319 lines
15 KiB
Plaintext
319 lines
15 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 402 RNN\n",
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"\n",
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"View more, visit my tutorial page: https://mofanpy.com/tutorials/\n",
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"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n",
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"\n",
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"Dependencies:\n",
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"* torch: 0.1.11\n",
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"* matplotlib\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"from torch import nn\n",
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"from torch.autograd import Variable\n",
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"import torchvision.datasets as dsets\n",
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"import torchvision.transforms as transforms\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<torch._C.Generator at 0x7f5864059930>"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"torch.manual_seed(1) # reproducible"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Hyper Parameters\n",
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"EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch\n",
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"BATCH_SIZE = 64\n",
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"TIME_STEP = 28 # rnn time step / image height\n",
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"INPUT_SIZE = 28 # rnn input size / image width\n",
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"LR = 0.01 # learning rate\n",
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"DOWNLOAD_MNIST = True # set to True if haven't download the data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Mnist digital dataset\n",
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"train_data = dsets.MNIST(\n",
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" root='./mnist/',\n",
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" train=True, # this is training data\n",
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" transform=transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to\n",
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" # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]\n",
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" download=DOWNLOAD_MNIST, # download it if you don't have it\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"torch.Size([60000, 28, 28])\n",
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"torch.Size([60000])\n"
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]
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},
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{
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"data": {
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"image/png": 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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f580f9626a0>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# plot one example\n",
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"print(train_data.train_data.size()) # (60000, 28, 28)\n",
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"print(train_data.train_labels.size()) # (60000)\n",
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"plt.imshow(train_data.train_data[0].numpy(), cmap='gray')\n",
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"plt.title('%i' % train_data.train_labels[0])\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Data Loader for easy mini-batch return in training\n",
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"train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# convert test data into Variable, pick 2000 samples to speed up testing\n",
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"test_data = dsets.MNIST(root='./mnist/', train=False, transform=transforms.ToTensor())\n",
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"test_x = Variable(test_data.test_data, volatile=True).type(torch.FloatTensor)[:2000]/255. # shape (2000, 28, 28) value in range(0,1)\n",
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"test_y = test_data.test_labels.numpy().squeeze()[:2000] # covert to numpy array"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"class RNN(nn.Module):\n",
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" def __init__(self):\n",
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" super(RNN, self).__init__()\n",
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"\n",
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" self.rnn = nn.LSTM( # if use nn.RNN(), it hardly learns\n",
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" input_size=INPUT_SIZE,\n",
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" hidden_size=64, # rnn hidden unit\n",
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" num_layers=1, # number of rnn layer\n",
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" batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)\n",
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" )\n",
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"\n",
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" self.out = nn.Linear(64, 10)\n",
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"\n",
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" def forward(self, x):\n",
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" # x shape (batch, time_step, input_size)\n",
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" # r_out shape (batch, time_step, output_size)\n",
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" # h_n shape (n_layers, batch, hidden_size)\n",
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" # h_c shape (n_layers, batch, hidden_size)\n",
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" r_out, (h_n, h_c) = self.rnn(x, None) # None represents zero initial hidden state\n",
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"\n",
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" # choose r_out at the last time step\n",
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" out = self.out(r_out[:, -1, :])\n",
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" return out"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"RNN (\n",
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" (rnn): LSTM(28, 64, batch_first=True)\n",
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" (out): Linear (64 -> 10)\n",
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")\n"
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]
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}
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],
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"source": [
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"rnn = RNN()\n",
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"print(rnn)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all cnn parameters\n",
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"loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch: 0 | train loss: 2.3127 | test accuracy: 0.14\n",
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"Epoch: 0 | train loss: 1.1182 | test accuracy: 0.56\n",
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"Epoch: 0 | train loss: 0.9374 | test accuracy: 0.68\n",
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"Epoch: 0 | train loss: 0.6279 | test accuracy: 0.75\n",
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"Epoch: 0 | train loss: 0.8004 | test accuracy: 0.75\n",
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"Epoch: 0 | train loss: 0.3407 | test accuracy: 0.88\n",
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"Epoch: 0 | train loss: 0.3342 | test accuracy: 0.89\n",
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"Epoch: 0 | train loss: 0.3200 | test accuracy: 0.91\n",
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"Epoch: 0 | train loss: 0.3430 | test accuracy: 0.92\n",
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"Epoch: 0 | train loss: 0.1234 | test accuracy: 0.93\n",
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"Epoch: 0 | train loss: 0.2714 | test accuracy: 0.92\n",
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"Epoch: 0 | train loss: 0.1043 | test accuracy: 0.93\n",
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"Epoch: 0 | train loss: 0.2893 | test accuracy: 0.93\n",
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"Epoch: 0 | train loss: 0.0635 | test accuracy: 0.94\n",
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"Epoch: 0 | train loss: 0.2412 | test accuracy: 0.94\n",
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"Epoch: 0 | train loss: 0.1203 | test accuracy: 0.92\n",
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"Epoch: 0 | train loss: 0.0807 | test accuracy: 0.94\n",
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"Epoch: 0 | train loss: 0.1307 | test accuracy: 0.94\n",
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"Epoch: 0 | train loss: 0.1166 | test accuracy: 0.95\n"
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]
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}
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],
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"source": [
|
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"# training and testing\n",
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"for epoch in range(EPOCH):\n",
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" for step, (x, y) in enumerate(train_loader): # gives batch data\n",
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" b_x = Variable(x.view(-1, 28, 28)) # reshape x to (batch, time_step, input_size)\n",
|
|
" b_y = Variable(y) # batch y\n",
|
|
"\n",
|
|
" output = rnn(b_x) # rnn output\n",
|
|
" loss = loss_func(output, b_y) # cross entropy loss\n",
|
|
" optimizer.zero_grad() # clear gradients for this training step\n",
|
|
" loss.backward() # backpropagation, compute gradients\n",
|
|
" optimizer.step() # apply gradients\n",
|
|
"\n",
|
|
" if step % 50 == 0:\n",
|
|
" test_output = rnn(test_x) # (samples, time_step, input_size)\n",
|
|
" pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()\n",
|
|
" accuracy = sum(pred_y == test_y) / float(test_y.size)\n",
|
|
" print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.2f' % accuracy)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[7 2 1 0 4 1 4 9 5 9] prediction number\n",
|
|
"[7 2 1 0 4 1 4 9 5 9] real number\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# print 10 predictions from test data\n",
|
|
"test_output = rnn(test_x[:10].view(-1, 28, 28))\n",
|
|
"pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()\n",
|
|
"print(pred_y, 'prediction number')\n",
|
|
"print(test_y[:10], 'real number')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.5.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|