‫Now the next step is to create the structure of our CNN model
‫for this problem.
‫We will be using four different conv layers with Max pooling.
‫And after that we will apply our dense layer and then an output Nero so for our first layer we will
‫have one layer with 32 feeders and three by three window.
‫And our input sizes 150 by 150 by 3.
‫Remember we mentioned our target size.
‫While data processing as one for pick was one of these.
‫And since this images are colored images we have added we will lose.
‫That's why we are providing a third dimension as three after this.
‫We want the max pooling layer here.
‫We will use a window of two way too.
‫After this we will use another layer with six to 44 years and three by three window.
‫Then another willingly then we will use another con layer with 128 features and add another pooling
‫layer of the White tool window then we will have another layer of 128 filters and a pooling there with
‫two by two window
‫then we will use like ten and then a single dense layer with five and value at all and then a single
‫output layer with one neuron.
‫Since we would only want to predict two glasses
‫one important thing here is to know the number of features that we are using.
‫You can see as we are going on in our network we are increasing the number of four years and now what
‫can layer.
‫So this is a general practice with each cone layer.
‫You have to double the number of filters.
‫You are going to use and another important thing is that if you remember the max pool of window 2 way
‫to you at reducing the size of fewer images.
‫So if you have a image of 150 by 150 after Max bullying you will have a major of 75 but 75 so after
‫this is step off that this layer we will have images in the dimension of zone divide by 70 feet.
‫Then after this another Max pulling we will have images of 37 by 37.
‫Then after this we will have images of 18 by 18 and then 9 by 9 so if you see the number of features
‫are increasing as we go along on our network and the work and the lens of forward images are decreasing.
‫This is a standard practice.
‫You are image size should decrease with each one layer and the feature map height should increase as
‫you go along the network.
‫So this is the structure we are following for this problem let's just run this
‫another thing is that earlier we were using guitars got laid.
‫So now we have important layers and we don't have to put guitars or layers and so on.
‫So another little thing
‫let's look at somebody.
‫You can see we have four can layer with four different pooling layers and then a dense layer and then
‫output layer the total number of parameters that we are creating in our model is that on three million
‫now.
‫The next step is to compile the model.
‫And this time instead of using as Zilly we are going to use Adam's prop
‫Adam's prop.
‫Have a little advantage or as Julie while performing image processing.
‫That's why we are going to use Adam's prop.
‫And here we have also mentioned the learning rate of zero point zero 0 1 by default.
‫The learning rate is point 0 1 and we have mentioned the learning date of zero point 0 0 1 7 this
‫now.
‫The next step is to fit training data in our model and earlier we were using model load fit but now
‫since we have data in the form of green generator we are getting our data in the batches of 20 directly
‫from our directory.
‫That's why instead of using dot fake we are going to use dot generated for using image data generated
‫as our input.
‫We have to use for dinner.
‫The process is almost same instead of DOT fed.
‫You have to use dot underscore the data then then you have to provide the object that is generating
‫the data.
‫In our case that is green generator that we have created earlier.
‫Now now another difference is that this train generator is generating data continuously.
‫So we have to mention this stopping point and we are mentioning the stopping point in the form of number
‫of steps.
‫As you remember here while creating our train generator we mentioned that the bed size should be of
‫20.
‫So this train generator is generating data in the packets of 20 images.
‫Now our crane dataset is of 2000 images so how many steps are required to cover all that to hold on
‫the steps.
‫That is 2000 divided by the number of images in each bet which is 20.
‫So that's where we want under step in the hundreds of steps we are going to cover all the 2000 images
‫that we have in our train directory.
‫So just remember to use the steps but epoch barometer.
‫And this you should go away by dividing the total number of images that you have by the number of images
‫you have in your each batch.
‫So 2000 divided by 20 equally 200.
‫We want to create this model for 20 bucks.
‫That's why we are providing box equal to 20 and similar to fate.
‫We can provide our validation data as well here the validation data is in the form of validation and
‫data.
‫We have created validation generated Aliyah and here also we are using a bed size of 20 and in our validation
‫formula we have it on poles and may images so tall then divided by 20.
‫We need to run this for steps.
‫So we have to provide validation additional steps equally 250
‫let's run this.
‫So if you see this is very similar to fake function and fake function will directly provide the data
‫here and set off providing data directly we are providing a generator that is generating the data and
‫we are mentioning the steps or the limit on the number of times that generator is going to generate
‫the data.
‫So the training may take several minutes.
‫So I am skipping the training part.