title: Python numpy not multiplying matrix correctly.
body: <p>I am attempting to pull out these y values from neural network. The current problem seems to be numpy not multiplying the matrix as I expected. I have included the code and output for your review. Thank you in advance for any insights you can provide.</p>
  
  <pre><code>def columnToRow(column):
      newarray = np.array([column])
      return newarray
  
  
  def calcIndividualOutput(indivInputs,weights,biases):
    # finds the resulting y values for one set of input data
    I_transposed= columnToRow(indivInputs)
    output = np.multiply(I_transposed, weights) + biases
    return output
  
  
  def getOutputs(inputs,weights,biases):
    # iterates over each set of inputs to find corresponding outputs 
    # returns output matrix
    i_len = len(inputs)-1
    outputs = []
    for i in range(0,i_len):
      result = calcIndividualOutput(inputs[i],weights,biases)
      outputs.append(np.tanh(result))
      if (i==i_len):
        print("Final Input reached:", i)
    return outputs
  
  
  
  # Test Single line of Outputs should
  #print("Resulting Outputs0:\n\n",resultingOutputs[0,0:])
  
  # Testing 
  currI=data[0]
  Itrans=columnToRow(currI)
  print(" THE CURRENT I0\n\n",currI,"\n\n")
  print("transposed I:\n\n",Itrans,"\n\n")
  print("Itrans shape:\n\n",Itrans.shape,"\n\n")
  
  print("Current biases:\n\n",model_l1_b,"\n\n")
  print("Current biases shape:\n\n",model_l1_b.shape,"\n\n")
  print("B trans:",b_trans,"\n\n")
  print("B trans shape:",b_trans.shape,"\n\n")
  
  print("Current weights:\n\n",model_l1_W,"\n\n")
  print("Transposed weights\n\n",w_transposed,"\n\n")
  print("wtrans shape:\n\n",w_transposed.shape,"\n\n")
  
  
  
  #Test calcIndividualOutput
  
  testOutput= calcIndividualOutput(currI,w_transposed,b_trans)
  print("Test calcIndividualOutput:\n\n",testOutput,"\n\n")
  print("Test calcIndividualOutput Shape:\n\n",testOutput.shape,"\n\n")
  
  # Transpose weights to match dimensions of input
  b_trans=columnToRow(model_l1_b)
  w_transposed=np.transpose(model_l1_W)
  resultingOutputs = getOutputs(data,w_transposed,b_trans)
  </code></pre>
  
  <p>Output:</p>
  
  <pre><code>THE CURRENT I0
  
   [-0.66399151 -0.59143853  0.5230611  -0.52583802 -0.31089544  0.47396523
   -0.7301591  -0.21042131  0.92044264 -0.48792791 -1.54127669] 
  
  
  transposed I:
  
   [[-0.66399151 -0.59143853  0.5230611  -0.52583802 -0.31089544  0.47396523
    -0.7301591  -0.21042131  0.92044264 -0.48792791 -1.54127669]] 
  
  
  Itrans shape:
  
   (1, 11) 
  
  
  Current biases:
  
   [ 0.04497563 -0.01878226  0.03285328  0.00443657 -0.10408497  0.03982726
   -0.07724283] 
  
  
  Current biases shape:
  
   (7,) 
  
  
  B trans: [[ 0.04497563 -0.01878226  0.03285328  0.00443657 -0.10408497  0.03982726
    -0.07724283]] 
  
  
  B trans shape: (1, 7) 
  
  
  Current weights:
  
   [[ 0.02534341  0.01163373 -0.20102289  0.23845847  0.20859972 -0.09515963
     0.00744185 -0.06694793 -0.03806938  0.02241485  0.34134269]
   [ 0.0828636  -0.14711063  0.44623381  0.0095899   0.41908434 -0.25378567
     0.35789928  0.21531652 -0.05924326 -0.18556432  0.23026766]
   [-0.23547475 -0.18090464 -0.15210266  0.10483326 -0.0182989   0.52936584
     0.15671678 -0.64570689 -0.27296376  0.28720504  0.21922119]
   [-0.17561196 -0.42502806 -0.34866759 -0.07662395 -0.02361901 -0.10330012
    -0.2626377   0.19807351  0.20543958 -0.34499851  0.29347673]
   [-0.04404973 -0.31600055 -0.22984107  0.21733086 -0.15065287  0.18301299
     0.13399698  0.11884601  0.04380761 -0.03720044  0.0146924 ]
   [ 0.25086868  0.15678053  0.30350113  0.13065964 -0.30319506  0.47015968
     0.00549904  0.32486886 -0.00331726  0.22858304  0.16789439]
   [-0.10196115 -0.03687141 -0.28674102  0.01066647  0.2475083   0.15808311
    -0.1452509   0.09170815 -0.14578934 -0.07375327 -0.16524883]] 
  
  
  Transposed weights
  
   [[ 0.02534341  0.0828636  -0.23547475 -0.17561196 -0.04404973  0.25086868
    -0.10196115]
   [ 0.01163373 -0.14711063 -0.18090464 -0.42502806 -0.31600055  0.15678053
    -0.03687141]
   [-0.20102289  0.44623381 -0.15210266 -0.34866759 -0.22984107  0.30350113
    -0.28674102]
   [ 0.23845847  0.0095899   0.10483326 -0.07662395  0.21733086  0.13065964
     0.01066647]
   [ 0.20859972  0.41908434 -0.0182989  -0.02361901 -0.15065287 -0.30319506
     0.2475083 ]
   [-0.09515963 -0.25378567  0.52936584 -0.10330012  0.18301299  0.47015968
     0.15808311]
   [ 0.00744185  0.35789928  0.15671678 -0.2626377   0.13399698  0.00549904
    -0.1452509 ]
   [-0.06694793  0.21531652 -0.64570689  0.19807351  0.11884601  0.32486886
     0.09170815]
   [-0.03806938 -0.05924326 -0.27296376  0.20543958  0.04380761 -0.00331726
    -0.14578934]
   [ 0.02241485 -0.18556432  0.28720504 -0.34499851 -0.03720044  0.22858304
    -0.07375327]
   [ 0.34134269  0.23026766  0.21922119  0.29347673  0.0146924   0.16789439
    -0.16524883]] 
  
  
  wtrans shape:
  
   (11, 7) 
  
  
  ---------------------------------------------------------------------------
  ValueError                                Traceback (most recent call last)
  &lt;ipython-input-162-7e8be1d52690&gt; in &lt;module&gt;()
       48 #Test calcIndividualOutput
       49 
  ---&gt; 50 testOutput= calcIndividualOutput(currI,w_transposed,b_trans)
       51 print("Test calcIndividualOutput:\n\n",testOutput,"\n\n")
       52 print("Test calcIndividualOutput Shape:\n\n",testOutput.shape,"\n\n")
  
  &lt;ipython-input-162-7e8be1d52690&gt; in calcIndividualOutput(indivInputs, weights, biases)
        7   # finds the resulting y values for one set of input data
        8   I_transposed= columnToRow(indivInputs)
  ----&gt; 9   output = np.multiply(I_transposed, weights) + biases
       10   return output
       11 
  
  ValueError: operands could not be broadcast together with shapes (1,11) (11,7) 
  </code></pre>