In [1]:

    

import numpy as np
import theano
from theano import tensor as T
theano_rng = T.shared_randomstreams.RandomStreams(1234)


    

    




Using gpu device 0: Tesla K40c (CNMeM is disabled, cuDNN 5105)
/home/ekhongl/.conda/envs/py3/lib/python3.5/site-packages/theano/sandbox/cuda/__init__.py:600: UserWarning: Your cuDNN version is more recent than the one Theano officially supports. If you see any problems, try updating Theano or downgrading cuDNN to version 5.
  warnings.warn(warn)

In [2]:

    

W_values = np.array([[1,1],[1,1]], dtype=theano.config.floatX)
bvis_values = np.array([0,0], dtype=theano.config.floatX)
bhid_values = np.array([0,0], dtype=theano.config.floatX)
W = theano.shared(W_values)
vbias = theano.shared(bvis_values)
hbias = theano.shared(bhid_values)


    

In [3]:

    

def propup(vis, v0_doc_len):
        pre_sigmoid_activation = T.dot(vis, W) + T.dot(hbias,v0_doc_len).T        #---------------------------[edited]
        return [pre_sigmoid_activation, T.nnet.sigmoid(pre_sigmoid_activation)]

def sample_h_given_v(v0_sample, v0_doc_len):
    pre_sigmoid_h1, h1_mean = propup(v0_sample, v0_doc_len)
    h1_sample = theano_rng.binomial(size=h1_mean.shape,
                                         n=1, p=h1_mean,
                                         dtype=theano.config.floatX)
    return [pre_sigmoid_h1, h1_mean, h1_sample]

def propdown(hid):
    pre_softmax_activation = T.dot(hid, W.T) + vbias                     #---------------------------[edited]
    return [pre_softmax_activation, T.nnet.softmax(pre_softmax_activation)]

def sample_v_given_h(h0_sample, v0_doc_len):
    pre_softmax_v1, v1_mean = propdown(h0_sample)
    v1_sample = theano_rng.multinomial(size=None,
                                         n=v0_doc_len, pvals=v1_mean,
                                         dtype=theano.config.floatX)     #---------------------------[edited]
    v1_doc_len = v1_sample.sum(axis=1).reshape([1,v1_sample.shape[0]])
    return [pre_softmax_v1, v1_mean, v1_sample, v1_doc_len]

def gibbs_hvh(h0_sample, v0_doc_len):
    pre_softmax_v1, v1_mean, v1_sample, v1_doc_len = sample_v_given_h(h0_sample, v0_doc_len)
    pre_sigmoid_h1, h1_mean, h1_sample = sample_h_given_v(v1_sample, v0_doc_len)
    return [pre_sigmoid_h1, h1_mean, h1_sample,
            pre_softmax_v1, v1_mean, v1_sample, v1_doc_len]                          #---------------------------[edited]

def gibbs_vhv(v0_sample, v0_doc_len):
    pre_sigmoid_h1, h1_mean, h1_sample = sample_h_given_v(v0_sample, v0_doc_len)
    pre_softmax_v1, v1_mean, v1_sample, v1_doc_len  = sample_v_given_h(h1_sample, v0_doc_len)
    return [pre_sigmoid_h1, h1_mean, h1_sample,
            softmax_v1, v1_mean, v1_sample, v1_doc_len]                              #---------------------------[edited]


    

In [4]:

    

ipt = T.matrix()
ipt_rSum = ipt.sum(axis=1).reshape([1,ipt.shape[0]])


    

In [5]:

    

pre_sigmoid_ph, ph_mean, ph_sample = sample_h_given_v(ipt, ipt_rSum)
chain_start = ph_sample


    

In [6]:

    

([pre_sigmoid_nhs,
  nh_means,
  nh_samples,
  pre_sigmoid_nvs,
  nv_means,
  nv_samples,
  nv_samples_sum],   updates) = theano.scan(
                                gibbs_hvh,
                                outputs_info=[None, None, chain_start, None, None, None, ipt_rSum],
                                n_steps=1,
                                name="gibbs_hvh" )


    

In [7]:

    

gibbs = theano.function( [ipt], outputs=[pre_sigmoid_nvs,
                                          nv_means,
                                          nv_samples,
                                          nv_samples_sum,
                                          pre_sigmoid_nhs,
                                          nh_means,
                                          nh_samples], updates=updates )


    

In [10]:

    

b = np.array([[2,0,],[0,2],[1,1]], dtype = theano.config.floatX)
gibbs(b)


    

    




---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/home/ekhongl/.conda/envs/py3/lib/python3.5/site-packages/theano/compile/function_module.py in __call__(self, *args, **kwargs)
    858         try:
--> 859             outputs = self.fn()
    860         except Exception:

ValueError: dimension mismatch in args to gemm (1,2)x(2,2)->(2,1)

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
<ipython-input-10-b4d5839210af> in <module>()
      1 b = np.array([[2,0,]], dtype = theano.config.floatX)
----> 2 gibbs(b)

/home/ekhongl/.conda/envs/py3/lib/python3.5/site-packages/theano/compile/function_module.py in __call__(self, *args, **kwargs)
    869                     node=self.fn.nodes[self.fn.position_of_error],
    870                     thunk=thunk,
--> 871                     storage_map=getattr(self.fn, 'storage_map', None))
    872             else:
    873                 # old-style linkers raise their own exceptions

/home/ekhongl/.conda/envs/py3/lib/python3.5/site-packages/theano/gof/link.py in raise_with_op(node, thunk, exc_info, storage_map)
    312         # extra long error message in that case.
    313         pass
--> 314     reraise(exc_type, exc_value, exc_trace)
    315 
    316 

/home/ekhongl/.conda/envs/py3/lib/python3.5/site-packages/six.py in reraise(tp, value, tb)
    683             value = tp()
    684         if value.__traceback__ is not tb:
--> 685             raise value.with_traceback(tb)
    686         raise value
    687 

/home/ekhongl/.conda/envs/py3/lib/python3.5/site-packages/theano/compile/function_module.py in __call__(self, *args, **kwargs)
    857         t0_fn = time.time()
    858         try:
--> 859             outputs = self.fn()
    860         except Exception:
    861             if hasattr(self.fn, 'position_of_error'):

ValueError: dimension mismatch in args to gemm (1,2)x(2,2)->(2,1)
Apply node that caused the error: GpuGemm{no_inplace}(<CudaNdarrayType(float32, matrix)>, TensorConstant{1.0}, GpuFromHost.0, GpuDimShuffle{1,0}.0, TensorConstant{1.0})
Toposort index: 26
Inputs types: [CudaNdarrayType(float32, matrix), TensorType(float32, scalar), CudaNdarrayType(float32, matrix), CudaNdarrayType(float32, matrix), TensorType(float32, scalar)]
Inputs shapes: [(2, 1), (), (1, 2), (2, 2), ()]
Inputs strides: [(1, 0), (), (0, 1), (1, 2), ()]
Inputs values: [b'CudaNdarray([[ 0.]\n [ 0.]])', array(1.0, dtype=float32), b'CudaNdarray([[ 1.  1.]])', b'CudaNdarray([[ 1.  1.]\n [ 1.  1.]])', array(1.0, dtype=float32)]
Outputs clients: [[GpuDimShuffle{0,1,x,x}(GpuGemm{no_inplace}.0), GpuDimShuffle{x,0,1}(GpuGemm{no_inplace}.0)]]

HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created. This can be done with by setting the Theano flag 'optimizer=fast_compile'. If that does not work, Theano optimizations can be disabled with 'optimizer=None'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.

In [22]:

    

b = np.array([[2,0,],[0,2],[1,1]], dtype = theano.config.floatX)
[pre_sigmoid_h1, h1_mean, h1_sample, softmax_v1, v1_doc_len, v1_mean, v1_sample]= gibbs(b)
print(pre_sigmoid_h1)
print('-------')
print(h1_mean)
print('-------')
print(h1_sample)
print('-------')
print(softmax_v1)
print('-------')
print(v1_mean)
print('-------')
print(v1_sample)


    

    




---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/home/ekhongl/.conda/envs/py3/lib/python3.5/site-packages/theano/compile/function_module.py in __call__(self, *args, **kwargs)
    858         try:
--> 859             outputs = self.fn()
    860         except Exception:

ValueError: dimension mismatch in args to gemm (3,2)x(2,2)->(2,1)

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
<ipython-input-22-1f8cc4efdce0> in <module>()
      1 b = np.array([[2,0,],[0,2],[1,1]], dtype = theano.config.floatX)
----> 2 [pre_sigmoid_h1, h1_mean, h1_sample, softmax_v1, v1_doc_len, v1_mean, v1_sample]= gibbs(b)
      3 print(pre_sigmoid_h1)
      4 print('-------')
      5 print(h1_mean)

/home/ekhongl/.conda/envs/py3/lib/python3.5/site-packages/theano/compile/function_module.py in __call__(self, *args, **kwargs)
    869                     node=self.fn.nodes[self.fn.position_of_error],
    870                     thunk=thunk,
--> 871                     storage_map=getattr(self.fn, 'storage_map', None))
    872             else:
    873                 # old-style linkers raise their own exceptions

/home/ekhongl/.conda/envs/py3/lib/python3.5/site-packages/theano/gof/link.py in raise_with_op(node, thunk, exc_info, storage_map)
    312         # extra long error message in that case.
    313         pass
--> 314     reraise(exc_type, exc_value, exc_trace)
    315 
    316 

/home/ekhongl/.conda/envs/py3/lib/python3.5/site-packages/six.py in reraise(tp, value, tb)
    683             value = tp()
    684         if value.__traceback__ is not tb:
--> 685             raise value.with_traceback(tb)
    686         raise value
    687 

/home/ekhongl/.conda/envs/py3/lib/python3.5/site-packages/theano/compile/function_module.py in __call__(self, *args, **kwargs)
    857         t0_fn = time.time()
    858         try:
--> 859             outputs = self.fn()
    860         except Exception:
    861             if hasattr(self.fn, 'position_of_error'):

ValueError: dimension mismatch in args to gemm (3,2)x(2,2)->(2,1)
Apply node that caused the error: GpuGemm{no_inplace}(<CudaNdarrayType(float32, matrix)>, TensorConstant{1.0}, GpuFromHost.0, GpuDimShuffle{1,0}.0, TensorConstant{1.0})
Toposort index: 8
Inputs types: [CudaNdarrayType(float32, matrix), TensorType(float32, scalar), CudaNdarrayType(float32, matrix), CudaNdarrayType(float32, matrix), TensorType(float32, scalar)]
Inputs shapes: [(2, 1), (), (3, 2), (2, 2), ()]
Inputs strides: [(1, 0), (), (2, 1), (1, 2), ()]
Inputs values: [b'CudaNdarray([[ 0.]\n [ 0.]])', array(1.0, dtype=float32), 'not shown', b'CudaNdarray([[ 1.  1.]\n [ 1.  1.]])', array(1.0, dtype=float32)]
Outputs clients: [[GpuDimShuffle{0,1,x,x}(GpuGemm{no_inplace}.0), GpuDimShuffle{x,0,1}(GpuGemm{no_inplace}.0)]]

HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created. This can be done with by setting the Theano flag 'optimizer=fast_compile'. If that does not work, Theano optimizations can be disabled with 'optimizer=None'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.

In [193]:

    

v1_sample.shape
h1_mean[None,:,:].shape
h1_mean.shape[1:]
h1_mean.reshape(h1_mean.shape[1:]).shape


    

    
Out[193]:





(3, 2)

In [11]:

    

W_values.sum(axis=1).reshape([1,W_values.sum(axis=1).shape[0]]).T


    

    
Out[11]:





array([[ 2.],
       [ 2.]], dtype=float32)