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Cnn Artificial Neural Network

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CNN is a type of deep artificial neural networks based on feed forward architecture that proves efficient when applied in visual imagery. It requires minimal preprocessing due to its multi-layer perceptron design and always assumes that the input it receives is an image which indeed helps to pass certain parameters into the architecture. However, because of this assumption we are able to implement forward function more efficiently and also this will help to reduce the parameters in the network.

  • In machine learning, CNN, or ConvNet is a class of deep, feed-forward artificial neural networks that has successfully been applied to analyzing visual imagery.
  • It uses relatively little pre-processing compared to other image classification algorithms.
  • The network learns the filters that in traditional algorithms were hand-engineered. This independence from prior knowledge and human effort in feature design is a major advantage. Figure4.2: Working of CNN. A CNN consists of an input layer, output layer, and multiple hidden layers. It’s architecture is formed by a stack of distinct layers that transform the input volume into an output volume through a differentiable function. A few distinct types of hidden layers which are commonly used are:
  • Convolutional layer: The convolutional layer is the core building block of a CNN. The layer’s parameters consist of a set of learnable filters (or kernels), which have a small receptive field, but extend through the full depth of the input volume. During the forward pass, each filter is convolved across the width and height of the input volume, computing the dot product between the entries of the filter and the input and producing a 2-dimensional activation map of that filter. As a result, the network learns filters that activate when it detects some specific type of feature at some spatial position in the input. Figure4.3: Convolutional Layer.
  • Pooling layer: Another important concept of CNNs is pooling, which is a form of non-linear down-sampling. There are several non-linear functions to implement pooling among which max pooling is the most common. It partitions the input image into a set of non-overlapping rectangles and, for each such sub-region, outputs the maximum. The intuition is that the exact location of a feature is less important than its rough location relative to other features. The pooling layer serves to progressively reduce the spatial size of the representation, to reduce the number of parameters and amount of computation in the network, and hence to also control over fitting. Figure4.4: Max Pooling Layer.
  • Fully connected: Fully connected layers connect every neuron in one layer to every neuron in another layer. Finally, after several convolutional and max pooling layers, the high-level reasoning in the neural network is done via fully connected layers. Neurons in a fully connected layer have connections to all activations in the previous layer, as seen in regular neural networks. Their activations can hence be computed with a matrix multiplication followed by a bias offset. The input Monochrome images for our training data was of size 128×128. We centered cropped our input images to get the desired pixel sizes for our experiments. The images are processed through a pile of convolutional layers where we have used a small filter size of 3×3.Our network comprise of total of 21 layers. We have used a fixed convolutional stride of 1 pixel with a padding of 1 pixel for the 3×3 convolutional layer. For max-pooling we have used a pixel window of 2×2. We used a stack of convolutional layers (where with different depths we can achieve different architectures) which is carry forward by four fully connected layers.

For the first fully connected layer we have used 500 channels and for the next three layers 1000 channels are used. The fifth fully connected layer will have 5 channels which will perform classification for our five classes. The dropout layers are used to reduce the over fitting of the network. Here is the layer information of the custom model.

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