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2 changes: 1 addition & 1 deletion code/SdA.py
Original file line number Diff line number Diff line change
Expand Up @@ -40,7 +40,7 @@

import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams

from logistic_sgd import LogisticRegression, load_data
from mlp import HiddenLayer
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2 changes: 1 addition & 1 deletion code/dA.py
Original file line number Diff line number Diff line change
Expand Up @@ -40,7 +40,7 @@

import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams

from logistic_sgd import load_data
from utils import tile_raster_images
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2 changes: 1 addition & 1 deletion code/hmc/hmc.py
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Expand Up @@ -358,7 +358,7 @@ def new_from_shared_positions(
stepsize = sharedX(initial_stepsize, 'hmc_stepsize')
avg_acceptance_rate = sharedX(target_acceptance_rate,
'avg_acceptance_rate')
s_rng = TT.shared_randomstreams.RandomStreams(seed)
s_rng = theano.sandbox.rng_mrg.MRG_RandomStreams(seed)
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AttributeError: 'module' object has no attribute 'sandbox'


# define graph for an `n_steps` HMC simulation
accept, final_pos = hmc_move(
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2 changes: 1 addition & 1 deletion code/rbm.py
Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@
import theano.tensor as T
import os

from theano.tensor.shared_randomstreams import RandomStreams
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams

from utils import tile_raster_images
from logistic_sgd import load_data
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2 changes: 1 addition & 1 deletion code/rnnrbm.py
Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@
from midi.utils import midiread, midiwrite
import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams

#Don't use a python long as this don't work on 32 bits computers.
numpy.random.seed(0xbeef)
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