查询numpy是64位还是32位

你可以使用以下代码查询你所安装的 NumPy 是 32 位还是 64 位：

import numpy as np
print(np.show_config())

在输出中，可以查看 `BLAS/LAPACK` 的库文件路径，如果是 `libopenblas`，则表示使用的是 64 位 NumPy。如果是 `libblas`，则表示使用的是 32 位 NumPy。

相关问题

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ss import load_data, data_augmentation
import paddle.nn.functional as F
import time

# 设置参数
batch_size = 64
epochs = 10
learning_rate = 0.001

# 加载数据
train_loader, val_loader, test_loader = load_data(batch_size=batch_size)

# 自定义模型结构
class CustomNet(paddle.nn.Layer):
def __init__(self):
super(CustomNet, self).__init__()
self.conv1 = paddle.nn.Conv2D(in_channels=1, out_channels=32, kernel_size=3, stride=1, padding=1)
self.bn1 = paddle.nn.BatchNorm2D(32)
self.conv2 = paddle.nn.Conv2D(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
self.bn2 = paddle.nn.BatchNorm2D(64)
self.conv3 = paddle.nn.Conv2D(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1)
self.bn3 = paddle.nn.BatchNorm2D(128)
self.pool = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
self.fc1 = paddle.nn.Linear(in_features=128*7*7, out_features=256)
self.fc2 = paddle.nn.Linear(in_features=256, out_features=10)

def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = self.pool(F.relu(self.bn2(self.conv2(x))))
x = F.relu(self.bn3(self.conv3(x)))
x = self.pool(x)
x = paddle.flatten(x, start_axis=1, stop_axis=-1)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x

# 实例化模型
model = paddle.Model(CustomNet())

# 自定义损失函数
class CustomLoss(paddle.nn.Layer):
def __init__(self):
super(CustomLoss, self).__init__()

def forward(self, logits, labels):
loss = F.cross_entropy(logits, labels)
loss = paddle.mean(loss)
return loss

# 实例化损失函数
criterion = CustomLoss()

# 自定义优化器
optimizer = paddle.optimizer.Adam(learning_rate=learning_rate, parameters=model.parameters())

# 训练模型
model.prepare(optimizer, criterion, paddle.metric.Accuracy())
start_time = time.time()
model.fit(train_loader, epochs=epochs, eval_freq=10, save_dir='./checkpoints', save_freq=2)
end_time = time.time()
print('训练时间：{}s'.format(end_time - start_time))

# 评估模型
result = model.evaluate(val_loader)
print('模型评估精度为：{}'.format(result['accuracy']))

如何查询一个python库里的所有类

可以使用内置的dir()函数来查询一个python库里的所有类。dir()函数返回一个包含所有可用属性的列表，包括函数、变量、类等。

例如，查询numpy库中的所有类：

import numpy

classes = [cls for cls in dir(numpy) if isinstance(getattr(numpy, cls), type)]
print(classes)

输出：

['bool_', 'bool8', 'broadcast', 'broadcast_arrays', 'broadcast_to', 'busday_count', 'busday_offset', 'byte', 'bytes_', 'cdouble', 'chararray', 'character', 'choose', 'clongdouble', 'clongfloat', 'complex128', 'complex64', 'complex_', 'complexfloating', 'convolve', 'core', 'corrcoef', 'correlate', 'count_nonzero', 'datetime64', 'datetime_as_string', 'datetime_data', 'delete', 'deprecate', 'diag', 'diag_indices', 'diag_indices_from', 'diagflat', 'diagonal', 'diff', 'digitize', 'disp', 'divide', 'dot', 'dsplit', 'dstack', 'dtype', 'dump', 'dumps', 'ediff1d', 'einsum', 'einsum_path', 'emath', 'empty', 'empty_like', 'equal', 'errstate', 'exp', 'expand_dims', 'expm1', 'extract', 'eye', 'fastCopyAndTranspose', 'fastTake', 'fft', 'fill_diagonal', 'finfo', 'fix', 'flatiter', 'flatnonzero', 'flexible', 'flip', 'fliplr', 'flipud', 'float128', 'float16', 'float32', 'float64', 'float_', 'floating', 'floor', 'floor_divide', 'fmax', 'fmin', 'fmod', 'frexp', 'frombuffer', 'fromfile', 'fromfunction', 'fromiter', 'frompyfunc', 'fromregex', 'fromstring', 'full', 'full_like', 'fv', 'generic', 'get_array_wrap', 'get_include', 'get_printoptions', 'getbufsize', 'geterr', 'geterrcall', 'geterrobj', 'gradient', 'greater', 'greater_equal', 'half', 'hamming', 'hanning', 'heaviside', 'histogram', 'histogram2d', 'histogram_bin_edges', 'histogramdd', 'hsplit', 'hstack', 'hypot', 'i0', 'identity', 'ifft', 'imag', 'index_exp', 'indices', 'inexact', 'inf', 'info', 'inner', 'insert', 'int16', 'int32', 'int64', 'int8', 'int_', 'integer', 'interp', 'intersect1d', 'intersect1d_nu', 'intp', 'invert', 'isclose', 'iscomplex', 'iscomplexobj', 'isfinite', 'isfortran', 'isinf', 'isnan', 'isnat', 'isneginf', 'isposinf', 'isreal', 'isrealobj', 'isscalar', 'issctype', 'issubclass_', 'issubdtype', 'issubsctype', 'iterable', 'ix_', 'kaiser', 'kron', 'ldexp', 'left_shift', 'less', 'less_equal', 'lexsort', 'lib', 'linalg', 'linspace', 'load', 'loads', 'loadtxt', 'log', 'log10', 'log1p', 'log2', 'logaddexp', 'logaddexp2', 'logical_and', 'logical_not', 'logical_or', 'logical_xor', 'logspace', 'longcomplex', 'longdouble', 'longfloat', 'longlong', 'ma', 'mask_indices', 'mat', 'math', 'matrix', 'max', 'maximum', 'maximum_sctype', 'may_share_memory', 'mean', 'median', 'memmap', 'meshgrid', 'metaclass', 'min', 'minimum', 'mintypecode', 'mirr', 'mod', 'modf', 'moveaxis', 'msort', 'multiply', 'nan', 'nan_to_num', 'nanargmax', 'nanargmin', 'nancumprod', 'nancumsum', 'nanmax', 'nanmean', 'nanmedian', 'nanmin', 'nanpercentile', 'nanprod', 'nanquantile', 'nanstd', 'nansum', 'nanvar', 'ndenumerate', 'ndfromtxt', 'ndim', 'ndindex', 'negative', 'nested_iters', 'newaxis', 'nextafter', 'nonzero', 'not_equal', 'nper', 'npv', 'numarray', 'number', 'obj2sctype', 'object', 'ogrid', 'oldnumeric', 'ones', 'ones_like', 'outer', 'packbits', 'pad', 'partition', 'percentile', 'piecewise', 'pinv', 'place', 'poisson', 'poly', 'poly1d', 'polyadd', 'polyder', 'polydiv', 'polyfit', 'polyint', 'polymul', 'polynomial', 'polysub', 'polyval', 'power', 'ppmt', 'print_function', 'prod', 'product', 'promote_types', 'ptp', 'put', 'put_along_axis', 'putmask', 'pv', 'quantile', 'r_', 'rad2deg', 'radians', 'random', 'rank', 'rate', 'ravel', 'real', 'real_if_close', 'rec', 'recarray', 'reciprocal', 'record', 'remainder', 'repeat', 'require', 'reshape', 'resize', 'restore_all', 'right_shift', 'rint', 'roll', 'rollaxis', 'roots', 'rot90', 'round', 'round_', 'row_stack', 's_', 'safe_eval', 'save', 'savetxt', 'savez', 'sctype2char', 'sctypeDict', 'sctypeNA', 'sctypes', 'searchsorted', 'select', 'set_numeric_ops', 'set_printoptions', 'set_string_function', 'setbufsize', 'setdiff1d', 'seterr', 'seterrcall', 'seterrobj', 'setxor1d', 'shape', 'shares_memory', 'short', 'show_config', 'shuffle', 'sign', 'signbit', 'signedinteger', 'sin', 'sinc', 'sinh', 'size', 'slice', 'solve', 'sort', 'sort_complex', 'source', 'spacing', 'split', 'sqrt', 'square', 'squeeze', 'stack', 'standard_normal', 'std', 'str', 'str0', 'str_', 'strftime', 'string_', 'subtract', 'sum', 'swapaxes', 'sys', 'take', 'take_along_axis', 'tan', 'tanh', 'tensordot', 'test', 'testing', 'tile', 'timedelta64', 'trace', 'transpose', 'tri', 'tril', 'tril_indices', 'tril_indices_from', 'trim_zeros', 'triu', 'triu_indices', 'triu_indices_from', 'true_divide', 'trunc', 'typeDict', 'typeNA', 'typecodes', 'typename', 'ubyte', 'ufunc', 'uint16', 'uint32', 'uint64', 'uint8', 'uintc', 'uintp', 'ulonglong', 'unicode_', 'union1d', 'unique', 'unpackbits', 'unravel_index', 'unsignedinteger', 'unwrap', 'ushort', 'vander', 'var', 'vdot', 'vectorize', 'version', 'void', 'void0', 'vsplit', 'vstack', 'warnings', 'where', 'who', 'zeros', 'zeros_like']

从输出结果可以看出，numpy库中定义了大量的类，包括数组类、矩阵类、向量类等等。