Mercurial > ift6266

annotate baseline/mlp/mlp_nist.py @ 169:d37c944133c3

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
directory name change
author Dumitru Erhan <dumitru.erhan@gmail.com>
date Fri, 26 Feb 2010 14:24:11 -0500
parents baseline_algorithms/mlp/mlp_nist.py@8ceaaf812891
children e390b0454515
rev   line source
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
1 """
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
2 This tutorial introduces the multilayer perceptron using Theano.
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
3
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
4 A multilayer perceptron is a logistic regressor where
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
5 instead of feeding the input to the logistic regression you insert a
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
6 intermidiate layer, called the hidden layer, that has a nonlinear
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
7 activation function (usually tanh or sigmoid) . One can use many such
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
8 hidden layers making the architecture deep. The tutorial will also tackle
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
9 the problem of MNIST digit classification.
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
10
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
11 .. math::
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
12
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
13 f(x) = G( b^{(2)} + W^{(2)}( s( b^{(1)} + W^{(1)} x))),
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
14
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
15 References:
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
16
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
17 - textbooks: "Pattern Recognition and Machine Learning" -
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
18 Christopher M. Bishop, section 5
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
19
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
20 TODO: recommended preprocessing, lr ranges, regularization ranges (explain
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
21 to do lr first, then add regularization)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
22
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
23 """
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
24 __docformat__ = 'restructedtext en'
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
25
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
26 import pdb
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
27 import numpy
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
28 import pylab
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
29 import theano
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
30 import theano.tensor as T
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
31 import time
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
32 import theano.tensor.nnet
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
33 import pylearn
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
34 from pylearn.io import filetensor as ft
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
35
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
36 data_path = '/data/lisa/data/nist/by_class/'
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
37
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
38 class MLP(object):
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
39 """Multi-Layer Perceptron Class
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
40
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
41 A multilayer perceptron is a feedforward artificial neural network model
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
42 that has one layer or more of hidden units and nonlinear activations.
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
43 Intermidiate layers usually have as activation function thanh or the
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
44 sigmoid function while the top layer is a softamx layer.
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
45 """
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
46
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
47
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
48
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
49 def __init__(self, input, n_in, n_hidden, n_out,learning_rate):
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
50 """Initialize the parameters for the multilayer perceptron
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
51
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
52 :param input: symbolic variable that describes the input of the
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
53 architecture (one minibatch)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
54
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
55 :param n_in: number of input units, the dimension of the space in
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
56 which the datapoints lie
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
57
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
58 :param n_hidden: number of hidden units
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
59
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
60 :param n_out: number of output units, the dimension of the space in
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
61 which the labels lie
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
62
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
63 """
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
64
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
65 # initialize the parameters theta = (W1,b1,W2,b2) ; note that this
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
66 # example contains only one hidden layer, but one can have as many
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
67 # layers as he/she wishes, making the network deeper. The only
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
68 # problem making the network deep this way is during learning,
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
69 # backpropagation being unable to move the network from the starting
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
70 # point towards; this is where pre-training helps, giving a good
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
71 # starting point for backpropagation, but more about this in the
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
72 # other tutorials
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
73
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
74 # `W1` is initialized with `W1_values` which is uniformely sampled
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
75 # from -6./sqrt(n_in+n_hidden) and 6./sqrt(n_in+n_hidden)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
76 # the output of uniform if converted using asarray to dtype
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
77 # theano.config.floatX so that the code is runable on GPU
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
78 W1_values = numpy.asarray( numpy.random.uniform( \
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
79 low = -numpy.sqrt(6./(n_in+n_hidden)), \
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
80 high = numpy.sqrt(6./(n_in+n_hidden)), \
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
81 size = (n_in, n_hidden)), dtype = theano.config.floatX)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
82 # `W2` is initialized with `W2_values` which is uniformely sampled
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
83 # from -6./sqrt(n_hidden+n_out) and 6./sqrt(n_hidden+n_out)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
84 # the output of uniform if converted using asarray to dtype
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
85 # theano.config.floatX so that the code is runable on GPU
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
86 W2_values = numpy.asarray( numpy.random.uniform(
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
87 low = -numpy.sqrt(6./(n_hidden+n_out)), \
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
88 high= numpy.sqrt(6./(n_hidden+n_out)),\
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
89 size= (n_hidden, n_out)), dtype = theano.config.floatX)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
90
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
91 self.W1 = theano.shared( value = W1_values )
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
92 self.b1 = theano.shared( value = numpy.zeros((n_hidden,),
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
93 dtype= theano.config.floatX))
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
94 self.W2 = theano.shared( value = W2_values )
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
95 self.b2 = theano.shared( value = numpy.zeros((n_out,),
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
96 dtype= theano.config.floatX))
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
97
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
98 #include the learning rate in the classifer so
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
99 #we can modify it on the fly when we want
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
100 lr_value=learning_rate
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
101 self.lr=theano.shared(value=lr_value)
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
102 # symbolic expression computing the values of the hidden layer
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
103 self.hidden = T.tanh(T.dot(input, self.W1)+ self.b1)
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
104
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
105
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
106
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
107 # symbolic expression computing the values of the top layer
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
108 self.p_y_given_x= T.nnet.softmax(T.dot(self.hidden, self.W2)+self.b2)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
109
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
110 # compute prediction as class whose probability is maximal in
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
111 # symbolic form
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
112 self.y_pred = T.argmax( self.p_y_given_x, axis =1)
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
113 self.y_pred_num = T.argmax( self.p_y_given_x[0:9], axis =1)
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
114
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
115
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
116
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
117
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
118 # L1 norm ; one regularization option is to enforce L1 norm to
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
119 # be small
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
120 self.L1 = abs(self.W1).sum() + abs(self.W2).sum()
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
121
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
122 # square of L2 norm ; one regularization option is to enforce
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
123 # square of L2 norm to be small
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
124 self.L2_sqr = (self.W1**2).sum() + (self.W2**2).sum()
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
125
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
126
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
127
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
128 def negative_log_likelihood(self, y):
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
129 """Return the mean of the negative log-likelihood of the prediction
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
130 of this model under a given target distribution.
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
131
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
132 .. math::
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
133
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
134 \frac{1}{|\mathcal{D}|}\mathcal{L} (\theta=\{W,b\}, \mathcal{D}) =
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
135 \frac{1}{|\mathcal{D}|}\sum_{i=0}^{|\mathcal{D}|} \log(P(Y=y^{(i)}|x^{(i)}, W,b)) \\
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
136 \ell (\theta=\{W,b\}, \mathcal{D})
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
137
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
138
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
139 :param y: corresponds to a vector that gives for each example the
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
140 :correct label
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
141 """
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
142 return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]),y])
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
143
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
144
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
145
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
146
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
147 def errors(self, y):
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
148 """Return a float representing the number of errors in the minibatch
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
149 over the total number of examples of the minibatch
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
150 """
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
151
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
152 # check if y has same dimension of y_pred
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
153 if y.ndim != self.y_pred.ndim:
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
154 raise TypeError('y should have the same shape as self.y_pred',
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
155 ('y', target.type, 'y_pred', self.y_pred.type))
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
156 # check if y is of the correct datatype
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
157 if y.dtype.startswith('int'):
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
158 # the T.neq operator returns a vector of 0s and 1s, where 1
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
159 # represents a mistake in prediction
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
160 return T.mean(T.neq(self.y_pred, y))
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
161 else:
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
162 raise NotImplementedError()
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
163
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
164
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
165 def mlp_full_nist( verbose = False,\
145
8ceaaf812891 changed adaptive lr flag from bool to int for jobman issues
XavierMuller
parents: 143
diff changeset
166 adaptive_lr = 0,\
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
167 train_data = 'all/all_train_data.ft',\
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
168 train_labels = 'all/all_train_labels.ft',\
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
169 test_data = 'all/all_test_data.ft',\
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
170 test_labels = 'all/all_test_labels.ft',\
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
171 learning_rate=0.01,\
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
172 L1_reg = 0.00,\
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
173 L2_reg = 0.0001,\
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
174 nb_max_exemples=1000000,\
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
175 batch_size=20,\
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
176 nb_hidden = 500,\
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
177 nb_targets = 62):
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
178
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
179
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
180 configuration = [learning_rate,nb_max_exemples,nb_hidden,adaptive_lr]
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
181
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
182 total_validation_error_list = []
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
183 total_train_error_list = []
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
184 learning_rate_list=[]
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
185 best_training_error=float('inf');
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
186
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
187
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
188
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
189 f = open(data_path+train_data)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
190 g= open(data_path+train_labels)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
191 h = open(data_path+test_data)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
192 i= open(data_path+test_labels)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
193
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
194 raw_train_data = ft.read(f)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
195 raw_train_labels = ft.read(g)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
196 raw_test_data = ft.read(h)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
197 raw_test_labels = ft.read(i)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
198
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
199 f.close()
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
200 g.close()
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
201 i.close()
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
202 h.close()
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
203 #create a validation set the same size as the test size
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
204 #use the end of the training array for this purpose
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
205 #discard the last remaining so we get a %batch_size number
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
206 test_size=len(raw_test_labels)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
207 test_size = int(test_size/batch_size)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
208 test_size*=batch_size
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
209 train_size = len(raw_train_data)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
210 train_size = int(train_size/batch_size)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
211 train_size*=batch_size
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
212 validation_size =test_size
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
213 offset = train_size-test_size
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
214 if verbose == True:
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
215 print 'train size = %d' %train_size
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
216 print 'test size = %d' %test_size
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
217 print 'valid size = %d' %validation_size
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
218 print 'offset = %d' %offset
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
219
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
220
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
221 train_set = (raw_train_data,raw_train_labels)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
222 train_batches = []
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
223 for i in xrange(0, train_size-test_size, batch_size):
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
224 train_batches = train_batches + \
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
225 [(raw_train_data[i:i+batch_size], raw_train_labels[i:i+batch_size])]
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
226
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
227 test_batches = []
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
228 for i in xrange(0, test_size, batch_size):
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
229 test_batches = test_batches + \
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
230 [(raw_test_data[i:i+batch_size], raw_test_labels[i:i+batch_size])]
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
231
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
232 validation_batches = []
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
233 for i in xrange(0, test_size, batch_size):
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
234 validation_batches = validation_batches + \
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
235 [(raw_train_data[offset+i:offset+i+batch_size], raw_train_labels[offset+i:offset+i+batch_size])]
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
236
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
237
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
238 ishape = (32,32) # this is the size of NIST images
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
239
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
240 # allocate symbolic variables for the data
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
241 x = T.fmatrix() # the data is presented as rasterized images
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
242 y = T.lvector() # the labels are presented as 1D vector of
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
243 # [long int] labels
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
244
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
245 if verbose==True:
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
246 print 'finished parsing the data'
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
247 # construct the logistic regression class
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
248 classifier = MLP( input=x.reshape((batch_size,32*32)),\
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
249 n_in=32*32,\
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
250 n_hidden=nb_hidden,\
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
251 n_out=nb_targets,
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
252 learning_rate=learning_rate)
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
253
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
254
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
255
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
256
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
257 # the cost we minimize during training is the negative log likelihood of
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
258 # the model plus the regularization terms (L1 and L2); cost is expressed
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
259 # here symbolically
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
260 cost = classifier.negative_log_likelihood(y) \
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
261 + L1_reg * classifier.L1 \
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
262 + L2_reg * classifier.L2_sqr
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
263
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
264 # compiling a theano function that computes the mistakes that are made by
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
265 # the model on a minibatch
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
266 test_model = theano.function([x,y], classifier.errors(y))
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
267
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
268 # compute the gradient of cost with respect to theta = (W1, b1, W2, b2)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
269 g_W1 = T.grad(cost, classifier.W1)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
270 g_b1 = T.grad(cost, classifier.b1)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
271 g_W2 = T.grad(cost, classifier.W2)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
272 g_b2 = T.grad(cost, classifier.b2)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
273
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
274 # specify how to update the parameters of the model as a dictionary
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
275 updates = \
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
276 { classifier.W1: classifier.W1 - classifier.lr*g_W1 \
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
277 , classifier.b1: classifier.b1 - classifier.lr*g_b1 \
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
278 , classifier.W2: classifier.W2 - classifier.lr*g_W2 \
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
279 , classifier.b2: classifier.b2 - classifier.lr*g_b2 }
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
280
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
281 # compiling a theano function `train_model` that returns the cost, but in
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
282 # the same time updates the parameter of the model based on the rules
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
283 # defined in `updates`
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
284 train_model = theano.function([x, y], cost, updates = updates )
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
285 n_minibatches = len(train_batches)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
286
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
287
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
288
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
289
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
290
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
291
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
292 #conditions for stopping the adaptation:
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
293 #1) we have reached nb_max_exemples (this is rounded up to be a multiple of the train size)
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
294 #2) validation error is going up twice in a row(probable overfitting)
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
295
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
296 # This means we no longer stop on slow convergence as low learning rates stopped
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
297 # too fast.
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
298
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
299 # no longer relevant
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
300 patience =nb_max_exemples/batch_size
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
301 patience_increase = 2 # wait this much longer when a new best is
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
302 # found
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
303 improvement_threshold = 0.995 # a relative improvement of this much is
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
304 # considered significant
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
305 validation_frequency = n_minibatches/4
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
306
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
307
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
308
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
309
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
310 best_params = None
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
311 best_validation_loss = float('inf')
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
312 best_iter = 0
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
313 test_score = 0.
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
314 start_time = time.clock()
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
315 n_iter = nb_max_exemples/batch_size # nb of max times we are allowed to run through all exemples
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
316 n_iter = n_iter/n_minibatches + 1 #round up
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
317 n_iter=max(1,n_iter) # run at least once on short debug call
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
318
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
319
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
320 if verbose == True:
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
321 print 'looping at most %d times through the data set' %n_iter
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
322 for iter in xrange(n_iter* n_minibatches):
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
323
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
324 # get epoch and minibatch index
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
325 epoch = iter / n_minibatches
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
326 minibatch_index = iter % n_minibatches
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
327
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
328
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
329
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
330 # get the minibatches corresponding to `iter` modulo
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
331 # `len(train_batches)`
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
332 x,y = train_batches[ minibatch_index ]
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
333 # convert to float
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
334 x_float = x/255.0
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
335 cost_ij = train_model(x_float,y)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
336
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
337 if (iter+1) % validation_frequency == 0:
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
338 # compute zero-one loss on validation set
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
339
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
340 this_validation_loss = 0.
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
341 for x,y in validation_batches:
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
342 # sum up the errors for each minibatch
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
343 x_float = x/255.0
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
344 this_validation_loss += test_model(x_float,y)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
345 # get the average by dividing with the number of minibatches
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
346 this_validation_loss /= len(validation_batches)
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
347 #save the validation loss
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
348 total_validation_error_list.append(this_validation_loss)
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
349
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
350 #get the training error rate
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
351 this_train_loss=0
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
352 for x,y in train_batches:
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
353 # sum up the errors for each minibatch
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
354 x_float = x/255.0
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
355 this_train_loss += test_model(x_float,y)
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
356 # get the average by dividing with the number of minibatches
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
357 this_train_loss /= len(train_batches)
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
358 #save the validation loss
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
359 total_train_error_list.append(this_train_loss)
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
360 if(this_train_loss<best_training_error):
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
361 best_training_error=this_train_loss
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
362
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
363 if verbose == True:
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
364 print('epoch %i, minibatch %i/%i, validation error %f, training error %f %%' % \
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
365 (epoch, minibatch_index+1, n_minibatches, \
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
366 this_validation_loss*100.,this_train_loss*100))
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
367
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
368
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
369 #save the learning rate
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
370 learning_rate_list.append(classifier.lr.value)
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
371
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
372
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
373 # if we got the best validation score until now
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
374 if this_validation_loss < best_validation_loss:
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
375 # save best validation score and iteration number
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
376 best_validation_loss = this_validation_loss
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
377 best_iter = iter
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
378 # reset patience if we are going down again
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
379 # so we continue exploring
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
380 patience=nb_max_exemples/batch_size
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
381 # test it on the test set
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
382 test_score = 0.
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
383 for x,y in test_batches:
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
384 x_float=x/255.0
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
385 test_score += test_model(x_float,y)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
386 test_score /= len(test_batches)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
387 if verbose == True:
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
388 print((' epoch %i, minibatch %i/%i, test error of best '
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
389 'model %f %%') %
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
390 (epoch, minibatch_index+1, n_minibatches,
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
391 test_score*100.))
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
392
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
393 # if the validation error is going up, we are overfitting (or oscillating)
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
394 # stop converging but run at least to next validation
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
395 # to check overfitting or ocsillation
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
396 # the saved weights of the model will be a bit off in that case
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
397 elif this_validation_loss >= best_validation_loss:
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
398 #calculate the test error at this point and exit
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
399 # test it on the test set
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
400 # however, if adaptive_lr is true, try reducing the lr to
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
401 # get us out of an oscilliation
145
8ceaaf812891 changed adaptive lr flag from bool to int for jobman issues
XavierMuller
parents: 143
diff changeset
402 if adaptive_lr==1:
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
403 classifier.lr.value=classifier.lr.value/2.0
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
404
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
405 test_score = 0.
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
406 #cap the patience so we are allowed one more validation error
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
407 #calculation before aborting
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
408 patience = iter+validation_frequency+1
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
409 for x,y in test_batches:
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
410 x_float=x/255.0
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
411 test_score += test_model(x_float,y)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
412 test_score /= len(test_batches)
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
413 if verbose == True:
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
414 print ' validation error is going up, possibly stopping soon'
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
415 print((' epoch %i, minibatch %i/%i, test error of best '
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
416 'model %f %%') %
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
417 (epoch, minibatch_index+1, n_minibatches,
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
418 test_score*100.))
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
419
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
420
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
421
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
422
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
423 if iter>patience:
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
424 print 'we have diverged'
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
425 break
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
426
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
427
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
428 end_time = time.clock()
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
429 if verbose == True:
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
430 print(('Optimization complete. Best validation score of %f %% '
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
431 'obtained at iteration %i, with test performance %f %%') %
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
432 (best_validation_loss * 100., best_iter, test_score*100.))
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
433 print ('The code ran for %f minutes' % ((end_time-start_time)/60.))
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
434 print iter
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
435
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
436 #save the model and the weights
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
437 numpy.savez('model.npy', config=configuration, W1=classifier.W1.value,W2=classifier.W2.value, b1=classifier.b1.value,b2=classifier.b2.value)
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
438 numpy.savez('results.npy',config=configuration,total_train_error_list=total_train_error_list,total_validation_error_list=total_validation_error_list,\
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
439 learning_rate_list=learning_rate_list)
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
440
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
441 return (best_training_error*100.0,best_validation_loss * 100.,test_score*100.,best_iter*batch_size,(end_time-start_time)/60)
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
442
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
443
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
444 if __name__ == '__main__':
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
445 mlp_full_mnist()
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
446
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
447 def jobman_mlp_full_nist(state,channel):
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
448 (train_error,validation_error,test_error,nb_exemples,time)=mlp_full_nist(learning_rate=state.learning_rate,\
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
449 nb_max_exemples=state.nb_max_exemples,\
143
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
450 nb_hidden=state.nb_hidden,\
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
451 adaptive_lr=state.adaptive_lr)
f341a4efb44a added adaptive lr, weight file save, traine error and error curves
XavierMuller
parents: 110
diff changeset
452 state.train_error=train_error
110
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
453 state.validation_error=validation_error
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
454 state.test_error=test_error
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
455 state.nb_exemples=nb_exemples
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
456 state.time=time
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
457 return channel.COMPLETE
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
458
93b4b84d86cf added simple mlp file
XavierMuller
parents:
diff changeset
459