Description

All of the dnn module is located in dnn folder. One may feel free to utilize the layers and optimizers to build his own multi-layer perceptron classifier in the same way as using Pytorch modules.

• data.py provides a function to load the MNIST dataset, which is located in mnist_data.
• functional.py provides all of the loss function, activation functions and linear layers required for this project. Specially, the Softmax classifier connected to the CrossEntropyLoss does not involve in the backpropagation, because the gradient of these two combined is easier to compute.
• nn.py provides a Module template for our net.
• optim.py provides an SGD optimizer.
• train.py provides the details of training procedure.
• utils.py provides a DataLoader() class analogous to the torch.utils.data.DataLoader().

The working file mnist.ipynb is for hyperparameter searching, and the file visualizer.ipynb is for parameter visualization.

A package of python-mnist is supposed to be installed in advance.

pip install python-mnist

Load Trained Model

Put the downloaded model named mnist_hidden_256.pkl (or any other file ends with .pkl) under model directory.

链接：https://pan.baidu.com/s/1ea0penlifIwlBU9KdQZlhw 提取码：0216

You may refer to the following code to reload the model.

import dnn
import dnn.functional as F
from dnn.data import load_data_mnist
import dnn.nn as nn
import dnn.optim as optim
from dnn.train import train
import numpy as np
import pickle

class MLP(nn.Module):
    def __init__(self, num_inputs, num_hiddens, num_outputs, activation='relu'):
        super(MLP, self).__init__()
        if activation == 'relu':
            self.layers = [F.Linear(num_inputs, num_hiddens), F.ReLU(), F.Linear(num_hiddens, num_outputs), F.Softmax()]
        elif activation == 'sigmoid':
            self.layers = [F.Linear(num_inputs, num_hiddens), F.Sigmoid(), F.Linear(num_hiddens, num_outputs), F.Softmax()]
            
with open('./model/mnist_hidden_256.pkl','rb') as f:
    net = pickle.load(f)    # This will be the file