title: Keras - CNN Model Summary Diemension Interpretation
body: <p>I am using Keras library to build this deep learning model: INPUT(depth=1, height=15, width=27) -> CONV[depth=8](height=4, width=27) -> POOL(height=2, width=1) -> (Regression) output. </p>
  
  <p>I expect the ouput shape from convolution2d_1 to be (None, 8, 12, 1) and thence, the ouput shape from pooling2d_1 to be (None, 8, 6, 1); while I am getting (None, 8, 15, 27) and (None, 8, 7, 27) respectively.</p>
  
  <p>What am I doing or interpreting wrong here?</p>
  
  <p>P.S.: Also, this setting gives a Baseline Error: 99.23%! </p>
  
  <pre><code>print "SHAPE OF INPUT IS:", num_train_3D, depth, height, width
  inp = Input(shape=(depth, height, width)) 
  conv_1 = Convolution2D(8, 4, 27, border_mode='same', activation='relu')(inp)
  pool_1 = MaxPooling2D(pool_size=(2, 1))(conv_1)
  ''' Now flatten to 1D, apply FC -&gt; ReLU (with dropout) -&gt; softmax '''
  flat = Flatten()(pool_1)
  out = Dense(1)(flat)  #regression
  
  model = Model(input=inp, output=out) # To define a model, just specify its input and output layers
  
  print "Model Summary:"
  print model.summary()
  </code></pre>
  
  <p>=====================================</p>
  
  <pre><code>SHAPE OF INPUT IS: 53745 1 15 27
  Model Summary:
  ____________________________________________________________________________________________________
  Layer (type)                     Output Shape          Param #     Connected to                     
  ====================================================================================================
  input_1 (InputLayer)             (None, 1, 15, 27)     0                                            
  ____________________________________________________________________________________________________
  convolution2d_1 (Convolution2D)  (None, 8, 15, 27)     872         input_1[0][0]                    
  ____________________________________________________________________________________________________
  maxpooling2d_1 (MaxPooling2D)    (None, 8, 7, 27)      0           convolution2d_1[0][0]            
  ____________________________________________________________________________________________________
  flatten_1 (Flatten)              (None, 1512)          0           maxpooling2d_1[0][0]             
  ____________________________________________________________________________________________________
  dense_1 (Dense)                  (None, 1)             1513        flatten_1[0][0]                  
  ====================================================================================================
  Total params: 2,385
  Trainable params: 2,385
  Non-trainable params: 0
  </code></pre>