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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 in and model on this problem, we used this side of the network.

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We first use flatten and 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 well, 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 straight off 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 where 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 swelling.

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So the convolutional layer, we are going to get this 26 by 26 and to 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 where to Max, bullying.

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So finally, after this corn layer, we are going to get 13 by 13 and to 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 get us 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 over feature of size three by three.

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So we have to grow.

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Kernel size has three comma three.

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If you want double the size of four, wherefore you can write for pomoc forward.

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By default those start value is one.

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We are going to use the same.

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And we are going to use buiding 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 Collodi images, you have to give values for channel as well.

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So for Galardi images, this should be three.

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

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So this is a work on layer four or pulling layer.

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We can edit using this syntax model a lot and then Gaydos 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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We love to cross too.

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That's why we are giving to comment to 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 and then.

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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 that tuition 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 Dort somebody Martek to get details.

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All four were Mortel architecture.

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Here you can get information of all the layers or does 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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no 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 bullying layer.

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The next step is to compile the model now, since we have exclusive might be 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.