title: reshaping array keras model to suit minimum size of pre_trained_model
body: <p>I am trying to do some image classification using transfer learning on the inceptionv3 algorithm. I have 48x48 greyscale images but the inception v3 model needs a minimum input size of 75x5 (see below)</p>
  <pre><code>pre_trained_model = InceptionV3(input_shape = (48,48,3),
                             include_top  = False,
                             weights      = 'imagenet')
  
  ValueError: Input size must be at least 75x75; Received: input_shape=(48, 48, 3)
  </code></pre>
  <p>If i try and reshape my array to something greater than 75x75 i get the following error which makes sense</p>
  <pre><code>train_images = train_image_array.reshape((train_image_array.shape[0], 76, 76, 1))
  ValueError: cannot reshape array of size 66145536 into shape (28709,76,76,1)
  </code></pre>
  <p>How can i populate my array as something bigger than 75x75? I feel like it should be easy but i cant seem to find a solution. My code to populate my array is as follows:</p>
  <pre><code>image_array = np.zeros(shape=(len(data), 48, 48))
  image_label = np.array(list(map(int, data['emotion'])))
  
  for i, row in enumerate(data.index):
      image = np.fromstring(data.loc[row, 'pixels'], dtype=int, sep=' ')
      image = np.reshape(image, (48, 48))
      image_array[i] = image
      
  return image_array, image_label
  </code></pre>