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A step towords different MLP architectures on MNIST dataset
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01_Tensorflow_Softmax_Classifier.ipynb
02_MLP_Tensorflow_drive.ipynb
03_Keras_on_Mnist_dataset.ipynb
2,3,5_Hidden_layers_Keras_Mnist.ipynb
Final 2,3,5_Hidden_layers_Keras_Mnist.pdf
README.md

README.md

Different-MLP-architectures-on-MNIST-dataset

A step towords different MLP architectures on MNIST dataset image image

  • This experiments are being implimented in Keras by using Google colab GPU.
  • To experiment with 2 hidden layes architecture we choose Neuron size of 512 and 256

2 hidden layers ==> 784-> 512,256 -->10

  • To experiment with 3 hidden layes architecture we choose Neuron size of 512,256 and 128

3 hidden layers ==> 784-> 512,256,128 -->10

  • To experiment with 5 hidden layes architecture we choose Neuron size of 512,256,128,64 and 32

5 hidden layers ==> 784-> 512,256,128,64,32 -->10

  • As we know till now, ReLU is one of the best activation function. Hence, we set our acivation function as ReLU.
  • And best optimizer is Adam. When we train our 2 layers, 3 layers and 5 layers NN with ReLU activation function on Adam Optimizer with Dropout, we got a very good Test score as compaire to other models.
  • As MNIST is not a very large dataset, we are not able to see far difference in the Test accuracy. This can be observe quite when we try our hand with large scale datasets.
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