In [1]:

    

from pylearn2.sandbox.rnn.models.rnn import *
from librnn.pylearn2.datasets.music import MusicSequence


    

In [2]:

    

!export $PYLEARN2_DATA_PATH='/home/eders/Copy/python/data' 
dataset = MusicSequence('jsb')
testset = MusicSequence('jsb', which_set='test')


    

    




/bin/sh: 1: export: /home/eders/Copy/python/data: bad variable name

In [9]:

    

from pylearn2.models.mlp import *
from pylearn2.sandbox.rnn.space import SequenceDataSpace
from pylearn2.space import VectorSpace
input_dim=96
model = RNN(
    input_space=SequenceDataSpace(
      space=VectorSpace(
        dim=input_dim
      )
    ),
    layers= [
      GRU(
        layer_name='h_1',
        dim=48,
        irange=0.1,
        weight_noise=1
      ),
      CompositeLayer(
        layer_name='comp',
        layers=[
          Linear(
            layer_name='y1',
            dim=input_dim,
            irange=0.1
          ),
          Linear(
            layer_name='y1',
            dim=input_dim,
            irange=0.1
          ),
        ]
    )
    ]
)


    

In [10]:

    

X = model.input_space.make_theano_batch()
Y = model.fprop(X)
print X
print Y


    

    




<TensorType(float64, 3D)>
(Reshape{3}.0, Reshape{3}.0)

In [11]:

    

import theano
fy = theano.function([X], Y)


    

In [12]:

    

import numpy as np
x = np.random.normal(0,1,(100,1,96))
y = fy(x)


    

In [13]:

    

print y[0].shape
print y[1].shape


    

    




(100, 1, 96)
(100, 1, 96)

In [14]:

    

y


    

    
Out[14]:





[array([[[-0.15201113, -0.0157631 ,  0.22228196, ...,  0.12983136,
           0.2222202 ,  0.05906553]],
 
        [[ 0.00516387,  0.11326955, -0.03720764, ...,  0.37823426,
          -0.11285937,  0.10099931]],
 
        [[ 0.27068657,  0.07016972,  0.06026686, ...,  0.4153196 ,
          -0.16458814,  0.17719878]],
 
        ..., 
        [[-0.12958628, -0.11383379,  0.3514788 , ..., -0.21723426,
           0.27314417, -0.09546039]],
 
        [[ 0.3491489 ,  0.03299455,  0.0500899 , ..., -0.2055891 ,
          -0.21999117, -0.29504592]],
 
        [[ 0.23996668,  0.09593623,  0.07461963, ..., -0.10210548,
          -0.05580304,  0.07031608]]]),
 array([[[-0.00107294,  0.12255906, -0.20888688, ..., -0.02626412,
           0.0162447 , -0.16240319]],
 
        [[ 0.01438983,  0.06109638,  0.05006939, ..., -0.06176206,
           0.17274757, -0.20334945]],
 
        [[ 0.21089543,  0.24128451, -0.10958972, ..., -0.05516336,
           0.24768474, -0.22600905]],
 
        ..., 
        [[ 0.08849473, -0.13173578, -0.08885208, ...,  0.1336145 ,
          -0.17992819,  0.18571529]],
 
        [[ 0.22688313, -0.05984479, -0.31638322, ..., -0.13990211,
          -0.06200777,  0.29229198]],
 
        [[ 0.27098825,  0.03684178,  0.02502807, ...,  0.0026248 ,
          -0.20689513,  0.22405564]]])]

In [ ]: