bindings/python/doc: fix most Sphinx warnings and some misc. errors
This commit is contained in:
Mark Hillebrand
Родитель
fbc6ba086b
Коммит
09c71fe83a
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@ -218,6 +218,10 @@ bindings/python/cntk/libs/
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bindings/python/cntk/cntk_py_wrap.cpp
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bindings/python/cntk/cntk_py_wrap.h
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bindings/python/dist/
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bindings/python/doc/cntk.*.rst
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bindings/python/doc/cntk.rst
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bindings/python/doc/modules.rst
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bindings/python/doc/_build
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# Auto-generated sources from CNTK.proto
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Source/CNTKv2LibraryDll/proto/CNTK.pb.cc
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@ -190,10 +190,11 @@ class Value(cntk_py.Value):
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dtype: data type (np.float32 or np.float64)
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batch: batch input for `var`.
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It can be:
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* a pure Python structure (list of lists, ...),
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* a list of NumPy arrays or SciPy sparse CSR matrices
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* a :class:`~cntk.core.Value` object (e.g. returned by :func:`one_hot`)
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seq_starts (list of `bool`s or None): if None, every sequence is
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seq_starts (list of `bool`\ s or None): if None, every sequence is
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treated as a new sequence. Otherwise, it is interpreted as a list of
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Booleans that tell whether a sequence is a new sequence (`True`) or a
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continuation of the sequence in the same slot of the previous
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@ -277,12 +278,13 @@ class Value(cntk_py.Value):
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Args:
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var (:class:`~cntk.ops.variables.Variable`): variable into which
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``data`` is passed
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data: data for `var`
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data: data for `var`.
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It can be:
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* a single NumPy array denoting the full minibatch
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* a list of NumPy arrays or SciPy sparse CSR matrices
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* a single NumPy array denoting one parameter or constant
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seq_starts (list of `bool`s or None): if None, every sequence is
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seq_starts (list of `bool`\ s or None): if None, every sequence is
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treated as a new sequence. Otherwise, it is interpreted as a list of
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Booleans that tell whether a sequence is a new sequence (`True`) or a
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continuation of the sequence in the same slot of the previous
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@ -446,6 +448,7 @@ class Value(cntk_py.Value):
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'''
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The mask matrix of this value. Each row denotes a sequence with its
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elements describing the mask of the element:
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* 2: beginning of sequence (e.g. an LSTM would be reset)
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* 1: valid element
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* 0: invalid element
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@ -63,9 +63,12 @@ def get_default_override(function, **kwargs):
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Meant to be used inside functions that use this facility.
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Args:
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function: the function that calls this, e.g.:
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``def Convolution(args, init=default_override_or(glorot_uniform()), activation=default_override_or(identity), pad=default_override_or(False)):
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init = _get_default_override(Convolution, init=init) # pass default under the same name``
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function: the function that calls this.
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For example::
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def Convolution(args, init=default_override_or(glorot_uniform()), activation=default_override_or(identity), pad=default_override_or(False)):
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init = _get_default_override(Convolution, init=init) # pass default under the same name
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'''
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# parameter checking and casting
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if len(kwargs) != 1:
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@ -231,6 +231,7 @@ def sanitize_var_map(op_arguments, arguments, precision=None,
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data.
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* any other type: if node has a unique input, arguments is
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mapped to this input.
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For nodes with more than one input, only dict is allowed.
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In both cases, every sample in the data will be interpreted
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@ -95,12 +95,14 @@ class MinibatchSource(cntk_py.MinibatchSource):
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`randomization_window`. If `True`, the size of the randomization window is interpreted as a certain
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number of samples, otherwise -- as a number of chunks. Similarly to `randomization_window`,
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this parameter is ignored, when `randomize` is `False`
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epoch_size (`int`, defaults to cntk.io.INFINITELY_REPEAT): number of samples as a scheduling unit.
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epoch_size (`int`, defaults to :const:`~cntk.io.INFINITELY_REPEAT`): number of samples as a scheduling unit.
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Parameters in the schedule change their values every `epoch_size`
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samples. If no `epoch_size` is provided, this parameter is substituted
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by the size of the full data sweep with infinte repeat, in which case the scheduling unit is
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the entire data sweep (as indicated by the MinibatchSource) and parameters
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change their values on the sweep-by-sweep basis specified by the schedule. **Important:** `click here <https://github.com/Microsoft/CNTK/wiki/BrainScript-epochSize-and-Python-epoch_size-in-CNTK>`_ for a full description of this parameter.
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change their values on the sweep-by-sweep basis specified by the schedule.
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**Important:**
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Click `here <https://github.com/Microsoft/CNTK/wiki/BrainScript-epochSize-and-Python-epoch_size-in-CNTK>`_ for a full description of this parameter.
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distributed_after (int, defaults to cntk.io.INFINITE_SAMPLES): sample count after which minibatch source becomes distributed
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multithreaded_deserializer (`bool`, defaults to `None`): using multi threaded deserializer
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frame_mode (`bool`, defaults to `False`): Specifies if data should be randomized and returned at the frame
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@ -181,7 +183,9 @@ class MinibatchSource(cntk_py.MinibatchSource):
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Args:
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minibatch_size_in_samples (int): number of samples to retrieve for
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the next minibatch. Must be > 0. **Important:** `click here <https://github.com/Microsoft/CNTK/wiki/BrainScript-minibatchSize-and-Python-minibatch_size_in_samples-in-CNTK>`_ for a full description of this parameter.
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the next minibatch. Must be > 0.
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**Important:**
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Click `here <https://github.com/Microsoft/CNTK/wiki/BrainScript-epochSize-and-Python-epoch_size-in-CNTK>`_ for a full description of this parameter.
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input_map (dict): mapping of :class:`~cntk.ops.variables.Variable`
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to :class:`~cntk.cntk_py.StreamInformation` which will be used to convert the
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returned data.
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@ -417,6 +421,7 @@ def ImageDeserializer(filename, streams):
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labels from a file of the form::
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<full path to image> <tab> <numerical label (0-based class id)>
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or::
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sequenceId <tab> path <tab> label
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@ -35,9 +35,11 @@ def Sequential(layers, name=''):
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def For(rng, constructor, name=''):
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'''
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Layer factory function to create a composite that applies a sequence of layers constructed with a constructor lambda(layer).
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E.g.
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For(range(3), lambda i: Dense(2000))
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For(range(3), lambda: Dense(2000))
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For example::
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For(range(3), lambda i: Dense(2000))
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For(range(3), lambda: Dense(2000))
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'''
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# Python 2.7 support requires us to use getargspec() instead of inspect
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from inspect import getargspec
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@ -28,7 +28,7 @@ def Dense(shape, activation=default_override_or(identity), init=default_override
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Dense(shape, activation=identity, init=glorot_uniform(),input_rank=None, map_rank=None, bias=True, init_bias=0, name='')
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Layer factory function to create an instance of a fully-connected linear layer of the form
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`activation(input @ W + b)` with weights `W` and bias `b`, and `activation` and `b` being optional.
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`activation(input @ W + b)` with weights `W` and bias `b`, and `activation` and `b` being optional.
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`shape` may describe a tensor as well.
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A ``Dense`` layer instance owns its parameter tensors `W` and `b`, and exposes them as attributes ``.W`` and ``.b``.
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@ -130,8 +130,9 @@ def Embedding(shape=None, init=default_override_or(glorot_uniform()), weights=No
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The lookup table in this layer is learnable,
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unless a user-specified one is supplied through the ``weights`` parameter.
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For example, to use an existing embedding table from a file in numpy format, use this:
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``Embedding(weights=np.load('PATH.npy'))``
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For example, to use an existing embedding table from a file in numpy format, use this::
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Embedding(weights=np.load('PATH.npy'))
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To initialize a learnable lookup table with a given numpy array that is to be used as
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the initial value, pass that array to the ``init`` parameter (not ``weights``).
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@ -139,7 +140,7 @@ def Embedding(shape=None, init=default_override_or(glorot_uniform()), weights=No
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An ``Embedding`` instance owns its weight parameter tensor `E`, and exposes it as an attribute ``.E``.
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Example:
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# learnable embedding
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>>> # learnable embedding
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>>> f = Embedding(5)
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>>> x = Input(3)
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>>> e = f(x)
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@ -148,7 +149,7 @@ def Embedding(shape=None, init=default_override_or(glorot_uniform()), weights=No
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>>> f.E.shape
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(3, 5)
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# user-supplied embedding
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>>> # user-supplied embedding
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>>> f = Embedding(weights=[[.5, .3, .1, .4, .2], [.7, .6, .3, .2, .9]])
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>>> f.E.value
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array([[ 0.5, 0.3, 0.1, 0.4, 0.2],
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@ -653,19 +654,19 @@ def ConvolutionTranspose(filter_shape, # shape of receptive field, e.g. (
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(3, 128, 3, 4)
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Args:
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filter_shape ((`int` or `tuple` of `int`s)): shape (spatial extent) of the receptive field, *not* including the input feature-map depth. E.g. (3,3) for a 2D convolution.
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num_filters (`int`): number of filters (output feature-map depth), or ``()`` to denote scalar output items (output shape will have no depth axis).
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filter_shape (`int` or tuple of `int`\ s): shape (spatial extent) of the receptive field, *not* including the input feature-map depth. E.g. (3,3) for a 2D convolution.
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num_filters (int): number of filters (output feature-map depth), or ``()`` to denote scalar output items (output shape will have no depth axis).
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activation (:class:`~cntk.ops.functions.Function`, optional): optional function to apply at the end, e.g. `relu`
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init (scalar or NumPy array or :mod:`cntk.initializer`, default `glorot_uniform()`): initial value of weights `W`
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pad (`bool` or `tuple` of `bool`s, default `False`): if `False`, then the filter will be shifted over the "valid"
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init (scalar or NumPy array or :mod:`cntk.initializer`, default :func:`glorot_uniform`): initial value of weights `W`
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pad (`bool` or tuple of `bool`\ s, default `False`): if `False`, then the filter will be shifted over the "valid"
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area of input, that is, no value outside the area is used. If ``pad=True`` on the other hand,
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the filter will be applied to all input positions, and positions outside the valid region will be considered containing zero.
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Use a `tuple` to specify a per-axis value.
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strides (`int` or `tuple` of `int`s, default `): stride of the convolution (increment when sliding the filter over the input). Use a `tuple` to specify a per-axis value.
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sharing (`bool`, default True): weight sharing, must be True for now.
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strides (`int` or tuple of `int`\ s, default 1): stride of the convolution (increment when sliding the filter over the input). Use a `tuple` to specify a per-axis value.
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sharing (`bool`, default `True`): weight sharing, must be True for now.
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bias (`bool`, optional, default `True`): the layer will have no bias if `False` is passed here
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init_bias (scalar or NumPy array or :mod:`cntk.initializer`): initial value of weights `b`
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output_shape ((`int` or `tuple` of `int`s)): output shape. When strides > 2, the output shape is non-deterministic. User can specify the wanted output shape. Note the
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output_shape (`int` or tuple of `int`\ s): output shape. When strides > 2, the output shape is non-deterministic. User can specify the wanted output shape. Note the
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specified shape must satisify the condition that if a convolution is perform from the output with the same setting, the result must have same shape as the input.
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reduction_rank (`int`, default 1): must be 1 for now.
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that is stored with tensor shape (H,W) instead of (1,H,W)
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@ -218,10 +218,14 @@ def UnfoldFrom(generator_function, map_state_function=identity, until_predicate=
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'''
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Layer factory function to create a function that implements the unfold() anamorphism. It creates a function that, starting with a seed input,
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applies 'generator_function' repeatedly and emits the sequence of results. Depending on the recurrent block,
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it may have this form:
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`result = f(... f(f([g(input), initial_state])) ... )`
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or this form:
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`result = f(g(input), ... f(g(input), f(g(input), initial_state)) ... )`
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it may have this form::
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result = f(... f(f([g(input), initial_state])) ... )
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or this form::
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result = f(g(input), ... f(g(input), f(g(input), initial_state)) ... )
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where `f` is `generator_function`.
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An example use of this is sequence-to-sequence decoding, where `g(input)` is the sequence encoder,
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`initial_state` is the sentence-start symbol, and `f` is the decoder. The first
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@ -462,11 +462,10 @@ def momentum_sgd(parameters, lr, momentum, unit_gain=default_unit_gain_value(),
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parameters (list of parameters): list of network parameters to tune.
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These can be obtained by the root operator's ``parameters``.
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lr (output of :func:`learning_rate_schedule`): learning rate schedule.
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momentum (output of :func:`momentum_schedule` or
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:func:`momentum_as_time_constant_schedule`): momentum schedule.
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momentum (output of :func:`momentum_schedule` or :func:`momentum_as_time_constant_schedule`): momentum schedule.
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For additional information, please refer to the `wiki
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<https://github.com/Microsoft/CNTK/wiki/SGD-block#converting-learning-rate-and-momentum-parameters-from-other-toolkits>`_.
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unit_gain: when ``True``, momentum is interpreted as a unit-gain filter. Defaults
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<https://github.com/Microsoft/CNTK/wiki/BrainScript-SGD-Block#converting-learning-rate-and-momentum-parameters-from-other-toolkits>`_.
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unit_gain: when ``True``, momentum is interpreted as a unit-gain filter. Defaults
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to the value returned by :func:`default_unit_gain_value`.
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l1_regularization_weight (float, optional): the L1 regularization weight per sample,
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defaults to 0.0
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@ -514,10 +513,9 @@ def nesterov(parameters, lr, momentum, unit_gain=default_unit_gain_value(),
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parameters (list of parameters): list of network parameters to tune.
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These can be obtained by the root operator's ``parameters``.
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lr (output of :func:`learning_rate_schedule`): learning rate schedule.
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momentum (output of :func:`momentum_schedule` or
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:func:`momentum_as_time_constant_schedule`): momentum schedule.
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momentum (output of :func:`momentum_schedule` or :func:`momentum_as_time_constant_schedule`): momentum schedule.
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For additional information, please refer to the `wiki
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<https://github.com/Microsoft/CNTK/wiki/SGD-block#converting-learning-rate-and-momentum-parameters-from-other-toolkits>`_.
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<https://github.com/Microsoft/CNTK/wiki/BrainScript-SGD-Block#converting-learning-rate-and-momentum-parameters-from-other-toolkits>`_.
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unit_gain: when ``True``, momentum is interpreted as a unit-gain filter. Defaults
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to the value returned by :func:`default_unit_gain_value`.
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l1_regularization_weight (float, optional): the L1 regularization weight per sample,
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@ -626,7 +624,7 @@ def fsadagrad(parameters, lr, momentum, unit_gain=default_unit_gain_value(),
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lr (output of :func:`learning_rate_schedule`): learning rate schedule.
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momentum (output of :func:`momentum_schedule` or :func:`momentum_as_time_constant_schedule`): momentum schedule.
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For additional information, please refer to the `wiki
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<https://github.com/Microsoft/CNTK/wiki/SGD-block#converting-learning-rate-and-momentum-parameters-from-other-toolkits>`_.
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<https://github.com/Microsoft/CNTK/wiki/BrainScript-SGD-Block#converting-learning-rate-and-momentum-parameters-from-other-toolkits>`_.
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unit_gain: when ``True``, momentum is interpreted as a unit-gain filter. Defaults
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to the value returned by :func:`default_unit_gain_value`.
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variance_momentum (output of :func:`momentum_schedule` or :func:`momentum_as_time_constant_schedule`): variance momentum schedule. Defaults
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@ -680,7 +678,7 @@ def adam(parameters, lr, momentum, unit_gain=default_unit_gain_value(),
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lr (output of :func:`learning_rate_schedule`): learning rate schedule.
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momentum (output of :func:`momentum_schedule` or :func:`momentum_as_time_constant_schedule`): momentum schedule.
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For additional information, please refer to the `wiki
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<https://github.com/Microsoft/CNTK/wiki/SGD-block#converting-learning-rate-and-momentum-parameters-from-other-toolkits>`_.
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<https://github.com/Microsoft/CNTK/wiki/BrainScript-SGD-Block#converting-learning-rate-and-momentum-parameters-from-other-toolkits>`_.
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unit_gain: when ``True``, momentum is interpreted as a unit-gain filter. Defaults
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to the value returned by :func:`default_unit_gain_value`.
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variance_momentum (output of :func:`momentum_schedule` or :func:`momentum_as_time_constant_schedule`): variance momentum schedule. Defaults
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@ -742,7 +740,7 @@ def adam_sgd(parameters, lr, momentum, unit_gain=default_unit_gain_value(),
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lr (output of :func:`learning_rate_schedule`): learning rate schedule.
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momentum (output of :func:`momentum_schedule` or :func:`momentum_as_time_constant_schedule`): momentum schedule.
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For additional information, please refer to the `wiki
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<https://github.com/Microsoft/CNTK/wiki/SGD-block#converting-learning-rate-and-momentum-parameters-from-other-toolkits>`_.
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<https://github.com/Microsoft/CNTK/wiki/BrainScript-SGD-Block#converting-learning-rate-and-momentum-parameters-from-other-toolkits>`_.
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unit_gain: when ``True``, momentum is interpreted as a unit-gain filter. Defaults
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to the value returned by :func:`default_unit_gain_value`.
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variance_momentum (output of :func:`momentum_schedule` or :func:`momentum_as_time_constant_schedule`): variance momentum schedule. Defaults
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@ -130,13 +130,13 @@ def plot(root, filename=None):
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* for DOT output: `pydot_ng <https://pypi.python.org/pypi/pydot-ng>`_
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* for PNG, PDF, and SVG output: `pydot_ng <https://pypi.python.org/pypi/pydot-ng>`_
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and `graphviz <[http://graphviz.org](http://graphviz.org)>_ (GraphViz executable has to be in the system's PATH).
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and `graphviz <http://graphviz.org>`_ (GraphViz executable has to be in the system's PATH).
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Args:
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node (graph node): the node to start the journey from
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filename (`str`, default None): file with extension '.dot', 'png', 'pdf', or 'svg'
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to denote what format should be written. If `None` then nothing
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will be plotted. Instead, and the returned string can be used to debug the graph.
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to denote what format should be written. If `None` then nothing
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will be plotted, and the returned string can be used to debug the graph.
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Returns:
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`str` describing the graph
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@ -30,7 +30,7 @@ def combine(operands, name=''):
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'''
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Create a new Function instance which just combines the outputs of the specified list of
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'operands' Functions such that the 'Outputs' of the new 'Function' are union of the
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'Outputs' of each of the specified 'operands' Functions. E.g. When creating a classification
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'Outputs' of each of the specified 'operands' Functions. E.g., when creating a classification
|
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model, typically the CrossEntropy loss Function and the ClassificationError Function comprise
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the two roots of the computation graph which can be combined to create a single Function
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with 2 outputs; viz. CrossEntropy loss and ClassificationError output.
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@ -881,9 +881,12 @@ def times(left, right, output_rank=1, infer_input_rank_to_map=TIMES_NO_INFERRED_
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The operator '@' has been overloaded such that in Python 3.5 and later X @ W equals times(X, W).
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For better performance on times operation on sequence which is followed by sequence.reduce_sum, use
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infer_input_rank_to_map=TIMES_REDUCE_SEQUENCE_AXIS_WITHOUT_INFERRED_INPUT_RANK, i.e. replace following:
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infer_input_rank_to_map=TIMES_REDUCE_SEQUENCE_AXIS_WITHOUT_INFERRED_INPUT_RANK, i.e. replace following::
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|
||||
sequence.reduce_sum(times(seq1, seq2))
|
||||
with:
|
||||
|
||||
with::
|
||||
|
||||
times(seq1, seq2, infer_input_rank_to_map=TIMES_REDUCE_SEQUENCE_AXIS_WITHOUT_INFERRED_INPUT_RANK)
|
||||
|
||||
Example:
|
||||
|
|
@ -1962,7 +1965,6 @@ def slice(x, axis, begin_index, end_index, name=''):
|
|||
... [4, 5, 6]]]],dtype=np.float32)})
|
||||
array([[[[ 1.],
|
||||
[ 4.]]]], dtype=float32)
|
||||
|
||||
<BLANKLINE>
|
||||
>>> # slice using constant
|
||||
>>> data = np.asarray([[1, 2, -3],
|
||||
|
|
@ -1973,7 +1975,6 @@ def slice(x, axis, begin_index, end_index, name=''):
|
|||
>>> C.slice(x, 1, 0, 1).eval()
|
||||
array([[ 1.],
|
||||
[ 4.]], dtype=float32)
|
||||
|
||||
<BLANKLINE>
|
||||
>>> # slice using the index overload
|
||||
>>> data = np.asarray([[1, 2, -3],
|
||||
|
|
@ -2485,7 +2486,7 @@ def dropout(x, dropout_rate=0.0, name=''):
|
|||
Args:
|
||||
x: input tensor
|
||||
dropout_rate (float, [0,1)): probability that an element of ``x`` will be set to zero
|
||||
name (:class:str, optional): the name of the Function instance in the network
|
||||
name (:class:`str`, optional): the name of the Function instance in the network
|
||||
|
||||
Returns:
|
||||
:class:`~cntk.ops.functions.Function`
|
||||
|
|
@ -2545,7 +2546,7 @@ def output_variable(shape, dtype, dynamic_axes, name=''):
|
|||
|
||||
Args:
|
||||
shape (tuple or int): the shape of the input tensor
|
||||
dtype (type): np.float32 or np.float64
|
||||
dtype (np.float32 or np.float64): data type
|
||||
dynamic_axes (list or tuple): a list of dynamic axis (e.g., batch axis, time axis)
|
||||
name (str, optional): the name of the Function instance in the network
|
||||
|
||||
|
|
|
|||
|
|
@ -65,9 +65,10 @@ class Function(cntk_py.Function):
|
|||
``@Function`` constructs a Function from a Python lambda
|
||||
where the Function's input signature is defined by the lambda.
|
||||
|
||||
Use this as a decorator, e.g.:
|
||||
``@Function
|
||||
def f(x): return x * x``
|
||||
Use this as a decorator, e.g.::
|
||||
|
||||
@Function
|
||||
def f(x): return x * x
|
||||
|
||||
The above form creates a CNTK Function whose arguments are placeholder variables.
|
||||
Such a function can only be combined with others symbolic functions.
|
||||
|
|
@ -76,19 +77,21 @@ class Function(cntk_py.Function):
|
|||
of the arguments. In this case, the @Function decorator creates a CNTK Function
|
||||
whose arguments are input variables.
|
||||
|
||||
If you use Python 3, Functions with types are declared using Python annotation syntax, e.g.:
|
||||
``@Function
|
||||
def f(x:Tensor[13]):
|
||||
return x * x``
|
||||
If you use Python 3, Functions with types are declared using Python annotation syntax, e.g.::
|
||||
|
||||
If you are still working with Python 2.7, use CNTK's @Signature decorator instead:
|
||||
``@Function
|
||||
@Function
|
||||
def f(x:Tensor[13]):
|
||||
return x * x
|
||||
|
||||
If you are working with Python 2.7, use CNTK's @Signature decorator instead::
|
||||
|
||||
@Function
|
||||
@Signature(Tensor[13])
|
||||
def f(x):
|
||||
return x * x``
|
||||
return x * x
|
||||
|
||||
``make_block=True`` is used to implement @BlockFunction(). If given the result will be wrapped
|
||||
in ``as_block()``, using the supplied ``op_name`` and ``name`` parameters, which are otherwise ignored.
|
||||
in ``as_block()``, using the supplied ``op_name`` and ``name`` parameters, which are otherwise ignored.
|
||||
'''
|
||||
f_name = f.__name__ # (only used for debugging and error messages)
|
||||
|
||||
|
|
@ -471,6 +474,7 @@ class Function(cntk_py.Function):
|
|||
input data.
|
||||
* any other type: if node has an unique input, arguments is
|
||||
mapped to this input.
|
||||
|
||||
For nodes with more than one input, only dict is allowed.
|
||||
|
||||
In both cases, every sample in the data will be interpreted
|
||||
|
|
@ -587,6 +591,7 @@ class Function(cntk_py.Function):
|
|||
elements of the sequence are grouped along axis 0.
|
||||
* any other type: if node has an unique input, arguments is
|
||||
mapped to this input.
|
||||
|
||||
For nodes with more than one input, only dict is allowed.
|
||||
|
||||
In both cases, every sample in the data will be interpreted
|
||||
|
|
|
|||
|
|
@ -96,11 +96,14 @@ class Trainer(cntk_py.Trainer):
|
|||
|
||||
Args:
|
||||
arguments: maps variables to their input data. Empty map signifies
|
||||
end of local training data.
|
||||
end of local training data.
|
||||
The interpretation depends on the input type:
|
||||
|
||||
* `dict`: keys are input variable or names, and values are the input data.
|
||||
|
||||
* any other type: if node has an unique input, ``arguments`` is mapped to this input.
|
||||
For nodes with more than one input, only `dict` is allowed.
|
||||
For nodes with more than one input, only `dict` is allowed.
|
||||
|
||||
In both cases, every sample in the data will be interpreted
|
||||
as a new sequence. To mark samples as continuations of the
|
||||
previous sequence, specify ``arguments`` as `tuple`: the
|
||||
|
|
@ -179,8 +182,10 @@ class Trainer(cntk_py.Trainer):
|
|||
|
||||
* `dict`: keys are input variable or names, and values are the input data.
|
||||
See :meth:`~cntk.ops.functions.Function.forward` for details on passing input data.
|
||||
|
||||
* any other type: if node has an unique input, ``arguments`` is mapped to this input.
|
||||
For nodes with more than one input, only `dict` is allowed.
|
||||
For nodes with more than one input, only `dict` is allowed.
|
||||
|
||||
In both cases, every sample in the data will be interpreted
|
||||
as a new sequence. To mark samples as continuations of the
|
||||
previous sequence, specify ``arguments`` as `tuple`: the
|
||||
|
|
|
|||
|
|
@ -157,7 +157,7 @@ class TrainingSession(cntk_py.TrainingSession):
|
|||
Perform training on a specified device.
|
||||
|
||||
Args:
|
||||
device (:class:~cntk.device.DeviceDescriptor): the device descriptor containing
|
||||
device (:class:`~cntk.device.DeviceDescriptor`): the device descriptor containing
|
||||
the type and id of the device where training takes place.
|
||||
'''
|
||||
|
||||
|
|
|
|||
|
|
@ -204,9 +204,12 @@ def eval(op, arguments=None, precision=None, device=None, backward_pass=False, e
|
|||
op (:class:`Function`): operation to evaluate
|
||||
arguments: maps variables to their input data. The
|
||||
interpretation depends on the input type:
|
||||
|
||||
* `dict`: keys are input variable or names, and values are the input data.
|
||||
|
||||
* any other type: if node has a unique input, ``arguments`` is mapped to this input.
|
||||
For nodes with more than one input, only `dict` is allowed.
|
||||
For nodes with more than one input, only `dict` is allowed.
|
||||
|
||||
In both cases, every sample in the data will be interpreted
|
||||
as a new sequence. To mark samples as continuations of the
|
||||
previous sequence, specify ``arguments`` as `tuple`: the
|
||||
|
|
@ -351,8 +354,9 @@ def Signature(*args, **kwargs):
|
|||
**kwargs: types of arguments with optional names, e.g. `x=Tensor[42]`. Use this second form for
|
||||
longer argument lists.
|
||||
|
||||
Example:
|
||||
``# Python 3:
|
||||
Example::
|
||||
|
||||
# Python 3:
|
||||
@Function
|
||||
def f(x: Tensor[42]):
|
||||
return sigmoid(x)
|
||||
|
|
|
|||
|
|
@ -1,17 +1,27 @@
|
|||
REM Steps to recreate the docs:
|
||||
|
||||
setlocal
|
||||
|
||||
cd "%~dp0"
|
||||
cd /d "%~dp0"
|
||||
set PYTHONPATH=%CD%\..
|
||||
echo PYTHONPATH=%PYTHONPATH%
|
||||
set PATH=%CD%\..;%CD%\..\..\..\x64\Release;%PATH%
|
||||
echo PATH=%PATH%
|
||||
|
||||
sphinx-apidoc.exe ..\cntk -o . -f
|
||||
@REM TODO better align conf.py exclude with excluded paths here
|
||||
sphinx-apidoc.exe ..\cntk -o . -f ^
|
||||
..\cntk\tests ^
|
||||
..\cntk\debugging\tests ^
|
||||
..\cntk\internal\tests ^
|
||||
..\cntk\io\tests ^
|
||||
..\cntk\layers\tests ^
|
||||
..\cntk\learners\tests ^
|
||||
..\cntk\logging\tests ^
|
||||
..\cntk\losses\tests ^
|
||||
..\cntk\metrics\tests ^
|
||||
..\cntk\ops\tests ^
|
||||
..\cntk\train\tests ^
|
||||
..\cntk\utils\tests
|
||||
|
||||
if errorlevel 1 exit /b 1
|
||||
|
||||
.\make.bat html
|
||||
if errorlevel 1 exit /b 1
|
||||
|
||||
echo start _build\html\index.html
|
||||
|
||||
|
|
|
|||
|
|
@ -1,10 +0,0 @@
|
|||
cntk.learner package
|
||||
====================
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: cntk.learner
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -1,10 +0,0 @@
|
|||
cntk.trainer package
|
||||
====================
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: cntk.trainer
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -1,10 +0,0 @@
|
|||
cntk.training_session package
|
||||
=============================
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: cntk.training_session
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -1,3 +1,5 @@
|
|||
:orphan:
|
||||
|
||||
Concepts
|
||||
========
|
||||
|
||||
|
|
|
|||
|
|
@ -146,7 +146,7 @@ html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
|
|||
# Add any paths that contain custom static files (such as style sheets) here,
|
||||
# relative to this directory. They are copied after the builtin static files,
|
||||
# so a file named "default.css" will overwrite the builtin "default.css".
|
||||
html_static_path = ['_static']
|
||||
#html_static_path = ['_static']
|
||||
|
||||
# Add any extra paths that contain custom files (such as robots.txt or
|
||||
# .htaccess) here, relative to this directory. These files are copied
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
Debugging models
|
||||
================
|
||||
|
||||
.. automodule:: cntk.debug
|
||||
.. automodule:: cntk.debugging
|
||||
:members:
|
||||
|
|
|
|||
|
|
@ -1,13 +0,0 @@
|
|||
Graph components
|
||||
===========================
|
||||
|
||||
.. automodule:: cntk.ops.variables
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
.. automodule:: cntk.ops.functions
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
|
@ -8,19 +8,19 @@ Tutorials
|
|||
CNTK 102: `Feed Forward network`_ with NumPy
|
||||
|
||||
#. *Recognize hand written digits (OCR) with MNIST data*
|
||||
CNTK 103 Part A: `Data preparation <https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb>`_ , Part B: `Feed Forward classifier`_
|
||||
CNTK 103 Part A: `MNIST data preparation`_ , Part B: `Feed Forward classifier`_
|
||||
|
||||
#. *Learn how to predict the stock market*
|
||||
CNTK 104: `Time Series basics`_ with finance data
|
||||
|
||||
#. *Compress (using autoencoder) hand written digits from MNIST data with no human input (unsupervised learning, FFN)*
|
||||
CNTK 105 Part A: `Data preparation <https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb>`_ , Part B: `Feed Forward autoencoder`_
|
||||
CNTK 105 Part A: `MNIST data preparation`_ , Part B: `Feed Forward autoencoder`_
|
||||
|
||||
#. *Forecasting using data from an IOT device*
|
||||
CNTK 106: LSTM based forecasting - Part A: `with simulated data <https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_106A_LSTM_Timeseries_with_Simulated_Data.ipynb>`_, Part B: `with real IOT data <https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_106B_LSTM_Timeseries_with_IOT_Data.ipynb>`_
|
||||
|
||||
#. *Recognize objects in images from CIFAR-10 data (Convolutional Network, CNN)*
|
||||
CNTK 201 Part A: `Data preparation <https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb>`_, Part B: `VGG and ResNet classifiers`_
|
||||
CNTK 201 Part A: `CIFAR data preparation`_, Part B: `VGG and ResNet classifiers`_
|
||||
|
||||
#. *Infer meaning from text snippets using LSTMs and word embeddings*
|
||||
CNTK 202: `Language understanding`_ with ATIS3 text data
|
||||
|
|
@ -35,7 +35,7 @@ Tutorials
|
|||
CNTK 205: `Artistic Style Transfer`_
|
||||
|
||||
#. *Produce realistic data (MNIST images) with no human input (unsupervised learning)*
|
||||
CNTK 206 Part A: `Data preparation <https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb>`_ , Part B: `Basic Generative Adversarial Networks (GAN)`_
|
||||
CNTK 206 Part A: `MNIST data preparation`_ , Part B: `Basic Generative Adversarial Networks (GAN)`_
|
||||
|
||||
#. *Training with Sampled Softmax*
|
||||
CNTK 207: `Training with Sampled Softmax`_
|
||||
|
|
@ -46,12 +46,12 @@ For our Japanese users, you can find some of the `tutorials in Japanese`_.
|
|||
|
||||
.. _`Logistic Regression`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_101_LogisticRegression.ipynb
|
||||
.. _`Feed Forward network`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_102_FeedForward.ipynb
|
||||
.. _`Data preparation`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb
|
||||
.. _`MNIST data preparation`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb
|
||||
.. _`Feed Forward classifier`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
|
||||
.. _`Time Series basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
|
||||
.. _`Feed Forward autoencoder`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_105_Basic_Autoencoder_for_Dimensionality_Reduction.ipynb
|
||||
.. _`Basic LSTM based time series`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_106A_LSTM_Timeseries_with_Simulated_Data.ipynb
|
||||
.. _`data preparation`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb
|
||||
.. _`CIFAR data preparation`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb
|
||||
.. _`VGG and ResNet classifiers`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb
|
||||
.. _`Language understanding`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_202_Language_Understanding.ipynb
|
||||
.. _`Reinforcement learning basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_203_Reinforcement_Learning_Basics.ipynb
|
||||
|
|
|
|||
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