def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net = Net().to(device)
criterion = nn.CrossEntropyLoss()
# optimizer = optim.SGD(net.parameters(), lr=.001, momentum=.9)
optimizer = optim.Adam(net.parameters())
trainloader, _ = load_data()
dataiter = iter(trainloader)
# Train
for epoch in range(20):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data[0].to(device), data[1].to(device)
optimizer.zero_grad()
outputs = net(inputs) # forward
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print metrics
running_loss += loss.item()
if i % 938 == 937:
print(f'Epoch: {epoch}\t Loss: {running_loss/938}')
running_loss = 0.0
print('Finished Training\n \n')
filename = input('Enter file name for model: ')
torch.save(net, filename)
def train(learning_rate=0.1):
#加载训练数据
train_set,valid_set,test_set=mnist_data.load_data()
train_X=train_set[0]
train_y=train_set[1]
y_=np.zeros((train_y.shape[0],10))
y_[np.arange(train_y.shape[0]),train_y]=1
#计算过程
x=tf.placeholder('float',[None,784])
y=tf.placeholder('float',[None,10])
logistic=LogisticRegression(x,784,10)
#y=logistic.output
init=tf.initialize_all_variables()
#损失函数
loss=logistic.cross_entropy(y)
#优化
opitimizer=tf.train.GradientDescentOptimizer(learning_rate)
train=opitimizer.minimize(loss)
with tf.Session() as sess:
sess.run(init)
for i in range(1000):
r=sess.run([train,loss],feed_dict={x:train_X,y:y_})
print r[1]
def main():
filename = input('Enter file name for model: ')
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net = torch.load(filename)
_, testloader = load_data()
# Test
print('Starting Testing \n')
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data[0].to(device), data[1].to(device)
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f'Accuracy: {100*correct/total}')
def get_input():
(train_images, train_labels), (test_images,
test_labels) = mnist_data.load_data()
TRAINING_SIZE = len(train_images)
TEST_SIZE = len(test_images)
train_images = np.asarray(train_images, dtype=np.float32) / 255
# Convert the train images and add channels
train_images = train_images.reshape((TRAINING_SIZE, 28, 28, 1))
test_images = np.asarray(test_images, dtype=np.float32) / 255
# Convert the train images and add channels
test_images = test_images.reshape((TEST_SIZE, 28, 28, 1))
train_labels = tf.keras.utils.to_categorical(train_labels,
LABEL_DIMENSIONS)
test_labels = tf.keras.utils.to_categorical(test_labels, LABEL_DIMENSIONS)
# Cast the labels to floats, needed later
train_labels = train_labels.astype(np.float32)
test_labels = test_labels.astype(np.float32)
return train_images, train_labels, test_images, test_labels
#!/usr/bin/env python """MNIST with TFlearn.""" from __future__ import division, print_function, absolute_import import tflearn from tflearn.layers.core import input_data, dropout, fully_connected from tflearn.layers.conv import conv_2d, max_pool_2d # from tflearn.layers.normalization import local_response_normalization from tflearn.layers.estimator import regression from double_cnn import conv_2d_double # Data loading and preprocessing # import tflearn.datasets.mnist as mnist from mnist_data import load_data X, Y, testX, testY = load_data(one_hot=True) X = X.reshape([-1, 28, 28, 1]) testX = testX.reshape([-1, 28, 28, 1]) # Building convolutional network network = input_data(shape=[None, 28, 28, 1], name='input') network = conv_2d_double(network, 32, 3, activation='relu') # network = conv_2d(network, 32, 3, activation='relu') network = max_pool_2d(network, 2) # network = conv_2d(network, 32, 3, activation='relu') # network = max_pool_2d(network, 2) # network = conv_2d(network, 32, 3, activation='relu') # network = max_pool_2d(network, 2) # network = conv_2d(network, 50, 3, activation='relu') # network = tflearn.layers.conv.global_avg_pool(network, name='gap') network = fully_connected(network, 256, activation='tanh')