `train_model` is then
defined such that:

- the input is the mini-batch index
`index`that together with the batch size( which is not an input since it is fixed) defines with corresponding labels - the return value is the cost/loss associated with the x, y defined by
the
`index` - on every function call, it will first replace
`x`and`y`with the corresponding slices from the training set as defined by the`index`and afterwards it will evaluate the cost associated with that minibatch and apply the operations defined by the`updates`list.

Each time `train_model(index)` function is called, it will thus compute and
return the appropriate cost, while also performing a step of MSGD. The entire
learning algorithm thus consists in looping over all examples in the dataset,
and repeatedly calling the `train_model` function.

As explained in *Learning a Classifier*, when testing the model we are
interested in the number of misclassified examples (and not only in the likelihood).
The `LogisticRegression` class therefore has an extra instance method, which
builds the symbolic graph for retrieving the number of misclassified examples in
each minibatch.

The code is as follows:

```
class LogisticRegression(object):
...
def errors(self, y):
"""Return a float representing the number of errors in the minibatch
over the total number of examples of the minibatch ; zero
one loss over the size of the minibatch
"""
return T.mean(T.neq(self.y_pred, y))
```

We then create a function `test_model` and a function `validate_model`, which we can call to retrieve this
value. As you will see shortly, `validate_model` is key to our early-stopping
implementation (see *Early-Stopping*). Both of these function
will get as input a batch offset and will compute the number of
missclassified examples for that mini-batch. The only difference between them
is that one draws its batches from the testing set, while
the other from the validation set.

```
# compiling a Theano function that computes the mistakes that are made by
# the model on a minibatch
test_model = theano.function(inputs=[index],
outputs=classifier.errors(y),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
y: test_set_y[index * batch_size: (index + 1) * batch_size]})
validate_model = theano.function(inputs=[index],
outputs=classifier.errors(y),
givens={
x: valid_set_x[index * batch_size: (index + 1) * batch_size],
y: valid_set_y[index * batch_size: (index + 1) * batch_size]})
```

The finished product is as follows.

```
"""
This tutorial introduces logistic regression using Theano and stochastic
gradient descent.
Logistic regression is a probabilistic, linear classifier. It is parametrized
by a weight matrix :math:`W` and a bias vector :math:`b`. Classification is
done by projecting data points onto a set of hyperplanes, the distance to
which is used to determine a class membership probability.
Mathematically, this can be written as:
.. math::
P(Y=i|x, W,b) &= softmax_i(W x + b) \\
&= \frac {e^{W_i x + b_i}} {\sum_j e^{W_j x + b_j}}
The output of the model or prediction is then done by taking the argmax of
the vector whose i'th element is P(Y=i|x).
.. math::
y_{pred} = argmax_i P(Y=i|x,W,b)
This tutorial presents a stochastic gradient descent optimization method
suitable for large datasets, and a conjugate gradient optimization method
that is suitable for smaller datasets.
References:
- textbooks: "Pattern Recognition and Machine Learning" -
Christopher M. Bishop, section 4.3.2
"""
__docformat__ = 'restructedtext en'
import cPickle
import gzip
import os
import sys
import time
import numpy
import theano
import theano.tensor as T
class LogisticRegression(object):
"""Multi-class Logistic Regression Class
The logistic regression is fully described by a weight matrix :math:`W`
and bias vector :math:`b`. Classification is done by projecting data
points onto a set of hyperplanes, the distance to which is used to
determine a class membership probability.
"""
def __init__(self, input, n_in, n_out):
""" Initialize the parameters of the logistic regression
:type input: theano.tensor.TensorType
:param input: symbolic variable that describes the input of the
architecture (one minibatch)
:type n_in: int
:param n_in: number of input units, the dimension of the space in
which the datapoints lie
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the labels lie
"""
# initialize with 0 the weights W as a matrix of shape (n_in, n_out)
self.W = theano.shared(value=numpy.zeros((n_in, n_out),
dtype=theano.config.floatX),
name='W', borrow=True)
# initialize the baises b as a vector of n_out 0s
self.b = theano.shared(value=numpy.zeros((n_out,),
dtype=theano.config.floatX),
name='b', borrow=True)
# compute vector of class-membership probabilities in symbolic form
self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b)
# compute prediction as class whose probability is maximal in
# symbolic form
self.y_pred = T.argmax(self.p_y_given_x, axis=1)
# parameters of the model
self.params = [self.W, self.b]
def negative_log_likelihood(self, y):
"""Return the mean of the negative log-likelihood of the prediction
of this model under a given target distribution.
.. math::
\frac{1}{|\mathcal{D}|} \mathcal{L} (\theta=\{W,b\}, \mathcal{D}) =
\frac{1}{|\mathcal{D}|} \sum_{i=0}^{|\mathcal{D}|} \log(P(Y=y^{(i)}|x^{(i)}, W,b)) \\
\ell (\theta=\{W,b\}, \mathcal{D})
:type y: theano.tensor.TensorType
:param y: corresponds to a vector that gives for each example the
correct label
Note: we use the mean instead of the sum so that
the learning rate is less dependent on the batch size
"""
# y.shape[0] is (symbolically) the number of rows in y, i.e.,
# number of examples (call it n) in the minibatch
# T.arange(y.shape[0]) is a symbolic vector which will contain
# [0,1,2,... n-1] T.log(self.p_y_given_x) is a matrix of
# Log-Probabilities (call it LP) with one row per example and
# one column per class LP[T.arange(y.shape[0]),y] is a vector
# v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ...,
# LP[n-1,y[n-1]]] and T.mean(LP[T.arange(y.shape[0]),y]) is
# the mean (across minibatch examples) of the elements in v,
# i.e., the mean log-likelihood across the minibatch.
return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])
def errors(self, y):
"""Return a float representing the number of errors in the minibatch
over the total number of examples of the minibatch ; zero one
loss over the size of the minibatch
:type y: theano.tensor.TensorType
:param y: corresponds to a vector that gives for each example the
correct label
"""
# check if y has same dimension of y_pred
if y.ndim != self.y_pred.ndim:
raise TypeError('y should have the same shape as self.y_pred',
('y', target.type, 'y_pred', self.y_pred.type))
# check if y is of the correct datatype
if y.dtype.startswith('int'):
# the T.neq operator returns a vector of 0s and 1s, where 1
# represents a mistake in prediction
return T.mean(T.neq(self.y_pred, y))
else:
raise NotImplementedError()
def load_data(dataset):
''' Loads the dataset
:type dataset: string
:param dataset: the path to the dataset (here MNIST)
'''
#############
# LOAD DATA #
#############
# Download the MNIST dataset if it is not present
data_dir, data_file = os.path.split(dataset)
if data_dir == "" and not os.path.isfile(dataset):
# Check if dataset is in the data directory.
new_path = os.path.join(os.path.split(__file__)[0], "..", "data", dataset)
if os.path.isfile(new_path) or data_file == 'mnist.pkl.gz':
dataset = new_path
if (not os.path.isfile(dataset)) and data_file == 'mnist.pkl.gz':
import urllib
origin = 'http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz'
print 'Downloading data from %s' % origin
urllib.urlretrieve(origin, dataset)
print '... loading data'
# Load the dataset
f = gzip.open(dataset, 'rb')
train_set, valid_set, test_set = cPickle.load(f)
f.close()
#train_set, valid_set, test_set format: tuple(input, target)
#input is an numpy.ndarray of 2 dimensions (a matrix)
#witch row's correspond to an example. target is a
#numpy.ndarray of 1 dimensions (vector)) that have the same length as
#the number of rows in the input. It should give the target
#target to the example with the same index in the input.
def shared_dataset(data_xy, borrow=True):
""" Function that loads the dataset into shared variables
The reason we store our dataset in shared variables is to allow
Theano to copy it into the GPU memory (when code is run on GPU).
Since copying data into the GPU is slow, copying a minibatch everytime
is needed (the default behaviour if the data is not in a shared
variable) would lead to a large decrease in performance.
"""
data_x, data_y = data_xy
shared_x = theano.shared(numpy.asarray(data_x,
dtype=theano.config.floatX),
borrow=borrow)
shared_y = theano.shared(numpy.asarray(data_y,
dtype=theano.config.floatX),
borrow=borrow)
# When storing data on the GPU it has to be stored as floats
# therefore we will store the labels as ``floatX`` as well
# (``shared_y`` does exactly that). But during our computations
# we need them as ints (we use labels as index, and if they are
# floats it doesn't make sense) therefore instead of returning
# ``shared_y`` we will have to cast it to int. This little hack
# lets ous get around this issue
return shared_x, T.cast(shared_y, 'int32')
test_set_x, test_set_y = shared_dataset(test_set)
valid_set_x, valid_set_y = shared_dataset(valid_set)
train_set_x, train_set_y = shared_dataset(train_set)
rval = [(train_set_x, train_set_y), (valid_set_x, valid_set_y),
(test_set_x, test_set_y)]
return rval
def sgd_optimization_mnist(learning_rate=0.13, n_epochs=1000,
dataset='mnist.pkl.gz',
batch_size=600):
"""
Demonstrate stochastic gradient descent optimization of a log-linear
model
This is demonstrated on MNIST.
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type dataset: string
:param dataset: the path of the MNIST dataset file from
http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz
"""
datasets = load_data(dataset)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
# construct the logistic regression class
# Each MNIST image has size 28*28
classifier = LogisticRegression(input=x, n_in=28 * 28, n_out=10)
# the cost we minimize during training is the negative log likelihood of
# the model in symbolic format
cost = classifier.negative_log_likelihood(y)
# compiling a Theano function that computes the mistakes that are made by
# the model on a minibatch
test_model = theano.function(inputs=[index],
outputs=classifier.errors(y),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
y: test_set_y[index * batch_size: (index + 1) * batch_size]})
validate_model = theano.function(inputs=[index],
outputs=classifier.errors(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size]})
# compute the gradient of cost with respect to theta = (W,b)
g_W = T.grad(cost=cost, wrt=classifier.W)
g_b = T.grad(cost=cost, wrt=classifier.b)
# specify how to update the parameters of the model as a list of
# (variable, update expression) pairs.
updates = [(classifier.W, classifier.W - learning_rate * g_W),
(classifier.b, classifier.b - learning_rate * g_b)]
# compiling a Theano function `train_model` that returns the cost, but in
# the same time updates the parameter of the model based on the rules
# defined in `updates`
train_model = theano.function(inputs=[index],
outputs=cost,
updates=updates,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]})
###############
# TRAIN MODEL #
###############
print '... training the model'
# early-stopping parameters
patience = 5000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
test_score = 0.
start_time = time.clock()
done_looping = False
epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
minibatch_avg_cost = train_model(minibatch_index)
# iteration number
iter = (epoch - 1) * n_train_batches + minibatch_index
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
validation_losses = [validate_model(i)
for i in xrange(n_valid_batches)]
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' % \
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
best_validation_loss = this_validation_loss
# test it on the test set
test_losses = [test_model(i)
for i in xrange(n_test_batches)]
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of best'
' model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print(('Optimization complete with best validation score of %f %%,'
'with test performance %f %%') %
(best_validation_loss * 100., test_score * 100.))
print 'The code run for %d epochs, with %f epochs/sec' % (
epoch, 1. * epoch / (end_time - start_time))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.1fs' % ((end_time - start_time)))
if __name__ == '__main__':
sgd_optimization_mnist()
```

The user can learn to classify MNIST digits with SGD logistic regression, by typing, from within the DeepLearningTutorials folder:

```
python code/logistic_sgd.py
```

The output one should expect is of the form :

```
...
epoch 72, minibatch 83/83, validation error 7.510417 %
epoch 72, minibatch 83/83, test error of best model 7.510417 %
epoch 73, minibatch 83/83, validation error 7.500000 %
epoch 73, minibatch 83/83, test error of best model 7.489583 %
Optimization complete with best validation score of 7.500000 %,with test performance 7.489583 %
The code run for 74 epochs, with 1.936983 epochs/sec
```

On an Intel(R) Core(TM)2 Duo CPU E8400 @ 3.00 Ghz the code runs with approximately 1.936 epochs/sec and it took 75 epochs to reach a test error of 7.489%. On the GPU the code does almost 10.0 epochs/sec. For this instance we used a batch size of 600.

Footnotes

[1] | For smaller datasets and simpler models, more sophisticated descent algorithms can be more effective. The sample code logistic_cg.py demonstrates how to use SciPy’s conjugate gradient solver with Theano on the logistic regression task. |