本文整理匯總了Python中cntk.times方法的典型用法代碼示例。如果您正苦於以下問題：Python cntk.times方法的具體用法？Python cntk.times怎麽用？Python cntk.times使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類cntk的用法示例。
在下文中一共展示了cntk.times方法的11個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: linear_layer
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import times [as 別名]
def linear_layer(input_var, output_dim):
input_dim = input_var.shape[0]
weight = C.parameter(shape=(input_dim, output_dim))
bias = C.parameter(shape=(output_dim))
return bias + C.times(input_var, weight) 示例2: linear_layer
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import times [as 別名]
def linear_layer(input_var, output_dim):
input_dim = input_var.shape[0]
weight_param = C.parameter(shape=(input_dim, output_dim))
bias_param = C.parameter(shape=(output_dim))
return C.times(input_var, weight_param) + bias_param 示例3: test_times_1
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import times [as 別名]
def test_times_1():
cntk_op = C.times([1, 2, 3], [[4], [5], [6]])
cntk_ret = cntk_op.eval()
ng_op, _ = CNTKImporter().import_model(cntk_op)
ng_ret = ng.transformers.make_transformer().computation(ng_op)()
assert np.array_equiv(cntk_ret, ng_ret) 示例4: test_times_2
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import times [as 別名]
def test_times_2():
cntk_op = C.times([[1, 2], [3, 4]], [[5, 6], [7, 8]])
cntk_ret = cntk_op.eval()
ng_op, _ = CNTKImporter().import_model(cntk_op)
ng_ret = ng.transformers.make_transformer().computation(ng_op)()
assert np.array_equiv(cntk_ret, ng_ret) 示例5: test_times_3
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import times [as 別名]
def test_times_3():
cntk_op = C.times([1, 2, 3], [[4, 5], [6, 7], [8, 9]])
cntk_ret = cntk_op.eval()
ng_op, _ = CNTKImporter().import_model(cntk_op)
ng_ret = ng.transformers.make_transformer().computation(ng_op)()
assert np.array_equiv(cntk_ret, ng_ret) 示例6: test_times_5
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import times [as 別名]
def test_times_5():
cntk_op = C.times([[1, 2, 3], [4, 5, 6]], [[7, 8], [9, 10], [11, 12]])
cntk_ret = cntk_op.eval()
ng_op, _ = CNTKImporter().import_model(cntk_op)
ng_ret = ng.transformers.make_transformer().computation(ng_op)()
assert np.array_equiv(cntk_ret, ng_ret) 示例7: test_times_6
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import times [as 別名]
def test_times_6():
cntk_op = C.times([[1, 2], [3, 4], [5, 6]], [[7, 8, 9], [10, 11, 12]])
cntk_ret = cntk_op.eval()
ng_op, _ = CNTKImporter().import_model(cntk_op)
ng_ret = ng.transformers.make_transformer().computation(ng_op)()
assert np.array_equiv(cntk_ret, ng_ret) 示例8: dot
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import times [as 別名]
def dot(x, y):
if len(x.shape) > 2 or len(y.shape) > 2:
y_shape = int_shape(y)
if len(y_shape) > 2:
permutation = [len(y_shape) - 2]
permutation += list(range(len(y_shape) - 2))
permutation += [len(y_shape) - 1]
y = C.transpose(y, perm=permutation)
return C.times(x, y, len(y_shape) - 1)
else:
return C.times(x, y) 示例9: batch_dot
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import times [as 別名]
def batch_dot(x, y, axes=None):
x_shape = int_shape(x)
y_shape = int_shape(y)
if isinstance(axes, int):
axes = (axes, axes)
if axes is None:
# behaves like tf.batch_matmul as default
axes = [len(x_shape) - 1, len(y_shape) - 2]
if b_any([isinstance(a, (list, tuple)) for a in axes]):
raise ValueError('Multiple target dimensions are not supported. ' +
'Expected: None, int, (int, int), ' +
'Provided: ' + str(axes))
if len(x_shape) == 2 and len(y_shape) == 2:
if axes[0] == axes[1]:
result = sum(x * y, axis=axes[0], keepdims=True)
return result if axes[0] == 1 else transpose(result)
else:
return sum(x * transpose(y), axis=axes[0], keepdims=True)
else:
if len(y_shape) == 2:
y = expand_dims(y)
normalized_axis = []
normalized_axis.append(_normalize_axis(axes[0], x)[0])
normalized_axis.append(_normalize_axis(axes[1], y)[0])
# transpose
i = normalized_axis[0]
while i < len(x.shape) - 1:
x = C.swapaxes(x, i, i + 1)
i += 1
i = normalized_axis[1]
while i > 0:
y = C.swapaxes(y, i, i - 1)
i -= 1
result = C.times(x, y, output_rank=(len(y.shape) - 1)
if len(y.shape) > 1 else 1)
if len(y_shape) == 2:
result = squeeze(result, -1)
return result 示例10: gather
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import times [as 別名]
def gather(reference, indices):
# There is a bug in cntk gather op which may cause crash.
# We have made a fix but not catched in CNTK 2.1 release.
# Will update with gather op in next release
if _get_cntk_version() >= 2.2:
return C.ops.gather(reference, indices)
else:
num_classes = reference.shape[0]
one_hot_matrix = C.ops.one_hot(indices, num_classes)
return C.times(one_hot_matrix, reference, output_rank=len(reference.shape) - 1) 示例11: batch_dot
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import times [as 別名]
def batch_dot(x, y, axes=None):
x_shape = int_shape(x)
y_shape = int_shape(y)
if isinstance(axes, int):
axes = (axes, axes)
if axes is None:
# behaves like tf.batch_matmul as default
axes = [len(x_shape) - 1, len(y_shape) - 2]
if len(x_shape) == 2 and len(y_shape) == 2:
return sum(x * y, axis=1, keepdims=True)
else:
if len(y_shape) == 2:
y = expand_dims(y)
normalized_axis = []
normalized_axis.append(_normalize_axis(axes[0], x)[0])
normalized_axis.append(_normalize_axis(axes[1], y)[0])
# transpose
i = normalized_axis[0]
while i < len(x.shape) - 1:
x = C.swapaxes(x, i, i + 1)
i += 1
i = normalized_axis[1]
while i > 0:
y = C.swapaxes(y, i, i - 1)
i -= 1
result = C.times(x, y, output_rank=(len(y.shape) - 1)
if len(y.shape) > 1 else 1)
if len(y_shape) == 2:
result = squeeze(result, -1)
return result