![]() Tf.(5,activation = "softmax") #Adding the Output LayerĪ convoluted image can be too large and so it is reduced without losing features or patterns, so pooling is done. Tf.(550,activation="relu"), #Adding the Hidden layer import NumPy as npĢ) Here we required the following code to form the CNN model model = tf.([ Now we will move forward to see a case study of CNN.ġ) Here we are going to import the necessary libraries which are required for performing CNN tasks. Up to this point, we have seen concepts that are important for our building CNN model. You can understand very easily from the following figure: So finally, there is a fully connected layer that you can see which identifies the exact object in the image. There are multiple hidden layers like the convolution, the ReLU, and the pooling layer that performs feature extraction from your image. The hidden layers carry Feature Extraction by performing various calculations and operations. The first thing you should do is feed the pixels of the image in the form of arrays to the input layer of the neural network (MLP networks used to classify such things). Now imagine there is an image of a bird, and you want to identify it whether it is really a bird or something other. It is a generalization of the sigmoid function.
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