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‫Now, this is the architecture we are going to use for our CNN model.

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‫If you remember last time when we ran ANN model on this problem, we used this side of the network.

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‫We first use flatten layer, then dense layered with 300 neuron, then dense layer with 100 neurons

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‫and then the output layer.

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‫We are going to add CNN layers before this.

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‫So this is our input, 28 by 28 pixel cross one.

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‫So 28 pixels as width, 28 pixels as height and one as the channel.

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‫We are going to use a filter size of three by three with the shride of one.

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‫And then this will be our convolutional layer.

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‫Since we are using filter size of three by three.

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‫And we are also using a padding as valid.

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‫We are ignoring one pixel from each side.

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‫Since our padding is valid

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‫So the convolutional layer, we are going to get this 26 by 26 into 32, we are going to use 32 different

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‫filters for this convolutional layer.

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‫After this, we are going to use to use Max pooling

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‫So finally, after this corn layer, we are going to get 13 by 13 into 32 cells in our pooling layer.

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‫Now, we have to add some dense layer as well.

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‫So we have to convert this 3D object into one dimensionality.

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‫So we are using flatten layer.

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‫Then we are going to use to dense layer.

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‫First one with 300 neuron.

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‫And next one with hundred neurons.

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‫And then we are going to use an output layer with 10 neurons.

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‫Since we have to identify ten different classes, the code is almost similar.

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‫First, we have to create a model object.

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‫Then we are going to add the convolutional layer.

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‫We'll write model, dot, add and then keras dot layers, dot com, 2d.

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‫And as parameters, we want to do filters.

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‫We want to have our filter of size three by three.

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‫So we have to provide

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‫Kernel size has three comma three.

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‫If you want a filter size of four by four you can write four comma four

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‫By default the stride value is one.

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‫We are going to use the same.

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‫And we are going to use padding as valid.

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‫The activation is always relu.

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‫And the input shape is 28.

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‫Cross 28.

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‫Cross one.

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‫If you are dealing with any Colored images, you have to give values for channel as well.

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‫So for colored images, this should be three.

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‫This should be the height, width
‫and then the number of channels.

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‫So this is a work on layer for our pulling layer.

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‫We can edit using this syntax model dot add then Keraf dot layers dot max pulling 2D.

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‫And then here you have to give the dimensions of your window.

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‫We are going to use deceptive.

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‫Field of two cross two.

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‫That's why we are giving two comma two as a parameter.

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‫After this, we want to convert our three dimensional matrix into one dimensional matrix.

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‫So we have to use flatten and after flatten.

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‫We can use our dense layers as we did in ANN.

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‫So we are adding another layer with 300 neurons.

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‫And then another layer with 200 neurons.

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‫And for both of these layers, we want our activation function to be of RELU.

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‫And the next layer is our output layer.

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‫We want to classify 10 different categories.

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‫That's why we are using 10 neurons.

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‫And the activation is softmax activation because this is a classification problem.

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‫Now, let's run this.

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‫No, similar to last time.

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‫We can use Dot summary method to get details.

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‫Of our model architecture.

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‫Here you can get information of all the layers or what is the shape of those layers and the number of parameters

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‫that you are going to train.

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‫Using this model architecture,

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‫now important thing here you can notice is that the number of parameters we are going to train is in

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‫millions.

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‫So in this dense layer, we have around one point six million parameters to train.

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‫Now, if we have not used Max fulling layer two, this would have been around four million or five million

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‫neurons.

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‫So so using max pooling has significantly reduced the number of parameters that we are going to train

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‫in the next lecture.

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‫We will try to train a model without a pooling layer.

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‫And there we can clearly see the difference in number of parameter and the execution time in the models

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‫with the pulling layer and without the pullying layer.

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‫The next step is to compile the model now since we have exclusive multi label classification problem.

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‫We are going to use a sparse, categorical and Cross and Groppi and we want to use Optimizer as a study.

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‫So class state, Verdean descent.

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‫And we want to calculate the accuracy.

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‫Since we are doing the classification problem, it's compiled this model.