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In [1]:



    


require 'cunn'; require 'nn'; require 'torch'; require 'image';



    











In [2]:



    


model = torch.load('result4/model.dat')



    











In [4]:



    


m = model:get(2)



    











In [12]:



    


data = torch.load('mnist.t7/test_32x32.t7', 'ascii')



    











In [54]:



    


model:evaluate()
prediction = model:forward(data['data'][1])
val,output = torch.max(prediction,1)
print(output)



    


















    
Out[54]:








 8
[torch.LongTensor of size 1]



















In [55]:



    


target = data['labels'][1]
print(target)



    


















    
Out[55]:








8	


















In [60]:



    


ind = 1
for t = 1,10 do
    while data['labels'][ind] ~= t do
        ind = ind + 1
    end
    model:evaluate()
    model:forward(data['data'][ind])

    for i = 1,m:size() do
        print('Layer ' .. i)
        print(m:get(i))
        print('Weights:')
        if not pcall(function () itorch.image(m:get(i).weight) end) then print("Can't display") end
        print('Output:')
        if not pcall(function () itorch.image(m:get(i).output) end) then print("Can't display") end
        print()
    end
end



    


















    
Out[60]:








Layer 1	
nn.SpatialConvolutionMM(1 -> 32, 5x5)
{
  padW : 0
  nInputPlane : 1
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x1x32x32
  gradBias : CudaTensor - size: 32
  dW : 1
  nOutputPlane : 32
  bias : CudaTensor - size: 32
  kH : 5
  finput : CudaTensor - size: 25x784
  weight : CudaTensor - size: 32x25
  train : false
  gradWeight : CudaTensor - size: 32x25
  fgradInput : CudaTensor - size: 28x28
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    

















    
Out[60]:








Output:	











    

















    
Out[60]:








Layer 2	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x32x28x28
  train : false
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 3	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x32x28x28
  indices : CudaTensor - size: 1x32x14x14
  train : false
  kW : 2
  ceil_mode : false
  padW : 0
  output : CudaTensor - size: 32x14x14
  kH : 2
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 4	
nn.SpatialConvolutionMM(32 -> 128, 5x5)
{
  padW : 0
  nInputPlane : 32
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x32x14x14
  gradBias : CudaTensor - size: 128
  dW : 1
  nOutputPlane : 128
  bias : CudaTensor - size: 128










    
Out[60]:








  kH : 5
  finput : CudaTensor - size: 800x100
  weight : CudaTensor - size: 128x800
  train : false
  gradWeight : CudaTensor - size: 128x800
  fgradInput : CudaTensor - size: 10x10
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    

















    
Out[60]:








Output:	











    

















    
Out[60]:








Layer 5	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x128x10x10
  train : false
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 6	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x128x10x10
  indices : CudaTensor - size: 1x128x5x5
  train : false
  kW : 2










    
Out[60]:








  ceil_mode : false
  padW : 0
  output : CudaTensor - size: 128x5x5
  kH : 2
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 7	
nn.Reshape(3200)
{
  size : LongStorage - size: 1
  batchsize : LongStorage - size: 2
  train : false
  output : CudaTensor - size: 3200
  gradInput : CudaTensor - size: 9x128x5x5
  nelement : 3200











    
Out[60]:








  _gradOutput : CudaTensor - empty
  _input : CudaTensor - empty
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 8	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x3200
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 3200
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 9	
nn.Linear(3200 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x3200
  train : false
  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x3200
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	
Can't display	

Layer 10	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 11	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	











    
Out[60]:








Can't display	

Layer 12	
nn.Linear(256 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x256
  train : false
  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    
Out[60]:








Output:	











    

















    
Out[60]:








Can't display	

Layer 13	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false











    
Out[60]:








  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 14	
nn.Dropout(0.500000)
{











    
Out[60]:








  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 15	
nn.Linear(256 -> 10)
{
  gradBias : CudaTensor - size: 10
  weight : CudaTensor - size: 10x256
  train : false
  bias : CudaTensor - size: 10
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 10
  gradWeight : CudaTensor - size: 10x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	











    
Out[60]:








Can't display	

Layer 16	
nn.SoftMax
{
  gradInput : CudaTensor - size: 9x10
  train : false
  output : CudaTensor - size: 10
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 1	
nn.SpatialConvolutionMM(1 -> 32, 5x5)
{
  padW : 0
  nInputPlane : 1
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x1x32x32
  gradBias : CudaTensor - size: 32
  dW : 1
  nOutputPlane : 32
  bias : CudaTensor - size: 32
  kH : 5
  finput : CudaTensor - size: 25x784
  weight : CudaTensor - size: 32x25
  train : false
  gradWeight : CudaTensor - size: 32x25
  fgradInput : CudaTensor - size: 28x28
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    

















    
Out[60]:








Output:	











    

















    
Out[60]:








Layer 2	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x32x28x28
  train : false
}
Weights:	











    
Out[60]:








Can't display	
Output:	











    

















    
Out[60]:








Layer 3	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x32x28x28
  indices : CudaTensor - size: 1x32x14x14
  train : false
  kW : 2
  ceil_mode : false
  padW : 0
  output : CudaTensor - size: 32x14x14
  kH : 2
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 4	
nn.SpatialConvolutionMM(32 -> 128, 5x5)
{
  padW : 0
  nInputPlane : 32
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x32x14x14
  gradBias : CudaTensor - size: 128
  dW : 1
  nOutputPlane : 128
  bias : CudaTensor - size: 128
  kH : 5
  finput : CudaTensor - size: 800x100











    
Out[60]:








  weight : CudaTensor - size: 128x800
  train : false
  gradWeight : CudaTensor - size: 128x800
  fgradInput : CudaTensor - size: 10x10
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    

















    
Out[60]:








Output:	











    

















    
Out[60]:








Layer 5	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x128x10x10
  train : false
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 6	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x128x10x10
  indices : CudaTensor - size: 1x128x5x5
  train : false
  kW : 2
  ceil_mode : false
  padW : 0
  output : CudaTensor - size: 128x5x5











    
Out[60]:








  kH : 2
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 7	
nn.Reshape(3200)
{
  size : LongStorage - size: 1
  batchsize : LongStorage - size: 2
  train : false
  output : CudaTensor - size: 3200
  gradInput : CudaTensor - size: 9x128x5x5
  nelement : 3200
  _gradOutput : CudaTensor - empty
  _input : CudaTensor - empty
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 8	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x3200
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 3200
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 9	
nn.Linear(3200 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x3200
  train : false
  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x3200
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	
Can't display	

Layer 10	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 11	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 12	
nn.Linear(256 -> 256)
{











    
Out[60]:








  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x256
  train : false
  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	











    
Out[60]:








Can't display	

Layer 13	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	











    
Out[60]:








Can't display	

Layer 14	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	











    
Out[60]:








Can't display	

Layer 15	
nn.Linear(256 -> 10)
{
  gradBias : CudaTensor - size: 10
  weight : CudaTensor - size: 10x256
  train : false
  bias : CudaTensor - size: 10
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 10
  gradWeight : CudaTensor - size: 10x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	











    
Out[60]:








Can't display	

Layer 16	
nn.SoftMax
{
  gradInput : CudaTensor - size: 9x10
  train : false
  output : CudaTensor - size: 10
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 1	
nn.SpatialConvolutionMM(1 -> 32, 5x5)
{
  padW : 0
  nInputPlane : 1
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x1x32x32











    
Out[60]:








  gradBias : CudaTensor - size: 32
  dW : 1
  nOutputPlane : 32
  bias : CudaTensor - size: 32
  kH : 5
  finput : CudaTensor - size: 25x784
  weight : CudaTensor - size: 32x25
  train : false
  gradWeight : CudaTensor - size: 32x25
  fgradInput : CudaTensor - size: 28x28
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    

















    
Out[60]:








Output:	











    
Out[60]:








Layer 2	











    

















    
Out[60]:








nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x32x28x28
  train : false
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 3	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x32x28x28
  indices : CudaTensor - size: 1x32x14x14
  train : false
  kW : 2
  ceil_mode : false
  padW : 0
  output : CudaTensor - size: 32x14x14
  kH : 2
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 4	
nn.SpatialConvolutionMM(32 -> 128, 5x5)
{
  padW : 0
  nInputPlane : 32
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x32x14x14
  gradBias : CudaTensor - size: 128
  dW : 1
  nOutputPlane : 128
  bias : CudaTensor - size: 128
  kH : 5
  finput : CudaTensor - size: 800x100
  weight : CudaTensor - size: 128x800
  train : false
  gradWeight : CudaTensor - size: 128x800
  fgradInput : CudaTensor - size: 10x10
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    

















    
Out[60]:








Output:	











    

















    
Out[60]:








Layer 5	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x128x10x10
  train : false
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 6	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x128x10x10
  indices : CudaTensor - size: 1x128x5x5
  train : false
  kW : 2
  ceil_mode : false
  padW : 0
  output : CudaTensor - size: 128x5x5
  kH : 2
}
Weights:	











    
Out[60]:








Can't display	
Output:	











    

















    
Out[60]:








Layer 7	
nn.Reshape(3200)
{
  size : LongStorage - size: 1
  batchsize : LongStorage - size: 2
  train : false
  output : CudaTensor - size: 3200
  gradInput : CudaTensor - size: 9x128x5x5
  nelement : 3200
  _gradOutput : CudaTensor - empty
  _input : CudaTensor - empty
}
Weights:	











    
Out[60]:








Can't display	
Output:	
Can't display	

Layer 8	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x3200
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 3200
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 9	
nn.Linear(3200 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x3200
  train : false
  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x3200
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	
Can't display	

Layer 10	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 11	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 12	
nn.Linear(256 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x256
  train : false
  bias : CudaTensor - size: 256











    
Out[60]:








  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	











    
Out[60]:








Can't display	

Layer 13	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false
  output : CudaTensor - size: 256
}
Weights:	
Can't display	











    
Out[60]:








Output:	
Can't display	

Layer 14	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	











    
Out[60]:








Can't display	

Layer 15	
nn.Linear(256 -> 10)
{
  gradBias : CudaTensor - size: 10
  weight : CudaTensor - size: 10x256
  train : false
  bias : CudaTensor - size: 10
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 10
  gradWeight : CudaTensor - size: 10x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	











    
Out[60]:








Can't display	

Layer 16	
nn.SoftMax
{
  gradInput : CudaTensor - size: 9x10
  train : false
  output : CudaTensor - size: 10
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 1	
nn.SpatialConvolutionMM(1 -> 32, 5x5)
{
  padW : 0
  nInputPlane : 1
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x1x32x32
  gradBias : CudaTensor - size: 32
  dW : 1
  nOutputPlane : 32
  bias : CudaTensor - size: 32
  kH : 5
  finput : CudaTensor - size: 25x784
  weight : CudaTensor - size: 32x25
  train : false
  gradWeight : CudaTensor - size: 32x25
  fgradInput : CudaTensor - size: 28x28
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    

















    
Out[60]:








Output:	











    

















    
Out[60]:








Layer 2	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x32x28x28
  train : false
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 3	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x32x28x28
  indices : CudaTensor - size: 1x32x14x14
  train : false
  kW : 2
  ceil_mode : false
  padW : 0
  output : CudaTensor - size: 32x14x14
  kH : 2
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 4	
nn.SpatialConvolutionMM(32 -> 128, 5x5)
{
  padW : 0
  nInputPlane : 32
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x32x14x14
  gradBias : CudaTensor - size: 128
  dW : 1
  nOutputPlane : 128
  bias : CudaTensor - size: 128
  kH : 5
  finput : CudaTensor - size: 800x100
  weight : CudaTensor - size: 128x800
  train : false
  gradWeight : CudaTensor - size: 128x800
  fgradInput : CudaTensor - size: 10x10
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    

















    
Out[60]:








Output:	











    

















    
Out[60]:








Layer 5	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x128x10x10
  train : false
}
Weights:	











    
Out[60]:








Can't display	
Output:	











    

















    
Out[60]:








Layer 6	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x128x10x10
  indices : CudaTensor - size: 1x128x5x5
  train : false
  kW : 2
  ceil_mode : false
  padW : 0
  output : CudaTensor - size: 128x5x5
  kH : 2
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 7	
nn.Reshape(3200)
{
  size : LongStorage - size: 1
  batchsize : LongStorage - size: 2
  train : false
  output : CudaTensor - size: 3200
  gradInput : CudaTensor - size: 9x128x5x5
  nelement : 3200
  _gradOutput : CudaTensor - empty
  _input : CudaTensor - empty
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 8	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x3200
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 3200
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 9	
nn.Linear(3200 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x3200
  train : false
  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x3200
  addBuffer : CudaTensor - size: 9
}
Weights:	











    
Out[60]:








Output:	











    

















    
Out[60]:








Can't display	

Layer 10	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 11	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	











    
Out[60]:








Can't display	

Layer 12	
nn.Linear(256 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x256
  train : false
  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	
Can't display	

Layer 13	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false
  output : CudaTensor - size: 256
}











    
Out[60]:








Weights:	
Can't display	
Output:	
Can't display	

Layer 14	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	











    
Out[60]:








Can't display	

Layer 15	
nn.Linear(256 -> 10)
{
  gradBias : CudaTensor - size: 10
  weight : CudaTensor - size: 10x256
  train : false
  bias : CudaTensor - size: 10
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 10
  gradWeight : CudaTensor - size: 10x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	











    
Out[60]:








Can't display	

Layer 16	
nn.SoftMax
{
  gradInput : CudaTensor - size: 9x10
  train : false
  output : CudaTensor - size: 10
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 1	
nn.SpatialConvolutionMM(1 -> 32, 5x5)
{
  padW : 0
  nInputPlane : 1
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x1x32x32
  gradBias : CudaTensor - size: 32
  dW : 1
  nOutputPlane : 32
  bias : CudaTensor - size: 32
  kH : 5
  finput : CudaTensor - size: 25x784
  weight : CudaTensor - size: 32x25
  train : false
  gradWeight : CudaTensor - size: 32x25
  fgradInput : CudaTensor - size: 28x28
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    

















    
Out[60]:








Output:	











    

















    
Out[60]:








Layer 2	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x32x28x28
  train : false
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 3	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x32x28x28
  indices : CudaTensor - size: 1x32x14x14
  train : false
  kW : 2
  ceil_mode : false
  padW : 0
  output : CudaTensor - size: 32x14x14
  kH : 2
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 4	
nn.SpatialConvolutionMM(32 -> 128, 5x5)
{
  padW : 0
  nInputPlane : 32
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x32x14x14
  gradBias : CudaTensor - size: 128
  dW : 1
  nOutputPlane : 128
  bias : CudaTensor - size: 128
  kH : 5
  finput : CudaTensor - size: 800x100











    
Out[60]:








  weight : CudaTensor - size: 128x800
  train : false
  gradWeight : CudaTensor - size: 128x800
  fgradInput : CudaTensor - size: 10x10
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    
Out[60]:








Output:	











    

















    
Out[60]:


















    

















    
Out[60]:








Layer 5	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x128x10x10
  train : false
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 6	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x128x10x10
  indices : CudaTensor - size: 1x128x5x5
  train : false
  kW : 2
  ceil_mode : false











    
Out[60]:








  padW : 0
  output : CudaTensor - size: 128x5x5
  kH : 2
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 7	
nn.Reshape(3200)
{
  size : LongStorage - size: 1
  batchsize : LongStorage - size: 2
  train : false
  output : CudaTensor - size: 3200
  gradInput : CudaTensor - size: 9x128x5x5
  nelement : 3200
  _gradOutput : CudaTensor - empty
  _input : CudaTensor - empty
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 8	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x3200
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 3200
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 9	
nn.Linear(3200 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x3200
  train : false
  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x3200
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	
Can't display	

Layer 10	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 11	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 12	
nn.Linear(256 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x256
  train : false
  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	
Can't display	

Layer 13	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false











    
Out[60]:








  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 14	











    
Out[60]:








nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 15	
nn.Linear(256 -> 10)
{











    
Out[60]:








  gradBias : CudaTensor - size: 10
  weight : CudaTensor - size: 10x256
  train : false
  bias : CudaTensor - size: 10
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 10
  gradWeight : CudaTensor - size: 10x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	











    
Out[60]:








Can't display	

Layer 16	
nn.SoftMax
{
  gradInput : CudaTensor - size: 9x10
  train : false
  output : CudaTensor - size: 10
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 1	
nn.SpatialConvolutionMM(1 -> 32, 5x5)
{
  padW : 0
  nInputPlane : 1
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x1x32x32
  gradBias : CudaTensor - size: 32
  dW : 1
  nOutputPlane : 32
  bias : CudaTensor - size: 32
  kH : 5
  finput : CudaTensor - size: 25x784
  weight : CudaTensor - size: 32x25
  train : false
  gradWeight : CudaTensor - size: 32x25
  fgradInput : CudaTensor - size: 28x28
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    

















    
Out[60]:








Output:	











    

















    
Out[60]:








Layer 2	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x32x28x28
  train : false
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 3	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x32x28x28
  indices : CudaTensor - size: 1x32x14x14
  train : false
  kW : 2
  ceil_mode : false
  padW : 0











    
Out[60]:








  output : CudaTensor - size: 32x14x14
  kH : 2
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 4	
nn.SpatialConvolutionMM(32 -> 128, 5x5)
{
  padW : 0
  nInputPlane : 32
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x32x14x14
  gradBias : CudaTensor - size: 128
  dW : 1
  nOutputPlane : 128
  bias : CudaTensor - size: 128
 










    
Out[60]:








 kH : 5
  finput : CudaTensor - size: 800x100
  weight : CudaTensor - size: 128x800
  train : false
  gradWeight : CudaTensor - size: 128x800
  fgradInput : CudaTensor - size: 10x10
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    
Out[60]:








Output:	











    

















    

















    
Out[60]:








Layer 5	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x128x10x10
  train : false
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 6	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x128x10x10
  indices : CudaTensor - size: 1x128x5x5
  train : false
  kW : 2
  ceil_mode : false
  padW : 0
  output : CudaTensor - size: 128x5x5
  kH : 2
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 7	
nn.Reshape(3200)
{
  size : LongStorage - size: 1
  batchsize : LongStorage - size: 2
  train : false
  output : CudaTensor - size: 3200
  gradInput : CudaTensor - size: 9x128x5x5
  nelement : 3200
  _gradOutput : CudaTensor - empty
  _input : CudaTensor - empty
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 8	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x3200
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 3200
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 9	
nn.Linear(3200 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x3200
  train : false
  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x3200
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	
Can't display	

Layer 10	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 11	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 12	
nn.Linear(256 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x256
  train : false
  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	











    
Out[60]:








Can't display	

Layer 13	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	











    
Out[60]:








Can't display	

Layer 14	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	











    
Out[60]:








Can't display	

Layer 15	
nn.Linear(256 -> 10)
{
  gradBias : CudaTensor - size: 10
  weight : CudaTensor - size: 10x256
  train : false
  bias : CudaTensor - size: 10
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 10
  gradWeight : CudaTensor - size: 10x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	











    
Out[60]:








Can't display	

Layer 16	
nn.SoftMax
{
  gradInput : CudaTensor - size: 9x10
  train : false
  output : CudaTensor - size: 10
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 1	
nn.SpatialConvolutionMM(1 -> 32, 5x5)
{
  padW : 0
  nInputPlane : 1
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x1x32x32
  gradBias : CudaTensor - size: 32
  dW : 1
  nOutputPlane : 32
  bias : CudaTensor - size: 32
  kH : 5
  finput : CudaTensor - size: 25x784
  weight : CudaTensor - size: 32x25
  train : false
  gradWeight : CudaTensor - size: 32x25
  fgradInput : CudaTensor - size: 28x28
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    

















    
Out[60]:








Output:	











    

















    
Out[60]:








Layer 2	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x32x28x28
  train : false
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 3	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x32x28x28
  indices : CudaTensor - size: 1x32x14x14
  train : false
  kW : 2
  ceil_mode : false
  padW : 0
  output : CudaTensor - size: 32x14x14
  kH : 2










    
Out[60]:








}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 4	
nn.SpatialConvolutionMM(32 -> 128, 5x5)
{
  padW : 0
  nInputPlane : 32
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x32x14x14
  gradBias : CudaTensor - size: 128
  dW : 1
  nOutputPlane : 128
  bias : CudaTensor - size: 128
 










    
Out[60]:








 kH : 5
  finput : CudaTensor - size: 800x100
  weight : CudaTensor - size: 128x800
  train : false
  gradWeight : CudaTensor - size: 128x800
  fgradInput : CudaTensor - size: 10x10
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    

















    
Out[60]:








Output:	











    

















    
Out[60]:








Layer 5	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x128x10x10
  train : false
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 6	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x128x10x10
  indices : CudaTensor - size: 1x128x5x5
  train : false
  kW : 2
  ceil_mode : false











    
Out[60]:








  padW : 0
  output : CudaTensor - size: 128x5x5
  kH : 2
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 7	
nn.Reshape(3200)
{
  size : LongStorage - size: 1
  batchsize : LongStorage - size: 2
  train : false
  output : CudaTensor - size: 3200
  gradInput : CudaTensor - size: 9x128x5x5
 










    
Out[60]:








 nelement : 3200
  _gradOutput : CudaTensor - empty
  _input : CudaTensor - empty
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 8	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x3200
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 3200
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 9	
nn.Linear(3200 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x3200
  train : false
  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x3200
  addBuffer : CudaTensor - size: 9
}
Weights:	











    
Out[60]:








Output:	











    

















    
Out[60]:








Can't display	

Layer 10	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 11	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 12	
nn.Linear(256 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x256
  train : false
  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	











    
Out[60]:








Can't display	

Layer 13	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	











    
Out[60]:








Can't display	

Layer 14	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	











    
Out[60]:








Can't display	

Layer 15	
nn.Linear(256 -> 10)
{
  gradBias : CudaTensor - size: 10
  weight : CudaTensor - size: 10x256
  train : false
  bias : CudaTensor - size: 10
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 10
  gradWeight : CudaTensor - size: 10x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	











    
Out[60]:








Can't display	

Layer 16	
nn.SoftMax
{
  gradInput : CudaTensor - size: 9x10
  train : false
  output : CudaTensor - size: 10
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 1	
nn.SpatialConvolutionMM(1 -> 32, 5x5)
{
  padW : 0
  nInputPlane : 1
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x1x32x32
  gradBias : CudaTensor - size: 32
  dW : 1
  nOutputPlane : 32
  bias : CudaTensor - size: 32
  kH : 5
  finput : CudaTensor - size: 25x784
  weight : CudaTensor - size: 32x25
  train : false
  gradWeight : CudaTensor - size: 32x25
  fgradInput : CudaTensor - size: 28x28
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    

















    
Out[60]:








Output:	











    

















    
Out[60]:








Layer 2	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x32x28x28
  train : false
}
Weights:	
Can't display	
Output:	











    
Out[60]:








Layer 3	











    

















    
Out[60]:








nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x32x28x28
  indices : CudaTensor - size: 1x32x14x14
  










    
Out[60]:








train : false
  kW : 2
  ceil_mode : false
  padW : 0
  output : CudaTensor - size: 32x14x14
  kH : 2
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 4	
nn.SpatialConvolutionMM(32 -> 128, 5x5)
{
  padW : 0
  nInputPlane : 32
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x32x14x14
  gradBias : CudaTensor - size: 128
  dW : 1
  nOutputPlane : 128











    
Out[60]:








  bias : CudaTensor - size: 128
  kH : 5
  finput : CudaTensor - size: 800x100
  weight : CudaTensor - size: 128x800
  train : false
  gradWeight : CudaTensor - size: 128x800
  fgradInput : CudaTensor - size: 10x10
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    

















    
Out[60]:








Output:	











    
Out[60]:


















    

















    
Out[60]:








Layer 5	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x128x10x10
  train : false
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 6	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x128x10x10
  indices : CudaTensor - size: 1x128x5x5
  train : false
  kW : 2
  ceil_mode : false
  padW : 0
  output : CudaTensor - size: 128x5x5
  kH : 2
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 7	
nn.Reshape(3200)
{
  size : LongStorage - size: 1
  batchsize : LongStorage - size: 2
  train : false
  output : CudaTensor - size: 3200
  gradInput : CudaTensor - size: 9x128x5x5
  nelement : 3200











    
Out[60]:








  _gradOutput : CudaTensor - empty
  _input : CudaTensor - empty
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 8	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x3200
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 3200
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 9	
nn.Linear(3200 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x3200
  train : false
  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x3200
  addBuffer : CudaTensor - size: 9
}
Weights:	











    
Out[60]:








Output:	











    

















    
Out[60]:








Can't display	

Layer 10	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 11	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 12	
nn.Linear(256 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x256
  train : false











    
Out[60]:








  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    
Out[60]:








Output:	











    

















    
Out[60]:








Can't display	

Layer 13	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	











    
Out[60]:








Can't display	

Layer 14	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	











    
Out[60]:








Can't display	

Layer 15	
nn.Linear(256 -> 10)
{
  gradBias : CudaTensor - size: 10
  weight : CudaTensor - size: 10x256
  train : false
  bias : CudaTensor - size: 10
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 10
  gradWeight : CudaTensor - size: 10x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	
Can't display	

Layer 16	
nn.SoftMax
{
  gradInput : CudaTensor - size: 9x10
  train : false
  output : CudaTensor - size: 10
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 1	
nn.SpatialConvolutionMM(1 -> 32, 5x5)
{
  padW : 0
  nInputPlane : 1
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x1x32x32
  gradBias : CudaTensor - size: 32
  dW : 1
  nOutputPlane : 32
  bias : CudaTensor - size: 32
  kH : 5
  finput : CudaTensor - size: 25x784
  weight : CudaTensor - size: 32x25
  train : false
  gradWeight : CudaTensor - size: 32x25
  fgradInput : CudaTensor - size: 28x28
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    

















    
Out[60]:








Output:	











    

















    
Out[60]:








Layer 2	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x32x28x28
  train : false
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 3	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x32x28x28
  indices : CudaTensor - size: 1x32x14x14
  train : false
  kW : 2
  ceil_mode : false
  padW : 0
  output : CudaTensor - size: 32x14x14
  kH : 2
}











    
Out[60]:








Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 4	
nn.SpatialConvolutionMM(32 -> 128, 5x5)
{
  padW : 0
  nInputPlane : 32
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x32x14x14
  gradBias : CudaTensor - size: 128
  dW : 1
  nOutputPlane : 128
  bias : CudaTensor - size: 128
  kH : 5
  finput : CudaTensor - size: 800x100
  weight : CudaTensor - size: 128x800
  train : false
  gradWeight : CudaTensor - size: 128x800
  fgradInput : CudaTensor - size: 10x10
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    
Out[60]:








Output:	











    

















    

















    
Out[60]:








Layer 5	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x128x10x10
  train : false
}
Weights:	
Can't display	











    
Out[60]:








Output:	











    
Out[60]:


















    

















    
Out[60]:








Layer 6	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x128x10x10
  indices : CudaTensor - size: 1x128x5x5
  train : false
  kW : 2
  ceil_mode : false
  padW : 0
  output : CudaTensor - size: 128x5x5
  kH : 2
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 7	
nn.Reshape(3200)
{
  size : LongStorage - size: 1
  batchsize : LongStorage - size: 2
  train : false
  output : CudaTensor - size: 3200
  gradInput : CudaTensor - size: 9x128x5x5
  nelement : 3200











    
Out[60]:








  _gradOutput : CudaTensor - empty
  _input : CudaTensor - empty
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 8	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x3200
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 3200
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 9	
nn.Linear(3200 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x3200
  train : false
  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x3200
  addBuffer : CudaTensor - size: 9
}
Weights:	











    
Out[60]:








Output:	











    

















    
Out[60]:








Can't display	

Layer 10	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 11	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 12	
nn.Linear(256 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x256
  train : false
  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	











    
Out[60]:








Can't display	

Layer 13	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	











    
Out[60]:








Can't display	

Layer 14	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	











    
Out[60]:








Can't display	

Layer 15	
nn.Linear(256 -> 10)
{
  gradBias : CudaTensor - size: 10
  weight : CudaTensor - size: 10x256
  train : false
  bias : CudaTensor - size: 10
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 10
  gradWeight : CudaTensor - size: 10x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    

















    
Out[60]:








Output:	











    
Out[60]:








Can't display	

Layer 16	
nn.SoftMax
{
  gradInput : CudaTensor - size: 9x10
  train : false
  output : CudaTensor - size: 10
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 1	
nn.SpatialConvolutionMM(1 -> 32, 5x5)
{
  padW : 0
  nInputPlane : 1
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x1x32x32
  gradBias : CudaTensor - size: 32
  dW : 1
  nOutputPlane : 32
  bias : CudaTensor - size: 32
  kH : 5
  finput : CudaTensor - size: 25x784
  weight : CudaTensor - size: 32x25
  train : false
  gradWeight : CudaTensor - size: 32x25
  fgradInput : CudaTensor - size: 28x28
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    

















    
Out[60]:








Output:	











    

















    
Out[60]:








Layer 2	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 32x28x28
  gradInput : CudaTensor - size: 9x32x28x28
  train : false
}
Weights:	











    
Out[60]:








Can't display	
Output:	











    

















    
Out[60]:








Layer 3	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x32x28x28
  indices : CudaTensor - size: 1x32x14x14
  train : false
  kW : 2
  ceil_mode : false
  padW : 0
  output : CudaTensor - size: 32x14x14
  kH : 2
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 4	
nn.SpatialConvolutionMM(32 -> 128, 5x5)
{
  padW : 0
  nInputPlane : 32
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x32x14x14
  gradBias : CudaTensor - size: 128
  dW : 1
  nOutputPlane : 128
  bias : CudaTensor - size: 128
  kH : 5
  finput : CudaTensor - size: 800x100
  weight : CudaTensor - size: 128x800
  train : false
  gradWeight : CudaTensor - size: 128x800
  fgradInput : CudaTensor - size: 10x10
  padH : 0
  dH : 1
  kW : 5
}
Weights:	











    

















    
Out[60]:








Output:	











    

















    
Out[60]:








Layer 5	
nn.ReLU
{
  inplace : false
  threshold : 0
  val : 0
  output : CudaTensor - size: 128x10x10
  gradInput : CudaTensor - size: 9x128x10x10
  train : false
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 6	
nn.SpatialMaxPooling(2,2,2,2)
{
  dH : 2
  dW : 2
  padH : 0
  gradInput : CudaTensor - size: 9x128x10x10
  indices : CudaTensor - size: 1x128x5x5
  train : false
  kW : 2
  ceil_mode : false
  padW : 0











    
Out[60]:








  output : CudaTensor - size: 128x5x5
  kH : 2
}
Weights:	
Can't display	
Output:	











    

















    
Out[60]:








Layer 7	
nn.Reshape(3200)
{
  size : LongStorage - size: 1
  batchsize : LongStorage - size: 2
  train : false
  output : CudaTensor - size: 3200
  gradInput : CudaTensor - size: 9x128x5x5
  nelement : 3200
  _gradOutput : CudaTensor - empty
  _input : CudaTensor - empty
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 8	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x3200
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 3200
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 9	
nn.Linear(3200 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x3200
  train : false
  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x3200
  output : CudaTensor - size: 256
  gradWeight : CudaTensor - size: 256x3200
  addBuffer : CudaTensor - size: 9
}
Weights:	











    
Out[60]:








Output:	











    

















    
Out[60]:








Can't display	

Layer 10	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 11	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	
Can't display	

Layer 12	
nn.Linear(256 -> 256)
{
  gradBias : CudaTensor - size: 256
  weight : CudaTensor - size: 256x256
  train : false
  bias : CudaTensor - size: 256
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256











    
Out[60]:








  gradWeight : CudaTensor - size: 256x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    
Out[60]:








Output:	











    

















    
Out[60]:








Can't display	

Layer 13	
nn.Tanh
{
  gradInput : CudaTensor - size: 9x256
  train : false
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	











    
Out[60]:








Can't display	

Layer 14	
nn.Dropout(0.500000)
{
  v2 : true
  noise : CudaTensor - size: 9x256
  train : false
  p : 0.5
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 256
}
Weights:	
Can't display	
Output:	











    
Out[60]:








Can't display	

Layer 15	
nn.Linear(256 -> 10)
{
  gradBias : CudaTensor - size: 10
  weight : CudaTensor - size: 10x256
  train : false
  bias : CudaTensor - size: 10
  gradInput : CudaTensor - size: 9x256
  output : CudaTensor - size: 10
  gradWeight : CudaTensor - size: 10x256
  addBuffer : CudaTensor - size: 9
}
Weights:	











    
Out[60]:








Output:	











    

















    
Out[60]:








Can't display	

Layer 16	
nn.SoftMax
{
  gradInput : CudaTensor - size: 9x10
  train : false
  output : CudaTensor - size: 10
}
Weights:	
Can't display	
Output:	
Can't display	



























In [ ]:



    






    
















    


    

        

            

            

                Content source: touqir14/Project_Cmput399
            

            
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