本文整理汇总了Python中logistic_sgd.load_data方法的典型用法代码示例。如果您正苦于以下问题：Python logistic_sgd.load_data方法的具体用法？Python logistic_sgd.load_data怎么用？Python logistic_sgd.load_data使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类logistic_sgd的用法示例。
在下文中一共展示了logistic_sgd.load_data方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_stacked_autoencoder
# 需要导入模块: import logistic_sgd [as 别名]
# 或者: from logistic_sgd import load_data [as 别名]
def test_stacked_autoencoder(finetune_lr=0.1, pretraining_epochs=15,
pretrain_lr=0.001, training_epochs=100,
dataset='mnist.pkl.gz', batch_size=1,
hidden_layers_sizes=[1000, 1000, 1000],
corruption_levels=[0.1, 0.2, 0.3],
pretrain_flag=True,
testerr_file='test_error.txt'):
datasets = load_data("../data/mnist.pkl.gz")
train_set_x = datasets[0][0]
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
numpy_rng = np.random.RandomState(89677)
print "building the model ..."
sda = StackedDenoisingAutoencoder(
numpy_rng,
28 * 28,
hidden_layers_sizes,
10,
corruption_levels)
# Pre-training
if pretrain_flag:
print "getting the pre-training functions ..."
pretraining_functions = sda.pretraining_functions(train_set_x=train_set_x,
batch_size=batch_size)
print "pre-training the model ..."
for i in xrange(sda.n_layers):
for epoch in xrange(pretraining_epochs):
c = []
for batch_index in xrange(n_train_batches):
c.append(pretraining_functions[i](index=batch_index,
corruption=corruption_levels[i],
lr=pretrain_lr))
print "Pre-training layer %i, epoch %d, cost %f" % (i, epoch, np.mean(c))
# Fine-tuning
print "getting the fine-tuning functions ..."
train_model, _, test_model = sda.build_finetune_functions(
datasets=datasets,
batch_size=batch_size,
learning_rate=finetune_lr
)
print "fine-tuning the model ..."
epoch = 0
fp = open(testerr_file, "w")
while (epoch < training_epochs):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
train_model(minibatch_index)
test_losses = test_model()
test_score = np.mean(test_losses)
print "Fine-tuning, epoch %d, test error %f" % (epoch, test_score * 100)
fp.write("%d\t%f\n" % (epoch, test_score * 100))
fp.close()