# how to improve convolutional neural network

The main difference between the two is that CNNs make the explicit assumption that the inputs are images, which allows us to incorporate certain properties into the architecture. Returns the loss gradient for this layer's inputs. A beginner-friendly guide on using Keras to implement a simple Convolutional Neural Network (CNN) in Python. The test accuracy of convolutional networks approaches that of fully connected networks as depth increases. How to get started with deep learning using MRI data. We only used a subset of the entire MNIST dataset for this example in the interest of time - our CNN implementation isn’t particularly fast. Then, we will code each method and see how it impacts the performance of a network! Run this CNN in your browser. Subscribe to get new posts by email! Here’s an example. We’ll incrementally write code as we derive results, and even a surface-level understanding can be helpful. It is often biased, time-consuming, and laborious. Run the following code. Therefore, regularization is a common method to reduce overfitting and consequently improve the model’s performance. ''', '[Step %d] Past 100 steps: Average Loss %.3f | Accuracy: %d%%'. It’s also available on Github. On the other hand, an input pixel that is the max value would have its value passed through to the output, so ∂output∂input=1\frac{\partial output}{\partial input} = 1∂input∂output=1, meaning ∂L∂input=∂L∂output\frac{\partial L}{\partial input} = \frac{\partial L}{\partial output}∂input∂L=∂output∂L. Deep Neural Networks (DNNs) are now the state-of-the-art in acous-tic modeling for speech recognition, showing tremendous improve-ments on the order of 10-30% relative across a variety of small and large vocabulary tasks [1]. In this post, L2 regularization and dropout will be introduced as regularization methods for neural networks. We’ll update the weights and bias using Stochastic Gradient Descent (SGD) just like we did in my introduction to Neural Networks and then return d_L_d_inputs: Notice that we added a learn_rate parameter that controls how fast we update our weights. np.log() is the natural log. Improve the loss reduction in a neural network model. Here’s what the output of our CNN looks like right now: Obviously, we’d like to do better than 10% accuracy… let’s teach this CNN a lesson. - input can be any array with any dimensions. ''' This is standard practice. Cross-validation is definitely helpful to reduce overfitting problem. On top of that,it depends on the number of filters you are going to use for each Convolutional layer. This is pretty easy, since only pip_ipi shows up in the loss equation: That’s our initial gradient you saw referenced above: We’re almost ready to implement our first backward phase - we just need to first perform the forward phase caching we discussed earlier: We cache 3 things here that will be useful for implementing the backward phase: With that out of the way, we can start deriving the gradients for the backprop phase. To calculate that, we ask ourselves this: how would changing a filter’s weight affect the conv layer’s output? With a better CNN architecture, we could improve that even more - in this official Keras MNIST CNN example, they achieve 99% test accuracy after 15 epochs. We ultimately want the gradients of loss against weights, biases, and input: To calculate those 3 loss gradients, we first need to derive 3 more results: the gradients of totals against weights, biases, and input. All code from this post is available on Github. Increase the number of hidden layers 2. Similar to the convolutional layer, the pooling operation sweeps a filter across the entire input, but the difference is … For large number of … For instance, in a convolutional neural network (CNN) used for a frame-by-frame video processing, is there a rough estimate for the minimum no. Convolutional neural networks (CNNs) have been adopted and proven to be very effective. Completes a full training step on the given image and label. Pooling layers, also known as downsampling, conducts dimensionality reduction, reducing the number of parameters in the input. Returns a 3d numpy array with dimensions (h, w, num_filters). ''' - image is a 2d numpy array That’s the best way to understand why this code correctly computes the gradients. Time to test it out…. Now let’s do the derivation for ccc, this time using Quotient Rule (because we have an etce^{t_c}etc in the numerator of outs(c)out_s(c)outs(c)): Phew. Then we can write outs(c)out_s(c)outs(c) as: where S=∑ietiS = \sum_i e^{t_i}S=∑ieti. One other way to increase your training accuracy is to increase the per GPU batch size. The definitive guide to Random Forests and Decision Trees. If we wanted to train a MNIST CNN for real, we’d use an ML library like Keras. 1. Training a neural network typically consists of two phases: We’ll follow this pattern to train our CNN. We’ve finished our first backprop implementation! We already have ∂L∂out\frac{\partial L}{\partial out}∂out∂L for the conv layer, so we just need ∂out∂filters\frac{\partial out}{\partial filters}∂filters∂out.

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