用简单的cnn识别minist手写数字简单识别代码
'''
Trains a simple convnet on the MNIST dataset.
'''
from future import print_function
import os
import struct
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
def load_mnist(path, kind='train'):
"""Load MNIST data from path"""
labels_path = os.path.join(path, '%s-labels.idx1-ubyte' % kind)
images_path = os.path.join(path, '%s-images.idx3-ubyte' % kind)
with open(labels_path, 'rb') as lbpath:
magic, n = struct.unpack('>II', lbpath.read(8))
labels = np.fromfile(lbpath, dtype=np.uint8)
with open(images_path, 'rb') as imgpath:
magic, num, rows, cols = struct.unpack(">IIII", imgpath.read(16))
images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 784)
return images, labels
X_train, y_train = load_mnist('./data', kind='train')
print("shape:",X_train.shape)
print('Rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1]))
X_test, y_test = load_mnist('./data', kind='t10k')
print('Rows: %d, columns: %d' % (X_test.shape[0], X_test.shape[1]))
batch_size = 100
num_classes = 10
epochs = 2
input image dimensions
img_rows, img_cols = 28, 28
x_train= X_train
x_test=X_test
if 'channels_first' == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
num_samples=x_train.shape[0]
print("num_samples:",num_samples)
'''
build torch model
'''
class Net(nn.Module):
def init(self):
super(Net, self).init()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x)
model = Net()
if os.path.exists('mnist_torch.pkl'):
model = torch.load('mnist_torch.pkl')
print(model)
'''
trainning
'''
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
loss=torch.nn.CrossEntropyLoss(size_average=True)
def train(epoch,x_train,y_train):
num_batchs = num_samples/ batch_size
model.train()
for k in range(num_batchs):
start,end = kbatch_size,(k+1)batch_size
data, target = Variable(x_train[start:end],requires_grad=False), Variable(y_train[start:end],requires_grad=False)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
if k % 10 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, k * len(data), num_samples,
-
- k / num_samples, loss.data[0]))
torch.save(model, 'mnist_torch.pkl')
'''
evaludate
'''
def test(epoch):
model.eval()
test_loss = 0
correct = 0
if 2>1:
data, target = Variable(x_test, volatile=True), Variable(y_test)
output = model(data)
test_loss += F.nll_loss(output, target).data[0]
pred = output.data.max(1)[1] # get the index of the max log-probability
correct += pred.eq(target.data).cpu().sum()
test_loss = test_loss
test_loss /= len(x_test) # loss function already averages over batch size
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(x_test),
- k / num_samples, loss.data[0]))
-
- correct / len(x_test)))
x_train=torch.from_numpy(x_train).float()
x_test=torch.from_numpy(x_test).float()
y_train=torch.from_numpy(y_train).long()
y_test=torch.from_numpy(y_test).long()
for epoch in range(1,epochs):
train(epoch,x_train,y_train)
test(epoch)
- correct / len(x_test)))