Negative dimension size caused by subtracting 3 from 1 for 'conv2d_2/convolution'

Question

I got this error message when declaring the input layer in Keras.

ValueError: Negative dimension size caused by subtracting 3 from 1 for \'conv2d_2/

Answer 1

    # define the model as a class
class LeNet:

  '''
      In a sequential model, we stack layers sequentially. 
      So, each layer has unique input and output, and those inputs and outputs 
      then also come with a unique input shape and output shape.

  '''

  @staticmethod                ## class can instantiated only once 
  def init(numChannels, imgRows, imgCols , numClasses, weightsPath=None):

    # if we are using channel first we have update the input size
    if backend.image_data_format() == "channels_first":
      inputShape = (numChannels , imgRows , imgCols)
    else: 
      inputShape = (imgRows , imgCols , numChannels)

    # initilize the model
    model = models.Sequential()

    # Define the first set of CONV => ACTIVATION => POOL LAYERS

    model.add(layers.Conv2D(  filters=6,kernel_size=(5,5),strides=(1,1), 
                              padding="valid",activation='relu',kernel_initializer='he_uniform',input_shape=inputShape))
    model.add(layers.AveragePooling2D(pool_size=(2,2),strides=(2,2)))

I hope it would help :)

See code : Fashion_Mnist_Using_LeNet_CNN