In this video, we are going to make a convolutional neural network model and we will train our model
to predict the fashion object in the images.
So we'll be using fashion amnesty doggin.
We have created Ian and modelled on this dataset in the previous section.
Now we are going to create a CNN model.
The architecture of the model is going to be this last time when we created in and model this right
and take part while our model.
We flatten the layer.
We put two dense layers, which were the hidden layers.
The first head and led had 300 neurons.
Second had 100 neurons.
And lastly, it had then you not know to make CNN model.
We are going to add two new two new layers.
The First Lady will be convolutional layer.
This layer will take out 32 features.
So we will have 32 different windows and therefore it will give us 32 features from the input images
on this convolutional layer will put up pulling it, pulling Lyd will aggregate the output of neurons
in the congressional layer and therefore reduce the computations in the network.
So this lead is added to make the network computationally more efficient.
So these are the only two layers we are adding for now.
The actual convolutional models have a stack of convolutional and pooling layers.
But for now, we just add one kostner and one pulling.
The output of pulling layer will then be input into the normal artificial neural network model that
we created lasting.
So this is the architecture on which we will be working on this thing.