Python cntk.splice() Examples

def _padding(x, pattern, axis):
    base_shape = x.shape
    if b_any([dim < 0 for dim in base_shape]):
        raise ValueError('CNTK Backend: padding input tensor with '
                         'shape `%s` contains non-specified dimension, '
                         'which is not supported. Please give fixed '
                         'dimension to enable padding.' % base_shape)
    if pattern[0] > 0:
        prefix_shape = list(base_shape)
        prefix_shape[axis] = pattern[0]
        prefix_shape = tuple(prefix_shape)
        x = C.splice(C.constant(value=0, shape=prefix_shape), x, axis=axis)
        base_shape = x.shape
    if pattern[1] > 0:
        postfix_shape = list(base_shape)
        postfix_shape[axis] = pattern[1]
        postfix_shape = tuple(postfix_shape)
        x = C.splice(x, C.constant(value=0, shape=postfix_shape), axis=axis)
    return x 

def repeat(x, n):
    # this is a workaround for recurrent layer
    # if n is inferred dimension,
    # we can't figure out how to repeat it in cntk now
    # return the same x to take cntk broadcast feature
    # to make the recurrent layer work.
    # need to be fixed in GA.
    if n is C.InferredDimension or n is C.FreeDimension:
        return x
    index = 1 - _get_dynamic_axis_num(x)
    if index < 0 or index > 1:
        raise NotImplementedError

    new_shape = list(x.shape)
    new_shape.insert(index, 1)
    new_shape = tuple(new_shape)
    x = C.reshape(x, new_shape)
    temp = [x] * n
    return C.splice(*temp, axis=index) 

def _padding(x, pattern, axis):
    base_shape = x.shape
    if b_any([dim < 0 for dim in base_shape]):
        raise ValueError('CNTK Backend: padding input tensor with '
                         'shape `%s` contains non-specified dimension, '
                         'which is not supported. Please give fixed '
                         'dimension to enable padding.' % base_shape)
    if pattern[0] > 0:
        prefix_shape = list(base_shape)
        prefix_shape[axis] = pattern[0]
        prefix_shape = tuple(prefix_shape)
        x = C.splice(C.constant(value=0, shape=prefix_shape), x, axis=axis)
        base_shape = x.shape
    if pattern[1] > 0:
        postfix_shape = list(base_shape)
        postfix_shape[axis] = pattern[1]
        postfix_shape = tuple(postfix_shape)
        x = C.splice(x, C.constant(value=0, shape=postfix_shape), axis=axis)
    return x 

def tile(x, n):
    if isinstance(n, int):
        n = (n,)
    elif isinstance(n, list):
        n = tuple(n)

    shape = int_shape(x)
    num_dynamic_axis = _get_dynamic_axis_num(x)
    # Padding the axis
    if len(n) < len(shape):
        n = tuple([1 for _ in range(len(shape) - len(n))]) + n

    if len(n) != len(shape):
        raise NotImplementedError

    i = num_dynamic_axis
    for i, rep in enumerate(n):
        if i >= num_dynamic_axis and shape[i] is not None:
            tmp = [x] * rep
            x = C.splice(*tmp, axis=i - num_dynamic_axis)
        i += 1

    return x 

def tile(x, n):
    if isinstance(n, int):
        n = (n,)
    elif isinstance(n, list):
        n = tuple(n)

    shape = int_shape(x)
    num_dynamic_axis = _get_dynamic_axis_num(x)
    # Padding the axis
    if len(n) < len(shape):
        n = tuple([1 for _ in range(len(shape) - len(n))]) + n

    if len(n) != len(shape):
        raise NotImplementedError

    i = num_dynamic_axis
    for i, rep in enumerate(n):
        if i >= num_dynamic_axis and shape[i] is not None:
            tmp = [x] * rep
            x = C.splice(*tmp, axis=i - num_dynamic_axis)
        i += 1

    return x 

def repeat(x, n):
    # this is a workaround for recurrent layer
    # if n is inferred dimension,
    # we can't figure out how to repeat it in cntk now
    # return the same x to take cntk broadcast feature
    # to make the recurrent layer work.
    # need to be fixed in GA.
    if n is C.InferredDimension or n is C.FreeDimension:
        return x
    index = 1 - _get_dynamic_axis_num(x)
    if index < 0 or index > 1:
        raise NotImplementedError

    new_shape = list(x.shape)
    new_shape.insert(index, 1)
    new_shape = tuple(new_shape)
    x = C.reshape(x, new_shape)
    temp = [x] * n
    return C.splice(*temp, axis=index) 

def _padding(x, pattern, axis):
    base_shape = x.shape
    if b_any([dim < 0 for dim in base_shape]):
        raise ValueError('CNTK Backend: padding input tensor with '
                         'shape `%s` contains non-specified dimension, '
                         'which is not supported. Please give fixed '
                         'dimension to enable padding.' % base_shape)
    if pattern[0] > 0:
        prefix_shape = list(base_shape)
        prefix_shape[axis] = pattern[0]
        prefix_shape = tuple(prefix_shape)
        x = C.splice(C.constant(value=0, shape=prefix_shape), x, axis=axis)
        base_shape = x.shape
    if pattern[1] > 0:
        postfix_shape = list(base_shape)
        postfix_shape[axis] = pattern[1]
        postfix_shape = tuple(postfix_shape)
        x = C.splice(x, C.constant(value=0, shape=postfix_shape), axis=axis)
    return x 

def tile(x, n):
    if isinstance(n, int):
        n = (n,)
    elif isinstance(n, list):
        n = tuple(n)

    shape = int_shape(x)
    num_dynamic_axis = _get_dynamic_axis_num(x)
    # Padding the axis
    if len(n) < len(shape):
        n = tuple([1 for _ in range(len(shape) - len(n))]) + n

    if len(n) != len(shape):
        raise NotImplementedError

    i = num_dynamic_axis
    for i, rep in enumerate(n):
        if i >= num_dynamic_axis and shape[i] is not None:
            tmp = [x] * rep
            x = C.splice(*tmp, axis=i - num_dynamic_axis)
        i += 1

    return x 

def tile(x, n):
    if isinstance(n, int):
        n = (n,)
    elif isinstance(n, list):
        n = tuple(n)

    shape = int_shape(x)
    num_dynamic_axis = _get_dynamic_axis_num(x)
    # Padding the axis
    if len(n) < len(shape):
        n = tuple([1 for _ in range(len(shape) - len(n))]) + n

    if len(n) != len(shape):
        raise NotImplementedError

    i = num_dynamic_axis
    for i, rep in enumerate(n):
        if i >= num_dynamic_axis and shape[i] is not None:
            tmp = [x] * rep
            x = C.splice(*tmp, axis=i - num_dynamic_axis)
        i += 1

    return x 

def _padding(x, pattern, axis):
    base_shape = x.shape
    if b_any([dim < 0 for dim in base_shape]):
        raise ValueError('CNTK Backend: padding input tensor with '
                         'shape `%s` contains non-specified dimension, '
                         'which is not supported. Please give fixed '
                         'dimension to enable padding.' % base_shape)
    if pattern[0] > 0:
        prefix_shape = list(base_shape)
        prefix_shape[axis] = pattern[0]
        prefix_shape = tuple(prefix_shape)
        x = C.splice(C.constant(value=0, shape=prefix_shape), x, axis=axis)
        base_shape = x.shape

    if pattern[1] > 0:
        postfix_shape = list(base_shape)
        postfix_shape[axis] = pattern[1]
        postfix_shape = tuple(postfix_shape)
        x = C.splice(x, C.constant(value=0, shape=postfix_shape), axis=axis)

    return x 

def _padding(x, pattern, axis):
    base_shape = x.shape
    if b_any([dim < 0 for dim in base_shape]):
        raise ValueError('CNTK Backend: padding input tensor with '
                         'shape `%s` contains non-specified dimension, '
                         'which is not supported. Please give fixed '
                         'dimension to enable padding.' % base_shape)
    if pattern[0] > 0:
        prefix_shape = list(base_shape)
        prefix_shape[axis] = pattern[0]
        prefix_shape = tuple(prefix_shape)
        x = C.splice(C.constant(value=0, shape=prefix_shape), x, axis=axis)
        base_shape = x.shape
    if pattern[1] > 0:
        postfix_shape = list(base_shape)
        postfix_shape[axis] = pattern[1]
        postfix_shape = tuple(postfix_shape)
        x = C.splice(x, C.constant(value=0, shape=postfix_shape), axis=axis)
    return x 

def tile(x, n):
    if isinstance(n, int):
        n = (n,)
    elif isinstance(n, list):
        n = tuple(n)

    shape = int_shape(x)
    num_dynamic_axis = _get_dynamic_axis_num(x)
    # Padding the axis
    if len(n) < len(shape):
        n = tuple([1 for _ in range(len(shape) - len(n))]) + n

    if len(n) != len(shape):
        raise NotImplementedError

    i = num_dynamic_axis
    for i, rep in enumerate(n):
        if i >= num_dynamic_axis and shape[i] is not None:
            tmp = [x] * rep
            x = C.splice(*tmp, axis=i - num_dynamic_axis)
        i += 1

    return x 

def repeat(x, n):
    # this is a workaround for recurrent layer
    # if n is inferred dimension,
    # we can't figure out how to repeat it in cntk now
    # return the same x to take cntk broadcast feature
    # to make the recurrent layer work.
    # need to be fixed in GA.
    if n is C.InferredDimension or n is C.FreeDimension:
        return x
    index = 1 - _get_dynamic_axis_num(x)
    if index < 0 or index > 1:
        raise NotImplementedError

    new_shape = list(x.shape)
    new_shape.insert(index, 1)
    new_shape = tuple(new_shape)
    x = C.reshape(x, new_shape)
    temp = [x] * n
    return C.splice(*temp, axis=index) 

def repeat(x, n):
    # this is a workaround for recurrent layer
    # if n is inferred dimension,
    # we can't figure out how to repeat it in cntk now
    # return the same x to take cntk broadcast feature
    # to make the recurrent layer work.
    # need to be fixed in GA.
    if n is C.InferredDimension or n is C.FreeDimension:
        return x
    index = 1 - _get_dynamic_axis_num(x)
    if index < 0 or index > 1:
        raise NotImplementedError

    new_shape = list(x.shape)
    new_shape.insert(index, 1)
    new_shape = tuple(new_shape)
    x = C.reshape(x, new_shape)
    temp = [x] * n
    return C.splice(*temp, axis=index) 

def tile(x, n):
    if isinstance(n, int):
        n = (n,)
    elif isinstance(n, list):
        n = tuple(n)

    shape = int_shape(x)
    num_dynamic_axis = _get_dynamic_axis_num(x)
    # Padding the axis
    if len(n) < len(shape):
        n = tuple([1 for _ in range(len(shape) - len(n))]) + n

    if len(n) != len(shape):
        raise NotImplementedError

    i = num_dynamic_axis
    for i, rep in enumerate(n):
        if i >= num_dynamic_axis and shape[i] is not None:
            tmp = [x] * rep
            x = C.splice(*tmp, axis=i - num_dynamic_axis)
        i += 1

    return x 

def _padding(x, pattern, axis):
    base_shape = x.shape
    if b_any([dim < 0 for dim in base_shape]):
        raise ValueError('CNTK Backend: padding input tensor with '
                         'shape `%s` contains non-specified dimension, '
                         'which is not supported. Please give fixed '
                         'dimension to enable padding.' % base_shape)
    if pattern[0] > 0:
        prefix_shape = list(base_shape)
        prefix_shape[axis] = pattern[0]
        prefix_shape = tuple(prefix_shape)
        x = C.splice(C.constant(value=0, shape=prefix_shape), x, axis=axis)
        base_shape = x.shape
    if pattern[1] > 0:
        postfix_shape = list(base_shape)
        postfix_shape[axis] = pattern[1]
        postfix_shape = tuple(postfix_shape)
        x = C.splice(x, C.constant(value=0, shape=postfix_shape), axis=axis)
    return x 

def repeat(x, n):
    # this is a workaround for recurrent layer
    # if n is inferred dimension,
    # we can't figure out how to repeat it in cntk now
    # return the same x to take cntk broadcast feature
    # to make the recurrent layer work.
    # need to be fixed in GA.
    if n is C.InferredDimension or n is C.FreeDimension:
        return x
    index = 1 - _get_dynamic_axis_num(x)
    if index < 0 or index > 1:
        raise NotImplementedError

    new_shape = list(x.shape)
    new_shape.insert(index, 1)
    new_shape = tuple(new_shape)
    x = C.reshape(x, new_shape)
    temp = [x] * n
    return C.splice(*temp, axis=index) 

def tile(x, n):
    if isinstance(n, int):
        n = (n,)
    elif isinstance(n, list):
        n = tuple(n)

    shape = int_shape(x)
    num_dynamic_axis = _get_dynamic_axis_num(x)
    # Padding the axis
    if len(n) < len(shape):
        n = tuple([1 for _ in range(len(shape) - len(n))]) + n

    if len(n) != len(shape):
        raise NotImplementedError

    i = num_dynamic_axis
    for i, rep in enumerate(n):
        if i >= num_dynamic_axis and shape[i] is not None:
            tmp = [x] * rep
            x = C.splice(*tmp, axis=i - num_dynamic_axis)
        i += 1

    return x 

def _padding(x, pattern, axis):
    base_shape = x.shape
    if b_any([dim < 0 for dim in base_shape]):
        raise ValueError('CNTK Backend: padding input tensor with '
                         'shape `%s` contains non-specified dimension, '
                         'which is not supported. Please give fixed '
                         'dimension to enable padding.' % base_shape)
    if pattern[0] > 0:
        prefix_shape = list(base_shape)
        prefix_shape[axis] = pattern[0]
        prefix_shape = tuple(prefix_shape)
        x = C.splice(C.constant(value=0, shape=prefix_shape), x, axis=axis)
        base_shape = x.shape
    if pattern[1] > 0:
        postfix_shape = list(base_shape)
        postfix_shape[axis] = pattern[1]
        postfix_shape = tuple(postfix_shape)
        x = C.splice(x, C.constant(value=0, shape=postfix_shape), axis=axis)
    return x 

def repeat(x, n):
    # this is a workaround for recurrent layer
    # if n is inferred dimension,
    # we can't figure out how to repeat it in cntk now
    # return the same x to take cntk broadcast feature
    # to make the recurrent layer work.
    # need to be fixed in GA.
    if n is C.InferredDimension or n is C.FreeDimension:
        return x
    index = 1 - _get_dynamic_axis_num(x)
    if index < 0 or index > 1:
        raise NotImplementedError

    new_shape = list(x.shape)
    new_shape.insert(index, 1)
    new_shape = tuple(new_shape)
    x = C.reshape(x, new_shape)
    temp = [x] * n
    return C.splice(*temp, axis=index) 

def repeat(x, n):
    # this is a workaround for recurrent layer
    # if n is inferred dimension,
    # we can't figure out how to repeat it in cntk now
    # return the same x to take cntk broadcast feature
    # to make the recurrent layer work.
    # need to be fixed in GA.
    if n is C.InferredDimension:
        return x
    index = 1 - _get_dynamic_axis_num(x)
    if index < 0 or index > 1:
        raise NotImplementedError

    new_shape = list(x.shape)
    new_shape.insert(index, 1)
    new_shape = tuple(new_shape)
    x = C.reshape(x, new_shape)
    temp = [x] * n
    return C.splice(*temp, axis=index) 

def repeat(x, n):
    # this is a workaround for recurrent layer
    # if n is inferred dimension,
    # we can't figure out how to repeat it in cntk now
    # return the same x to take cntk broadcast feature
    # to make the recurrent layer work.
    # need to be fixed in GA.
    if n is C.InferredDimension or n is C.FreeDimension:
        return x
    index = 1 - _get_dynamic_axis_num(x)
    if index < 0 or index > 1:
        raise NotImplementedError

    new_shape = list(x.shape)
    new_shape.insert(index, 1)
    new_shape = tuple(new_shape)
    x = C.reshape(x, new_shape)
    temp = [x] * n
    return C.splice(*temp, axis=index) 

def _padding(x, pattern, axis):
    base_shape = x.shape
    if b_any([dim < 0 for dim in base_shape]):
        raise ValueError('CNTK Backend: padding input tensor with '
                         'shape `%s` contains non-specified dimension, '
                         'which is not supported. Please give fixed '
                         'dimension to enable padding.' % base_shape)
    if pattern[0] > 0:
        prefix_shape = list(base_shape)
        prefix_shape[axis] = pattern[0]
        prefix_shape = tuple(prefix_shape)
        x = C.splice(C.constant(value=0, shape=prefix_shape), x, axis=axis)
        base_shape = x.shape
    if pattern[1] > 0:
        postfix_shape = list(base_shape)
        postfix_shape[axis] = pattern[1]
        postfix_shape = tuple(postfix_shape)
        x = C.splice(x, C.constant(value=0, shape=postfix_shape), axis=axis)
    return x 

def repeat(x, n):
    # this is a workaround for recurrent layer
    # if n is inferred dimension,
    # we can't figure out how to repeat it in cntk now
    # return the same x to take cntk broadcast feature
    # to make the recurrent layer work.
    # need to be fixed in GA.
    if n is C.InferredDimension or n is C.FreeDimension:
        return x
    index = 1 - _get_dynamic_axis_num(x)
    if index < 0 or index > 1:
        raise NotImplementedError

    new_shape = list(x.shape)
    new_shape.insert(index, 1)
    new_shape = tuple(new_shape)
    x = C.reshape(x, new_shape)
    temp = [x] * n
    return C.splice(*temp, axis=index) 

def _padding(x, pattern, axis):
    base_shape = x.shape
    if b_any([dim < 0 for dim in base_shape]):
        raise ValueError('CNTK Backend: padding input tensor with '
                         'shape `%s` contains non-specified dimension, '
                         'which is not supported. Please give fixed '
                         'dimension to enable padding.' % base_shape)
    if pattern[0] > 0:
        prefix_shape = list(base_shape)
        prefix_shape[axis] = pattern[0]
        prefix_shape = tuple(prefix_shape)
        x = C.splice(C.constant(value=0, shape=prefix_shape), x, axis=axis)
        base_shape = x.shape
    if pattern[1] > 0:
        postfix_shape = list(base_shape)
        postfix_shape[axis] = pattern[1]
        postfix_shape = tuple(postfix_shape)
        x = C.splice(x, C.constant(value=0, shape=postfix_shape), axis=axis)
    return x 

def tile(x, n):
    if isinstance(n, int):
        n = (n,)
    elif isinstance(n, list):
        n = tuple(n)

    shape = int_shape(x)
    num_dynamic_axis = _get_dynamic_axis_num(x)
    # Padding the axis
    if len(n) < len(shape):
        n = tuple([1 for _ in range(len(shape) - len(n))]) + n

    if len(n) != len(shape):
        raise NotImplementedError

    i = num_dynamic_axis
    for i, rep in enumerate(n):
        if i >= num_dynamic_axis and shape[i] is not None:
            tmp = [x] * rep
            x = C.splice(*tmp, axis=i - num_dynamic_axis)
        i += 1

    return x 

def tile(x, n):
    if isinstance(n, list):
        n = tuple(n)

    shape = int_shape(x)
    num_dynamic_axis = _get_dynamic_axis_num(x)
    # Padding the axis
    if len(n) < len(shape):
        n = tuple([None for _ in range(len(shape) - len(n))]) + n

    if len(n) != len(shape):
        raise NotImplementedError

    i = num_dynamic_axis
    for i, rep in enumerate(n):
        if i >= num_dynamic_axis and shape[i] is not None:
            tmp = [x] * rep
            x = C.splice(*tmp, axis=i - num_dynamic_axis)
        i += 1

    return x 

def repeat(x, n):
    # this is a workaround for recurrent layer
    # if n is inferred dimension,
    # we can't figure out how to repeat it in cntk now
    # return the same x to take cntk broadcast feature
    # to make the recurrent layer work.
    # need to be fixed in GA.
    if n is C.InferredDimension or n is C.FreeDimension:
        return x
    index = 1 - _get_dynamic_axis_num(x)
    if index < 0 or index > 1:
        raise NotImplementedError

    new_shape = list(x.shape)
    new_shape.insert(index, 1)
    new_shape = tuple(new_shape)
    x = C.reshape(x, new_shape)
    temp = [x] * n
    return C.splice(*temp, axis=index) 

def _padding(x, pattern, axis):  # pragma: no cover
    base_shape = x.shape
    if b_any([dim < 0 for dim in base_shape]):
        raise ValueError('CNTK Backend: padding input tensor with '
                         'shape `%s` contains non-specified dimension, '
                         'which is not supported. Please give fixed '
                         'dimension to enable padding.' % base_shape)
    if pattern[0] > 0:
        prefix_shape = list(base_shape)
        prefix_shape[axis] = pattern[0]
        prefix_shape = tuple(prefix_shape)
        x = C.splice(C.constant(value=0, shape=prefix_shape), x, axis=axis)
        base_shape = x.shape
    if pattern[1] > 0:
        postfix_shape = list(base_shape)
        postfix_shape[axis] = pattern[1]
        postfix_shape = tuple(postfix_shape)
        x = C.splice(x, C.constant(value=0, shape=postfix_shape), axis=axis)
    return x 

def concatenate(tensors, axis=-1):
    if len(tensors) == 0:
        return None

    axis = [axis]
    axis = _normalize_axis(axis, tensors[0])
    return C.splice(*tensors, axis=axis[0])