def evaluate_lenet5(learning_rate=0.1, n_epochs=200, dataset='mnist.pkl.gz', nkerns=[20, 50], batch_size=500):
rng = numpy.random.RandomState(23455)
datasets = load_data(dataset) #加载数据
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
n_train_batches /= batch_size
n_valid_batches /= batch_size
n_test_batches /= batch_size
# 定义几个变量，index表示batch下标，x表示输入的训练数据，y对应其标签
index = T.lscalar()
x = T.matrix('x')
y = T.ivector('y')
############
# 构建模型 #
############
print '... building the model'
# 我们加载进来的batch大小的数据是(batch_size, 28 * 28)，但是LeNetConvPoolLayer的输入是四维的，所以要reshape
layer0_input = x.reshape((batch_size, 1, 28, 28))
# layer0即第一个LeNetConvPoolLayer层
# 输入的单张图片(28,28)，经过conv得到(28-5+1 , 28-5+1) = (24, 24)，
# 经过maxpooling得到(24/2, 24/2) = (12, 12)
# 因为每个batch有batch_size张图，第一个LeNetConvPoolLayer层有nkerns[0]个卷积核，
# 故layer0输出为(batch_size, nkerns[0], 12, 12)
layer0 = LeNetConvPoolLayer(
rng, input=layer0_input,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2)
)
# layer1即第二个LeNetConvPoolLayer层
# 输入是layer0的输出，每张特征图为(12,12),经过conv得到(12-5+1, 12-5+1) = (8, 8),
# 经过maxpooling得到(8/2, 8/2) = (4, 4)
# 因为每个batch有batch_size张图（特征图），第二个LeNetConvPoolLayer层有nkerns[1]个卷积核
# ，故layer1输出为(batch_size, nkerns[1], 4, 4)
layer1 = LeNetConvPoolLayer(
rng, input=layer0.output,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2)
)
#前面定义好了两个LeNetConvPoolLayer（layer0和layer1），layer1后面接layer2，这是一个全连接层，相当于MLP里面的隐含层
#故可以用MLP中定义的HiddenLayer来初始化layer2，layer2的输入是二维的(batch_size, num_pixels) ，
#故要将上层中同一张图经不同卷积核卷积出来的特征图合并为一维向量，
#也就是将layer1的输出(batch_size, nkerns[1], 4, 4)flatten为(batch_size, nkerns[1]*4*4)=(500，800),作为layer2的输入。
#(500，800)表示有500个样本，每一行代表一个样本。layer2的输出大小是(batch_size,n_out)=(500,500)
layer2_input = layer1.output.flatten(2)
layer2 = HiddenLayer(
rng,
input=layer2_input,
n_in=nkerns[1] * 4 * 4,
n_out=500,
activation=T.tanh
)
# 最后一层layer3是分类层，用的是逻辑回归中定义的LogisticRegression，
# layer3的输入是layer2的输出(500,500)，layer3的输出就是(batch_size,n_out)=(500,10)
layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
# 代价函数NLL
cost = layer3.negative_log_likelihood(y)
# test_model计算测试误差，x、y根据给定的index具体化，然后调用layer3，
# layer3又会逐层地调用layer2、layer1、layer0，故test_model其实就是整个CNN结构，
# test_model的输入是x、y，输出是layer3.errors(y)的输出，即误差。
test_model = theano.function(
[index],
layer3.errors(y),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
y: test_set_y[index * batch_size: (index + 1) * batch_size]
}
)
validate_model = theano.function(
[index],
layer3.errors(y),
givens={
x: valid_set_x[index * batch_size: (index + 1) * batch_size],
y: valid_set_y[index * batch_size: (index + 1) * batch_size]
}
)
# 下面是train_model，涉及到优化算法即SGD，需要计算梯度、更新参数
params = layer3.params + layer2.params + layer1.params + layer0.params # 参数集
grads = T.grad(cost, params) # 对各个参数的梯度
# 因为参数太多，在updates规则里面一个一个具体地写出来是很麻烦的，
# 所以下面用了一个for..in..,自动生成规则对(param_i, param_i - learning_rate * grad_i)
updates = [ (param_i, param_i - learning_rate * grad_i) for param_i, grad_i in zip(params, grads) ]
#train_model，代码分析同test_model。train_model里比test_model、validation_model多出updates规则
train_model = theano.function(
[index],
cost,
updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size]
}
)
############
# 训练模型 #
############
print '... training'
patience = 10000 # 提早终止参数
patience_increase = 2
improvement_threshold = 0.995
# 这样设置validation_frequency可以保证每一次epoch都会在验证集上测试。
validation_frequency = min(n_train_batches, patience / 2)
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
if iter % 100 == 0:
print 'training @ iter = ', iter
cost_ij = train_model(minibatch_index)
if (iter + 1) % validation_frequency == 0:
validation_losses = [validate_model(i) for i in xrange(n_valid_batches)]
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches, this_validation_loss * 100.))
if this_validation_loss < best_validation_loss:
if this_validation_loss < best_validation_loss * improvement_threshold:
patience = max(patience, iter * patience_increase)
best_validation_loss = this_validation_loss
best_iter = iter
test_losses = [ test_model(i) for i in xrange(n_test_batches) ]
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of ' 'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches, test_score * 100.))
if patience <= iter:
done_looping = True
break
layer0.save_net("layer0")
layer1.save_net("layer1")
layer2.save_net("layer2")
layer3.save_net("layer3")
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i, ' 'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] + ' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.1,
n_epochs=200,
dataset='mnist.pkl.gz',
nkerns=[20, 50],
batch_size=500):
rng = numpy.random.RandomState(23455)
datasets = load_data(dataset) #加载数据
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
n_train_batches /= batch_size
n_valid_batches /= batch_size
n_test_batches /= batch_size
# 定义几个变量，index表示batch下标，x表示输入的训练数据，y对应其标签
index = T.lscalar()
x = T.matrix('x')
y = T.ivector('y')
############
# 构建模型 #
############
print '... building the model'
# 我们加载进来的batch大小的数据是(batch_size, 28 * 28)，但是LeNetConvPoolLayer的输入是四维的，所以要reshape
layer0_input = x.reshape((batch_size, 1, 28, 28))
# layer0即第一个LeNetConvPoolLayer层
# 输入的单张图片(28,28)，经过conv得到(28-5+1 , 28-5+1) = (24, 24)，
# 经过maxpooling得到(24/2, 24/2) = (12, 12)
# 因为每个batch有batch_size张图，第一个LeNetConvPoolLayer层有nkerns[0]个卷积核，
# 故layer0输出为(batch_size, nkerns[0], 12, 12)
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2))
# layer1即第二个LeNetConvPoolLayer层
# 输入是layer0的输出，每张特征图为(12,12),经过conv得到(12-5+1, 12-5+1) = (8, 8),
# 经过maxpooling得到(8/2, 8/2) = (4, 4)
# 因为每个batch有batch_size张图（特征图），第二个LeNetConvPoolLayer层有nkerns[1]个卷积核
# ，故layer1输出为(batch_size, nkerns[1], 4, 4)
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2))
#前面定义好了两个LeNetConvPoolLayer（layer0和layer1），layer1后面接layer2，这是一个全连接层，相当于MLP里面的隐含层
#故可以用MLP中定义的HiddenLayer来初始化layer2，layer2的输入是二维的(batch_size, num_pixels) ，
#故要将上层中同一张图经不同卷积核卷积出来的特征图合并为一维向量，
#也就是将layer1的输出(batch_size, nkerns[1], 4, 4)flatten为(batch_size, nkerns[1]*4*4)=(500，800),作为layer2的输入。
#(500，800)表示有500个样本，每一行代表一个样本。layer2的输出大小是(batch_size,n_out)=(500,500)
layer2_input = layer1.output.flatten(2)
layer2 = HiddenLayer(rng,
input=layer2_input,
n_in=nkerns[1] * 4 * 4,
n_out=500,
activation=T.tanh)
# 最后一层layer3是分类层，用的是逻辑回归中定义的LogisticRegression，
# layer3的输入是layer2的输出(500,500)，layer3的输出就是(batch_size,n_out)=(500,10)
layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
# 代价函数NLL
cost = layer3.negative_log_likelihood(y)
# test_model计算测试误差，x、y根据给定的index具体化，然后调用layer3，
# layer3又会逐层地调用layer2、layer1、layer0，故test_model其实就是整个CNN结构，
# test_model的输入是x、y，输出是layer3.errors(y)的输出，即误差。
test_model = theano.function(
[index],
layer3.errors(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size]
})
validate_model = theano.function(
[index],
layer3.errors(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size]
})
# 下面是train_model，涉及到优化算法即SGD，需要计算梯度、更新参数
params = layer3.params + layer2.params + layer1.params + layer0.params # 参数集
grads = T.grad(cost, params) # 对各个参数的梯度
# 因为参数太多，在updates规则里面一个一个具体地写出来是很麻烦的，
# 所以下面用了一个for..in..,自动生成规则对(param_i, param_i - learning_rate * grad_i)
updates = [(param_i, param_i - learning_rate * grad_i)
for param_i, grad_i in zip(params, grads)]
#train_model，代码分析同test_model。train_model里比test_model、validation_model多出updates规则
train_model = theano.function(
[index],
cost,
updates=updates,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]
})
############
# 训练模型 #
############
print '... training'
patience = 10000 # 提早终止参数
patience_increase = 2
improvement_threshold = 0.995
# 这样设置validation_frequency可以保证每一次epoch都会在验证集上测试。
validation_frequency = min(n_train_batches, patience / 2)
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
if iter % 100 == 0:
print 'training @ iter = ', iter
cost_ij = train_model(minibatch_index)
if (iter + 1) % validation_frequency == 0:
validation_losses = [
validate_model(i) for i in xrange(n_valid_batches)
]
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
if this_validation_loss < best_validation_loss:
if this_validation_loss < best_validation_loss * improvement_threshold:
patience = max(patience, iter * patience_increase)
best_validation_loss = this_validation_loss
best_iter = iter
test_losses = [
test_model(i) for i in xrange(n_test_batches)
]
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
if patience <= iter:
done_looping = True
break
layer0.save_net("layer0")
layer1.save_net("layer1")
layer2.save_net("layer2")
layer3.save_net("layer3")
end_time = time.clock()
print('Optimization complete.')
print(
'Best validation score of %f %% obtained at iteration %i, '
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
