本文整理汇总了Python中mnist.MNIST属性的典型用法代码示例。如果您正苦于以下问题：Python mnist.MNIST属性的具体用法？Python mnist.MNIST怎么用？Python mnist.MNIST使用的例子？那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类mnist的用法示例。
在下文中一共展示了mnist.MNIST属性的5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: __init__
# 需要导入模块: import mnist [as 别名]
# 或者: from mnist import MNIST [as 别名]
def __init__(self, loc='data/raw'):
"""
Dataloader for the MNIST data. Relies on this library
https://pypi.python.org/pypi/python-mnist/0.3
:param loc:
"""
mndata = MNIST(loc)
self.data = {}
# train data
images, labels = mndata.load_training()
images = np.array(images)
labels = np.array(labels).astype(np.int64)
self.data['X_train'] = self.normalize(images)
self.data['y_train'] = labels
# test data
images, labels = mndata.load_testing()
images = np.array(images)
labels = np.array(labels).astype(np.int64)
self.data['X_test'] = self.normalize(images)
self.data['y_test'] = labels 示例2: initialize_parameters
# 需要导入模块: import mnist [as 别名]
# 或者: from mnist import MNIST [as 别名]
def initialize_parameters():
mnist_common = mnist.MNIST(mnist.file_path,
'mnist_params.txt',
'keras',
prog='mnist_mlp',
desc='MNIST example'
)
# Initialize parameters
gParameters = candle.finalize_parameters(mnist_common)
csv_logger = CSVLogger('{}/params.log'.format(gParameters))
return gParameters 示例3: initialize_parameters
# 需要导入模块: import mnist [as 别名]
# 或者: from mnist import MNIST [as 别名]
def initialize_parameters():
mnist_common = mnist.MNIST(mnist.file_path,
'mnist_params.txt',
'keras',
prog='mnist_cnn',
desc='MNIST CNN example'
)
# Initialize parameters
gParameters = candle.finalize_parameters(mnist_common)
csv_logger = CSVLogger('{}/params.log'.format(gParameters))
return gParameters 示例4: __init__
# 需要导入模块: import mnist [as 别名]
# 或者: from mnist import MNIST [as 别名]
def __init__(self, prob):
"""load mnist dataset"""
print("Loading MNIST dataset...")
mndata = MNIST('./data/mnist/')
mnist_train_images, mnist_train_labels = mndata.load_training()
mnist_train_images = np.asarray(mnist_train_images)
mnist_train_images = normalize(mnist_train_images)
mnist_train_labels = np.asarray(mnist_train_labels)
"""divide datset by label"""
print("Dividing dataset...")
sorted_train_images = []
sorted_train_labels = []
for label in range(0,10):
train_index = np.where(mnist_train_labels == label)
sorted_train_images.append(mnist_train_images[train_index[0]])
sorted_train_labels.append(np.asarray([label] * len(train_index[0])))
"""add salt_and_pepper noise"""
print("Adding salt and pepper noise...")
shape = 28 * 28 ##image shape of mnist
self.train_images = []
self.train_labels = sorted_train_labels
for images in sorted_train_images:
noise_images = []
for image in images:
noise_image = salt_and_pepper(image, prob, shape)
noise_images.append(noise_image)
self.train_images.append(noise_images) 示例5: run
# 需要导入模块: import mnist [as 别名]
# 或者: from mnist import MNIST [as 别名]
def run():
TorchModel = PyTorchHelpers.load_lua_class('torch_model.lua', 'TorchModel')
torchModel = TorchModel(backend, 28, 10)
mndata = MNIST('../../data/mnist')
imagesList, labelsList = mndata.load_training()
labels = np.array(labelsList, dtype=np.uint8)
images = np.array(imagesList, dtype=np.float32)
labels += 1 # since torch/lua labels are 1-based
N = labels.shape[0]
print('loaded mnist training data')
if numTrain > 0:
N = min(N, numTrain)
print('numExamples N', N)
numBatches = N // batchSize
for epoch in range(numEpochs):
epochLoss = 0
epochNumRight = 0
for b in range(numBatches):
res = torchModel.trainBatch(
learningRate,
images[b * batchSize:(b+1) * batchSize],
labels[b * batchSize:(b+1) * batchSize])
# print('res', res)
numRight = res['numRight']
loss = res['loss']
epochNumRight += numRight
epochLoss += loss
print('epoch ' + str(epoch) + ' batch ' + str(b) + ' accuracy: ' + str(numRight * 100.0 / batchSize) + '%')
print('epoch ' + str(epoch) + ' accuracy: ' + str(epochNumRight * 100.0 / N) + '%')
print('finished training')
print('loading test data...')
imagesList, labelsList = mndata.load_testing()
labels = np.array(labelsList, dtype=np.uint8)
images = np.array(imagesList, dtype=np.float32)
labels += 1 # since torch/lua labels are 1-based
N = labels.shape[0]
print('loaded mnist testing data')
numBatches = N // batchSize
epochLoss = 0
epochNumRight = 0
for b in range(numBatches):
predictions = torchModel.predict(images[b * batchSize:(b+1) * batchSize]).asNumpyTensor().reshape(batchSize)
labelsBatch = labels[b * batchSize:(b+1) * batchSize]
numRight = (predictions == labelsBatch).sum()
epochNumRight += numRight
print('test results: accuracy: ' + str(epochNumRight * 100.0 / N) + '%')