In [2]:
# https://gist.github.com/arngarden/6087798
import theano
from pylearn2.models import mlp
from pylearn2.training_algorithms import sgd
from pylearn2.termination_criteria import EpochCounter
from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix
import numpy as np
from random import randint
In [3]:
class XOR(DenseDesignMatrix):
def __init__(self):
self.class_names = ['0', '1']
X = [[randint(0, 1), randint(0, 1)] for _ in range(1000)]
y = []
for a, b in X:
if a + b == 1:
y.append([0, 1])
else:
y.append([1, 0])
X = np.array(X)
y = np.array(y)
super(XOR, self).__init__(X=X, y=y)
In [4]:
# create XOR dataset
ds = XOR()
In [5]:
print ds.X.shape
print ds.y.shape
(1000, 2)
(1000, 2)
In [6]:
print ds.X[0]
print ds.y[0]
[1 0]
[0 1]
In [5]:
# create hidden layer with 2 nodes, init weights in range -0.1 to 0.1 and add
# a bias with value 1
hidden_layer = mlp.Sigmoid(layer_name='hidden', dim=2, irange=.1, init_bias=1.)
In [6]:
# create Softmax output layer
output_layer = mlp.Softmax(2, 'output', irange=.1)
In [7]:
# create Stochastic Gradient Descent trainer that runs for 400 epochs
trainer = sgd.SGD(learning_rate=.05, batch_size=10, termination_criterion=EpochCounter(400))
In [8]:
layers = [hidden_layer, output_layer]
In [9]:
# create neural net that takes two inputs
ann = mlp.MLP(layers, nvis=2)
trainer.setup(ann, ds)
Parameter and initial learning rate summary:
hidden_W: 0.05
hidden_b: 0.05
softmax_b: 0.05
softmax_W: 0.05
Compiling sgd_update...
Compiling sgd_update done. Time elapsed: 1.533082 seconds
In [10]:
# train neural net until the termination criterion is true
while True:
trainer.train(dataset=ds)
ann.monitor.report_epoch()
ann.monitor()
if not trainer.continue_learning(ann):
break
compiling begin_record_entry...
compiling begin_record_entry done. Time elapsed: 0.013912 seconds
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In [11]:
inputs = np.array([[0, 0]])
print ann.fprop(theano.shared(inputs, name='inputs')).eval()
inputs = np.array([[0, 1]])
print ann.fprop(theano.shared(inputs, name='inputs')).eval()
inputs = np.array([[1, 0]])
print ann.fprop(theano.shared(inputs, name='inputs')).eval()
inputs = np.array([[1, 1]])
print ann.fprop(theano.shared(inputs, name='inputs')).eval()
[[ 0.96310425 0.03689575]]
[[ 0.04224482 0.95775518]]
[[ 0.04463144 0.95536856]]
[[ 0.97087054 0.02912946]]
In [12]:
ann.fprop
Out[12]:
<bound method MLP.fprop of <pylearn2.models.mlp.MLP object at 0x1042d3610>>
Content source: dikien/personnel-study
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