Python cntk.input方法代码示例

# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import input [as 别名]
def create_basic_model(input, out_dims):
    net = C.layers.Convolution(
        (5, 5), 32, init=C.initializer.glorot_uniform(), activation=C.relu, pad=True
    )(input)
    net = C.layers.MaxPooling((3, 3), strides=(2, 2))(net)

    net = C.layers.Convolution(
        (5, 5), 32, init=C.initializer.glorot_uniform(), activation=C.relu, pad=True
    )(net)
    net = C.layers.MaxPooling((3, 3), strides=(2, 2))(net)

    net = C.layers.Convolution(
        (5, 5), 64, init=C.initializer.glorot_uniform(), activation=C.relu, pad=True
    )(net)
    net = C.layers.MaxPooling((3, 3), strides=(2, 2))(net)

    net = C.layers.Dense(64, init=C.initializer.glorot_uniform())(net)
    net = C.layers.Dense(out_dims, init=C.initializer.glorot_uniform(), activation=None)(net)

    return net 

# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import input [as 别名]
def create_vgg9_model(input, out_dims):
    with C.layers.default_options(activation=C.relu):
        model = C.layers.Sequential([
            C.layers.For(range(3), lambda i: [
                C.layers.Convolution(
                    (3, 3), [64, 96, 128][i], init=C.initializer.glorot_uniform(), pad=True
                ),
                C.layers.Convolution(
                    (3, 3), [64, 96, 128][i], init=C.initializer.glorot_uniform(), pad=True
                ),
                C.layers.MaxPooling((3, 3), strides=(2, 2))
            ]),
            C.layers.For(range(2), lambda: [
                C.layers.Dense(1024, init=C.initializer.glorot_uniform())
            ]),
            C.layers.Dense(out_dims, init=C.initializer.glorot_uniform(), activation=None)
        ])
    return model(input) 

# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import input [as 别名]
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 

# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import input [as 别名]
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 

# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import input [as 别名]
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 

# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import input [as 别名]
def create_terse_model(input, out_dims):
    with C.layers.default_options(activation=C.relu):
        model = C.layers.Sequential([
            C.layers.For(range(3), lambda i: [
                C.layers.Convolution(
                    (5, 5), [32, 32, 64][i], init=C.initializer.glorot_uniform(), pad=True
                ),
                C.layers.MaxPooling((3, 3), strides=(2, 2))
            ]),
            C.layers.Dense(64, init=C.initializer.glorot_uniform()),
            C.layers.Dense(out_dims, init=C.initializer.glorot_uniform(), activation=None)
        ])

    return model(input) 

# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import input [as 别名]
def create_dropout_model(input, out_dims):
    with C.layers.default_options(activation=C.relu):
        model = C.layers.Sequential([
            C.layers.For(range(3), lambda i: [
                C.layers.Convolution(
                    (5, 5), [32, 32, 64][i], init=C.initializer.glorot_uniform(), pad=True
                ),
                C.layers.MaxPooling((3, 3), strides=(2, 2))
            ]),
            C.layers.Dense(64, init=C.initializer.glorot_uniform()),
            C.layers.Dropout(0.25),
            C.layers.Dense(out_dims, init=C.initializer.glorot_uniform(), activation=None)
        ])

    return model(input) 

# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import input [as 别名]
def convolution_bn(input, filter_size, num_filters, strides=(1, 1),
                   init=C.he_normal(), activation=C.relu):
    if activation is None:
        activation = lambda x: x

    r = C.layers.Convolution(
        filter_size, num_filters,
        strides=strides, init=init,
        activation=None, pad=True, bias=False
    )(input)
    # r = C.layers.BatchNormalization(map_rank=1)(r)
    return activation(r) 

# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import input [as 别名]
def resnet_basic_inc(input, num_filters):
    c1 = convolution_bn(input, (3, 3), num_filters, strides=(2, 2))
    c2 = convolution_bn(c1, (3, 3), num_filters, activation=None)
    s = convolution_bn(input, (1, 1), num_filters, strides=(2, 2), activation=None)
    return C.relu(c2 + s) 

# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import input [as 别名]
def resnet_basic_stack(input, num_filters, num_stack):
    assert num_stack > 0
    r = input
    for _ in range(num_stack):
        r = resnet_basic(r, num_filters)
    return r 

# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import input [as 别名]
def create_resnet_model(input, out_dims):
    conv = convolution_bn(input, (3, 3), 16)
    r1_1 = resnet_basic_stack(conv, 16, 3)

    r2_1 = resnet_basic_inc(r1_1, 32)
    r2_2 = resnet_basic_stack(r2_1, 32, 2)

    r3_1 = resnet_basic_inc(r2_2, 64)
    r3_2 = resnet_basic_stack(r3_1, 64, 2)

    pool = C.layers.AveragePooling(filter_shape=(8, 8), strides=(1, 1))(r3_2)
    net = C.layers.Dense(out_dims, init=C.he_normal(), activation=None)(pool)
    return net 

# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import input [as 别名]
def placeholder(
        shape=None,
        ndim=None,
        dtype=None,
        sparse=False,
        name=None,
        dynamic_axis_num=1):
    if dtype is None:
        dtype = floatx()
    if not shape:
        if ndim:
            shape = tuple([None for _ in range(ndim)])

    dynamic_dimension = C.FreeDimension if _get_cntk_version() >= 2.2 else C.InferredDimension
    cntk_shape = [dynamic_dimension if s is None else s for s in shape]
    cntk_shape = tuple(cntk_shape)

    if dynamic_axis_num > len(cntk_shape):
        raise ValueError('CNTK backend: creating placeholder with '
                         '%d dimension is not supported, at least '
                         '%d dimensions are needed.'
                         % (len(cntk_shape), dynamic_axis_num))

    if name is None:
        name = ''

    cntk_shape = cntk_shape[dynamic_axis_num:]

    x = C.input(
        shape=cntk_shape,
        dtype=_convert_string_dtype(dtype),
        is_sparse=sparse,
        name=name)
    x._keras_shape = shape
    x._uses_learning_phase = False
    x._cntk_placeholder = True
    return x 

# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import input [as 别名]
def _is_input_shape_compatible(input, placeholder):
        if hasattr(input, 'shape') and hasattr(placeholder, 'shape'):
            num_dynamic = get_num_dynamic_axis(placeholder)
            input_shape = input.shape[num_dynamic:]
            placeholder_shape = placeholder.shape
            for i, p in zip(input_shape, placeholder_shape):
                if i != p and p != C.InferredDimension and p != C.FreeDimension:
                    return False
        return True 

# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import input [as 别名]
def _preprocess_conv2d_input(x, data_format):
    if data_format == 'channels_last':
        # TF uses the last dimension as channel dimension,
        # instead of the 2nd one.
        # TH input shape: (samples, input_depth, rows, cols)
        # TF input shape: (samples, rows, cols, input_depth)
        x = C.transpose(x, (2, 0, 1))
    return x 

# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import input [as 别名]
def _preprocess_conv3d_input(x, data_format):
    if data_format == 'channels_last':
        # TF uses the last dimension as channel dimension,
        # instead of the 2nd one.
        # TH input shape: (samples, input_depth, conv_dim1, conv_dim2, conv_dim3)
        # TF input shape: (samples, conv_dim1, conv_dim2, conv_dim3,
        # input_depth)
        x = C.transpose(x, (3, 0, 1, 2))
    return x