pep8

Author: nouiz

code/convolutional_mlp.py

@@ -1,24 +1,33 @@
-"""
-This tutorial introduces the LeNet5 neural network architecture using Theano.  LeNet5 is a
-convolutional neural network, good for classifying images. This tutorial shows how to build the
-architecture, and comes with all the hyper-parameters you need to reproduce the paper's MNIST
-results.
+"""This tutorial introduces the LeNet5 neural network architecture
+using Theano.  LeNet5 is a convolutional neural network, good for
+classifying images. This tutorial shows how to build the architecture,
+and comes with all the hyper-parameters you need to reproduce the
+paper's MNIST results.
 
 
 This implementation simplifies the model in the following ways:
 
  - LeNetConvPool doesn't implement location-specific gain and bias parameters
- - LeNetConvPool doesn't implement pooling by average, it implements pooling by max.
- - Digit classification is implemented with a logistic regression rather than an RBF network
+ - LeNetConvPool doesn't implement pooling by average, it implements pooling
+   by max.
+ - Digit classification is implemented with a logistic regression rather than
+   an RBF network
  - LeNet5 was not fully-connected convolutions at second layer
 
 References:
- - Y. LeCun, L. Bottou, Y. Bengio and P. Haffner: Gradient-Based Learning Applied to Document
+ - Y. LeCun, L. Bottou, Y. Bengio and P. Haffner:
+   Gradient-Based Learning Applied to Document
    Recognition, Proceedings of the IEEE, 86(11):2278-2324, November 1998.
    http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf
+
 """
+import cPickle
+import gzip
+import os
+import sys
+import time
 
-import numpy, time, cPickle, gzip, sys, os
+import numpy
 
 import theano
 import theano.tensor as T
@@ -32,7 +41,7 @@
 class LeNetConvPoolLayer(object):
     """Pool Layer of a convolutional network """
 
-    def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2,2)):
+    def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2, 2)):
         """
         Allocate a LeNetConvPoolLayer with shared variable internal parameters.
 
@@ -54,56 +63,59 @@ def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2,2)):
         :param poolsize: the downsampling (pooling) factor (#rows,#cols)
         """
 
-        assert image_shape[1]==filter_shape[1]
+        assert image_shape[1] == filter_shape[1]
         self.input = input
-  
-        # initialize weights to temporary values until we know the shape of the output feature
-        # maps
+
+        # initialize weights to temporary values until we know the
+        # shape of the output feature maps
         W_values = numpy.zeros(filter_shape, dtype=theano.config.floatX)
-        self.W = theano.shared(value = W_values)
+        self.W = theano.shared(value=W_values)
 
         # the bias is a 1D tensor -- one bias per output feature map
-        b_values = numpy.zeros((filter_shape[0],), dtype= theano.config.floatX)
-        self.b = theano.shared(value= b_values)
+        b_values = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)
+        self.b = theano.shared(value=b_values)
 
         # convolve input feature maps with filters
-        conv_out = conv.conv2d(input = input, filters = self.W, 
+        conv_out = conv.conv2d(input=input, filters=self.W,
                 filter_shape=filter_shape, image_shape=image_shape)
 
-        # there are "num input feature maps * filter height * filter width" inputs
-        # to each hidden unit
+        # there are "num input feature maps * filter height * filter width"
+        # inputs to each hidden unit
         fan_in = numpy.prod(filter_shape[1:])
         # each unit in the lower layer receives a gradient from:
-        # "num output feature maps * filter height * filter width" / pooling size
-        fan_out = filter_shape[0] * numpy.prod(filter_shape[2:]) / numpy.prod(poolsize)
+        # "num output feature maps * filter height * filter width" /
+        #   pooling size
+        fan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]) /
+                   numpy.prod(poolsize))
         # replace weight values with random weights
-        W_bound = numpy.sqrt(6./(fan_in + fan_out))
+        W_bound = numpy.sqrt(6. / (fan_in + fan_out))
         self.W.set_value(numpy.asarray(
                 rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
-                dtype = theano.config.floatX),
+                dtype=theano.config.floatX),
             borrow=True)
 
         # downsample each feature map individually, using maxpooling
-        pooled_out = downsample.max_pool_2d( input = conv_out, 
-                                    ds = poolsize, ignore_border=True)
+        pooled_out = downsample.max_pool_2d(input=conv_out,
+                                            ds=poolsize, ignore_border=True)
 
         # add the bias term. Since the bias is a vector (1D array), we first
-        # reshape it to a tensor of shape (1,n_filters,1,1). Each bias will thus
-        # be broadcasted across mini-batches and feature map width & height
+        # reshape it to a tensor of shape (1,n_filters,1,1). Each bias will
+        # thus be broadcasted across mini-batches and feature map
+        # width & height
         self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
 
         # store parameters of this layer
         self.params = [self.W, self.b]
 
 
-
-def evaluate_lenet5(learning_rate=0.1, n_epochs=200, dataset='../data/mnist.pkl.gz',
-        nkerns=[20,50], batch_size = 500):
+def evaluate_lenet5(learning_rate=0.1, n_epochs=200,
+                    dataset='../data/mnist.pkl.gz',
+                    nkerns=[20, 50], batch_size=500):
     """ Demonstrates lenet on MNIST dataset
 
     :type learning_rate: float
     :param learning_rate: learning rate used (factor for the stochastic
-                          gradient) 
+                          gradient)
 
     :type n_epochs: int
     :param n_epochs: maximal number of epochs to run the optimizer
@@ -121,22 +133,23 @@ def evaluate_lenet5(learning_rate=0.1, n_epochs=200, dataset='../data/mnist.pkl.
 
     train_set_x, train_set_y = datasets[0]
     valid_set_x, valid_set_y = datasets[1]
-    test_set_x , test_set_y  = datasets[2]
-
+    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
+    n_train_batches = train_set_x.get_value(borrow=True).shape[0]
+    n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
+    n_test_batches = test_set_x.get_value(borrow=True).shape[0]
+    n_train_batches /= batch_size
+    n_valid_batches /= batch_size
+    n_test_batches /= batch_size
 
     # 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
+    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
 
-
-    ishape = (28,28)     # this is the size of MNIST images
+    ishape = (28, 28)  # this is the size of MNIST images
 
     ######################
     # BUILD ACTUAL MODEL #
@@ -145,32 +158,32 @@ def evaluate_lenet5(learning_rate=0.1, n_epochs=200, dataset='../data/mnist.pkl.
 
     # Reshape matrix of rasterized images of shape (batch_size,28*28)
     # to a 4D tensor, compatible with our LeNetConvPoolLayer
-    layer0_input = x.reshape((batch_size,1,28,28))
+    layer0_input = x.reshape((batch_size, 1, 28, 28))
 
     # Construct the first convolutional pooling layer:
     # filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
     # maxpooling reduces this further to (24/2,24/2) = (12,12)
     # 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
     layer0 = LeNetConvPoolLayer(rng, input=layer0_input,
-            image_shape=(batch_size,1,28,28), 
-            filter_shape=(nkerns[0],1,5,5), poolsize=(2,2))
+            image_shape=(batch_size, 1, 28, 28),
+            filter_shape=(nkerns[0], 1, 5, 5), poolsize=(2, 2))
 
     # Construct the second convolutional pooling layer
     # filtering reduces the image size to (12-5+1,12-5+1)=(8,8)
     # maxpooling reduces this further to (8/2,8/2) = (4,4)
     # 4D output tensor is thus of shape (nkerns[0],nkerns[1],4,4)
     layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
-            image_shape=(batch_size,nkerns[0],12,12), 
-            filter_shape=(nkerns[1],nkerns[0],5,5), poolsize=(2,2))
+            image_shape=(batch_size, nkerns[0], 12, 12),
+            filter_shape=(nkerns[1], nkerns[0], 5, 5), poolsize=(2, 2))
 
     # the TanhLayer being fully-connected, it operates on 2D matrices of
     # shape (batch_size,num_pixels) (i.e matrix of rasterized images).
     # This will generate a matrix of shape (20,32*4*4) = (20,512)
     layer2_input = layer1.output.flatten(2)
 
     # construct a fully-connected sigmoidal layer
-    layer2 = HiddenLayer(rng, input=layer2_input, n_in=nkerns[1]*4*4, 
-                         n_out=500, activation = T.tanh)
+    layer2 = HiddenLayer(rng, input=layer2_input, n_in=nkerns[1] * 4 * 4,
+                         n_out=500, activation=T.tanh)
 
     # classify the values of the fully-connected sigmoidal layer
     layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
@@ -180,113 +193,116 @@ def evaluate_lenet5(learning_rate=0.1, n_epochs=200, dataset='../data/mnist.pkl.
 
     # create a function to compute the mistakes that are made by the model
     test_model = theano.function([index], layer3.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]})
+             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([index], layer3.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]})
+            givens={
+                x: valid_set_x[index * batch_size: (index + 1) * batch_size],
+                y: valid_set_y[index * batch_size: (index + 1) * batch_size]})
 
     # create a list of all model parameters to be fit by gradient descent
-    params = layer3.params+ layer2.params+ layer1.params + layer0.params
-    
+    params = layer3.params + layer2.params + layer1.params + layer0.params
+
     # create a list of gradients for all model parameters
     grads = T.grad(cost, params)
 
-    # train_model is a function that updates the model parameters by SGD
-    # Since this model has many parameters, it would be tedious to manually
-    # create an update rule for each model parameter. We thus create the updates
-    # dictionary by automatically looping over all (params[i],grads[i])  pairs.
+    # train_model is a function that updates the model parameters by
+    # SGD Since this model has many parameters, it would be tedious to
+    # manually create an update rule for each model parameter. We thus
+    # create the updates dictionary by automatically looping over all
+    # (params[i],grads[i]) pairs.
     updates = {}
     for param_i, grad_i in zip(params, grads):
         updates[param_i] = param_i - learning_rate * grad_i
-    
-    train_model = theano.function([index], 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 = theano.function([index], 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'
     # early-stopping parameters
-    patience              = 10000 # 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
+    patience = 10000  # 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
-    best_iter            = 0
-    test_score           = 0.
+    best_iter = 0
+    test_score = 0.
     start_time = time.clock()
 
-    epoch = 0 
+    epoch = 0
     done_looping = False
 
     while (epoch < n_epochs) and (not done_looping):
-      epoch = epoch + 1
-      for minibatch_index in xrange(n_train_batches):
-        
-        iter = epoch * n_train_batches + minibatch_index
+        epoch = epoch + 1
+        for minibatch_index in xrange(n_train_batches):
 
-        if iter %100 == 0:
-            print 'training @ iter = ', iter
-        cost_ij = train_model(minibatch_index)
+            iter = epoch * n_train_batches + minibatch_index
 
-        if (iter+1) % validation_frequency == 0: 
+            if iter % 100 == 0:
+                print 'training @ iter = ', iter
+            cost_ij = train_model(minibatch_index)
 
-            # 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 (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:
+                # 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)
+                    #improve patience if loss improvement is good enough
+                    if this_validation_loss < best_validation_loss *  \
+                       improvement_threshold:
+                        patience = max(patience, iter * patience_increase)
 
-                # save best validation score and iteration number
-                best_validation_loss = this_validation_loss
-                best_iter = iter
+                    # save best validation score and iteration number
+                    best_validation_loss = this_validation_loss
+                    best_iter = iter
 
-                # 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.))
+                    # 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
+            if patience <= iter:
+                done_looping = True
+                break
 
     end_time = time.clock()
     print('Optimization complete.')
     print('Best validation score of %f %% obtained at iteration %i,'\
-          'with test performance %f %%' %  
-          (best_validation_loss * 100., best_iter, test_score*100.))
-    print >> sys.stderr, ('The code for file '+os.path.split(__file__)[1]+' ran for %.2fm' % ((end_time-start_time)/60.))
+          'with test performance %f %%' %
+          (best_validation_loss * 100., best_iter, test_score * 100.))
+    print >> sys.stderr, ('The code for file ' +
+                          os.path.split(__file__)[1] +
+                          ' ran for %.2fm' % ((end_time - start_time) / 60.))
 
 if __name__ == '__main__':
     evaluate_lenet5()
 
+
 def experiment(state, channel):
     evaluate_lenet5(state.learning_rate, dataset=state.dataset)