diff options
author | 2016-10-31 13:38:50 -0800 | |
---|---|---|
committer | 2016-10-31 14:50:06 -0700 | |
commit | 61c309287ebba939f3bfacc8540a8e5017b1061a (patch) | |
tree | 39b5c48f2f316e301b819fa6676d479ffe810c7c | |
parent | 4f50a5f58b82b3628975897bb8cfd7f0123ac803 (diff) |
Update generated Python Op docs.
Change: 137750783
-rw-r--r-- | tensorflow/g3doc/api_docs/python/contrib.learn.md | 234 | ||||
-rw-r--r-- | tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.LinearRegressor.md | 234 |
2 files changed, 10 insertions, 458 deletions
diff --git a/tensorflow/g3doc/api_docs/python/contrib.learn.md b/tensorflow/g3doc/api_docs/python/contrib.learn.md index 54a2b06ada..8d55f807ae 100644 --- a/tensorflow/g3doc/api_docs/python/contrib.learn.md +++ b/tensorflow/g3doc/api_docs/python/contrib.learn.md @@ -1561,13 +1561,6 @@ Construct a `LinearRegressor` estimator object. - - - -#### `tf.contrib.learn.LinearRegressor.__repr__()` {#LinearRegressor.__repr__} - - - - -- - - - #### `tf.contrib.learn.LinearRegressor.bias_` {#LinearRegressor.bias_} DEPRECATED FUNCTION @@ -1586,162 +1579,37 @@ This method will be removed after the deprecation date. To inspect variables, us - - - -#### `tf.contrib.learn.LinearRegressor.dnn_bias_` {#LinearRegressor.dnn_bias_} - -Returns bias of deep neural network part. (deprecated) - -THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-30. -Instructions for updating: -This method will be removed after the deprecation date. To inspect variables, use get_variable_names() and get_variable_value(). - - -- - - - -#### `tf.contrib.learn.LinearRegressor.dnn_weights_` {#LinearRegressor.dnn_weights_} - -Returns weights of deep neural network part. (deprecated) - -THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-30. -Instructions for updating: -This method will be removed after the deprecation date. To inspect variables, use get_variable_names() and get_variable_value(). - - -- - - - #### `tf.contrib.learn.LinearRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#LinearRegressor.evaluate} -See `Evaluable`. - -##### Raises: - - -* <b>`ValueError`</b>: If at least one of `x` or `y` is provided, and at least one of - `input_fn` or `feed_fn` is provided. - Or if `metrics` is not `None` or `dict`. +See evaluable.Evaluable. - - - -#### `tf.contrib.learn.LinearRegressor.export(*args, **kwargs)` {#LinearRegressor.export} - -Exports inference graph into given dir. (deprecated arguments) - -SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-23. -Instructions for updating: -The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn (and in most cases, input_feature_key) will become required args, and use_deprecated_input_fn will default to False and be removed altogether. - - Args: - export_dir: A string containing a directory to write the exported graph - and checkpoints. - input_fn: If `use_deprecated_input_fn` is true, then a function that given - `Tensor` of `Example` strings, parses it into features that are then - passed to the model. Otherwise, a function that takes no argument and - returns a tuple of (features, labels), where features is a dict of - string key to `Tensor` and labels is a `Tensor` that's currently not - used (and so can be `None`). - input_feature_key: Only used if `use_deprecated_input_fn` is false. String - key into the features dict returned by `input_fn` that corresponds to a - the raw `Example` strings `Tensor` that the exported model will take as - input. Can only be `None` if you're using a custom `signature_fn` that - does not use the first arg (examples). - use_deprecated_input_fn: Determines the signature format of `input_fn`. - signature_fn: Function that returns a default signature and a named - signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s - for features and `Tensor` or `dict` of `Tensor`s for predictions. - prediction_key: The key for a tensor in the `predictions` dict (output - from the `model_fn`) to use as the `predictions` input to the - `signature_fn`. Optional. If `None`, predictions will pass to - `signature_fn` without filtering. - default_batch_size: Default batch size of the `Example` placeholder. - exports_to_keep: Number of exports to keep. +#### `tf.contrib.learn.LinearRegressor.export(export_dir, input_fn=None, input_feature_key=None, use_deprecated_input_fn=True, signature_fn=None, default_batch_size=1, exports_to_keep=None)` {#LinearRegressor.export} - Returns: - The string path to the exported directory. NB: this functionality was - added ca. 2016/09/25; clients that depend on the return value may need - to handle the case where this function returns None because subclasses - are not returning a value. +See BaseEstimator.export. - - - #### `tf.contrib.learn.LinearRegressor.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)` {#LinearRegressor.fit} -See `Trainable`. - -##### Raises: - - -* <b>`ValueError`</b>: If `x` or `y` are not `None` while `input_fn` is not `None`. -* <b>`ValueError`</b>: If both `steps` and `max_steps` are not `None`. - - -- - - - -#### `tf.contrib.learn.LinearRegressor.get_params(deep=True)` {#LinearRegressor.get_params} - -Get parameters for this estimator. - -##### Args: - - -* <b>`deep`</b>: boolean, optional - - If `True`, will return the parameters for this estimator and - contained subobjects that are estimators. - -##### Returns: - - params : mapping of string to any - Parameter names mapped to their values. +See trainable.Trainable. - - - #### `tf.contrib.learn.LinearRegressor.get_variable_names()` {#LinearRegressor.get_variable_names} -Returns list of all variable names in this model. -##### Returns: - - List of names. - - - #### `tf.contrib.learn.LinearRegressor.get_variable_value(name)` {#LinearRegressor.get_variable_value} -Returns value of the variable given by name. - -##### Args: - - -* <b>`name`</b>: string, name of the tensor. - -##### Returns: - - Numpy array - value of the tensor. - - -- - - - -#### `tf.contrib.learn.LinearRegressor.linear_bias_` {#LinearRegressor.linear_bias_} - -Returns bias of the linear part. (deprecated) - -THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-30. -Instructions for updating: -This method will be removed after the deprecation date. To inspect variables, use get_variable_names() and get_variable_value(). - - -- - - -#### `tf.contrib.learn.LinearRegressor.linear_weights_` {#LinearRegressor.linear_weights_} - -Returns weights per feature of the linear part. (deprecated) - -THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-30. -Instructions for updating: -This method will be removed after the deprecation date. To inspect variables, use get_variable_names() and get_variable_value(). - - - @@ -1753,52 +1621,9 @@ This method will be removed after the deprecation date. To inspect variables, us - - - -#### `tf.contrib.learn.LinearRegressor.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None)` {#LinearRegressor.partial_fit} - -Incremental fit on a batch of samples. - -This method is expected to be called several times consecutively -on different or the same chunks of the dataset. This either can -implement iterative training or out-of-core/online training. - -This is especially useful when the whole dataset is too big to -fit in memory at the same time. Or when model is taking long time -to converge, and you want to split up training into subparts. - -##### Args: - - -* <b>`x`</b>: Matrix of shape [n_samples, n_features...]. Can be iterator that - returns arrays of features. The training input samples for fitting the - model. If set, `input_fn` must be `None`. -* <b>`y`</b>: Vector or matrix [n_samples] or [n_samples, n_outputs]. Can be - iterator that returns array of labels. The training label values - (class labels in classification, real numbers in regression). If set, - `input_fn` must be `None`. -* <b>`input_fn`</b>: Input function. If set, `x`, `y`, and `batch_size` must be - `None`. -* <b>`steps`</b>: Number of steps for which to train model. If `None`, train forever. -* <b>`batch_size`</b>: minibatch size to use on the input, defaults to first - dimension of `x`. Must be `None` if `input_fn` is provided. -* <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks - inside the training loop. - -##### Returns: - - `self`, for chaining. - -##### Raises: - - -* <b>`ValueError`</b>: If at least one of `x` and `y` is provided, and `input_fn` is - provided. - - -- - - - #### `tf.contrib.learn.LinearRegressor.predict(*args, **kwargs)` {#LinearRegressor.predict} -Returns predictions for given features. (deprecated arguments) +Runs inference to determine the predicted class. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-15. Instructions for updating: @@ -1806,55 +1631,6 @@ The default behavior of predict() is changing. The default value for as_iterable will change to True, and then the flag will be removed altogether. The behavior of this flag is described below. - Args: - x: Matrix of shape [n_samples, n_features...]. Can be iterator that - returns arrays of features. The training input samples for fitting the - model. If set, `input_fn` must be `None`. - input_fn: Input function. If set, `x` and 'batch_size' must be `None`. - batch_size: Override default batch size. If set, 'input_fn' must be - 'None'. - outputs: list of `str`, name of the output to predict. - If `None`, returns all. - as_iterable: If True, return an iterable which keeps yielding predictions - for each example until inputs are exhausted. Note: The inputs must - terminate if you want the iterable to terminate (e.g. be sure to pass - num_epochs=1 if you are using something like read_batch_features). - - Returns: - A numpy array of predicted classes or regression values if the - constructor's `model_fn` returns a `Tensor` for `predictions` or a `dict` - of numpy arrays if `model_fn` returns a `dict`. Returns an iterable of - predictions if as_iterable is True. - - Raises: - ValueError: If x and input_fn are both provided or both `None`. - - -- - - - -#### `tf.contrib.learn.LinearRegressor.set_params(**params)` {#LinearRegressor.set_params} - -Set the parameters of this estimator. - -The method works on simple estimators as well as on nested objects -(such as pipelines). The former have parameters of the form -``<component>__<parameter>`` so that it's possible to update each -component of a nested object. - -##### Args: - - -* <b>`**params`</b>: Parameters. - -##### Returns: - - self - -##### Raises: - - -* <b>`ValueError`</b>: If params contain invalid names. - - - - diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.LinearRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.LinearRegressor.md index 87bc01d137..bdeab9de13 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.LinearRegressor.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.LinearRegressor.md @@ -84,13 +84,6 @@ Construct a `LinearRegressor` estimator object. - - - -#### `tf.contrib.learn.LinearRegressor.__repr__()` {#LinearRegressor.__repr__} - - - - -- - - - #### `tf.contrib.learn.LinearRegressor.bias_` {#LinearRegressor.bias_} DEPRECATED FUNCTION @@ -109,162 +102,37 @@ This method will be removed after the deprecation date. To inspect variables, us - - - -#### `tf.contrib.learn.LinearRegressor.dnn_bias_` {#LinearRegressor.dnn_bias_} - -Returns bias of deep neural network part. (deprecated) - -THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-30. -Instructions for updating: -This method will be removed after the deprecation date. To inspect variables, use get_variable_names() and get_variable_value(). - - -- - - - -#### `tf.contrib.learn.LinearRegressor.dnn_weights_` {#LinearRegressor.dnn_weights_} - -Returns weights of deep neural network part. (deprecated) - -THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-30. -Instructions for updating: -This method will be removed after the deprecation date. To inspect variables, use get_variable_names() and get_variable_value(). - - -- - - - #### `tf.contrib.learn.LinearRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#LinearRegressor.evaluate} -See `Evaluable`. - -##### Raises: - - -* <b>`ValueError`</b>: If at least one of `x` or `y` is provided, and at least one of - `input_fn` or `feed_fn` is provided. - Or if `metrics` is not `None` or `dict`. +See evaluable.Evaluable. - - - -#### `tf.contrib.learn.LinearRegressor.export(*args, **kwargs)` {#LinearRegressor.export} +#### `tf.contrib.learn.LinearRegressor.export(export_dir, input_fn=None, input_feature_key=None, use_deprecated_input_fn=True, signature_fn=None, default_batch_size=1, exports_to_keep=None)` {#LinearRegressor.export} -Exports inference graph into given dir. (deprecated arguments) - -SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-23. -Instructions for updating: -The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn (and in most cases, input_feature_key) will become required args, and use_deprecated_input_fn will default to False and be removed altogether. - - Args: - export_dir: A string containing a directory to write the exported graph - and checkpoints. - input_fn: If `use_deprecated_input_fn` is true, then a function that given - `Tensor` of `Example` strings, parses it into features that are then - passed to the model. Otherwise, a function that takes no argument and - returns a tuple of (features, labels), where features is a dict of - string key to `Tensor` and labels is a `Tensor` that's currently not - used (and so can be `None`). - input_feature_key: Only used if `use_deprecated_input_fn` is false. String - key into the features dict returned by `input_fn` that corresponds to a - the raw `Example` strings `Tensor` that the exported model will take as - input. Can only be `None` if you're using a custom `signature_fn` that - does not use the first arg (examples). - use_deprecated_input_fn: Determines the signature format of `input_fn`. - signature_fn: Function that returns a default signature and a named - signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s - for features and `Tensor` or `dict` of `Tensor`s for predictions. - prediction_key: The key for a tensor in the `predictions` dict (output - from the `model_fn`) to use as the `predictions` input to the - `signature_fn`. Optional. If `None`, predictions will pass to - `signature_fn` without filtering. - default_batch_size: Default batch size of the `Example` placeholder. - exports_to_keep: Number of exports to keep. - - Returns: - The string path to the exported directory. NB: this functionality was - added ca. 2016/09/25; clients that depend on the return value may need - to handle the case where this function returns None because subclasses - are not returning a value. +See BaseEstimator.export. - - - #### `tf.contrib.learn.LinearRegressor.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)` {#LinearRegressor.fit} -See `Trainable`. - -##### Raises: - - -* <b>`ValueError`</b>: If `x` or `y` are not `None` while `input_fn` is not `None`. -* <b>`ValueError`</b>: If both `steps` and `max_steps` are not `None`. - - -- - - - -#### `tf.contrib.learn.LinearRegressor.get_params(deep=True)` {#LinearRegressor.get_params} - -Get parameters for this estimator. - -##### Args: - - -* <b>`deep`</b>: boolean, optional - - If `True`, will return the parameters for this estimator and - contained subobjects that are estimators. - -##### Returns: - - params : mapping of string to any - Parameter names mapped to their values. +See trainable.Trainable. - - - #### `tf.contrib.learn.LinearRegressor.get_variable_names()` {#LinearRegressor.get_variable_names} -Returns list of all variable names in this model. - -##### Returns: - List of names. - - - #### `tf.contrib.learn.LinearRegressor.get_variable_value(name)` {#LinearRegressor.get_variable_value} -Returns value of the variable given by name. - -##### Args: - - -* <b>`name`</b>: string, name of the tensor. - -##### Returns: - Numpy array - value of the tensor. - - -- - - - -#### `tf.contrib.learn.LinearRegressor.linear_bias_` {#LinearRegressor.linear_bias_} - -Returns bias of the linear part. (deprecated) - -THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-30. -Instructions for updating: -This method will be removed after the deprecation date. To inspect variables, use get_variable_names() and get_variable_value(). - - -- - - - -#### `tf.contrib.learn.LinearRegressor.linear_weights_` {#LinearRegressor.linear_weights_} - -Returns weights per feature of the linear part. (deprecated) - -THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-30. -Instructions for updating: -This method will be removed after the deprecation date. To inspect variables, use get_variable_names() and get_variable_value(). - - - @@ -276,52 +144,9 @@ This method will be removed after the deprecation date. To inspect variables, us - - - -#### `tf.contrib.learn.LinearRegressor.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None)` {#LinearRegressor.partial_fit} - -Incremental fit on a batch of samples. - -This method is expected to be called several times consecutively -on different or the same chunks of the dataset. This either can -implement iterative training or out-of-core/online training. - -This is especially useful when the whole dataset is too big to -fit in memory at the same time. Or when model is taking long time -to converge, and you want to split up training into subparts. - -##### Args: - - -* <b>`x`</b>: Matrix of shape [n_samples, n_features...]. Can be iterator that - returns arrays of features. The training input samples for fitting the - model. If set, `input_fn` must be `None`. -* <b>`y`</b>: Vector or matrix [n_samples] or [n_samples, n_outputs]. Can be - iterator that returns array of labels. The training label values - (class labels in classification, real numbers in regression). If set, - `input_fn` must be `None`. -* <b>`input_fn`</b>: Input function. If set, `x`, `y`, and `batch_size` must be - `None`. -* <b>`steps`</b>: Number of steps for which to train model. If `None`, train forever. -* <b>`batch_size`</b>: minibatch size to use on the input, defaults to first - dimension of `x`. Must be `None` if `input_fn` is provided. -* <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks - inside the training loop. - -##### Returns: - - `self`, for chaining. - -##### Raises: - - -* <b>`ValueError`</b>: If at least one of `x` and `y` is provided, and `input_fn` is - provided. - - -- - - - #### `tf.contrib.learn.LinearRegressor.predict(*args, **kwargs)` {#LinearRegressor.predict} -Returns predictions for given features. (deprecated arguments) +Runs inference to determine the predicted class. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-15. Instructions for updating: @@ -329,55 +154,6 @@ The default behavior of predict() is changing. The default value for as_iterable will change to True, and then the flag will be removed altogether. The behavior of this flag is described below. - Args: - x: Matrix of shape [n_samples, n_features...]. Can be iterator that - returns arrays of features. The training input samples for fitting the - model. If set, `input_fn` must be `None`. - input_fn: Input function. If set, `x` and 'batch_size' must be `None`. - batch_size: Override default batch size. If set, 'input_fn' must be - 'None'. - outputs: list of `str`, name of the output to predict. - If `None`, returns all. - as_iterable: If True, return an iterable which keeps yielding predictions - for each example until inputs are exhausted. Note: The inputs must - terminate if you want the iterable to terminate (e.g. be sure to pass - num_epochs=1 if you are using something like read_batch_features). - - Returns: - A numpy array of predicted classes or regression values if the - constructor's `model_fn` returns a `Tensor` for `predictions` or a `dict` - of numpy arrays if `model_fn` returns a `dict`. Returns an iterable of - predictions if as_iterable is True. - - Raises: - ValueError: If x and input_fn are both provided or both `None`. - - -- - - - -#### `tf.contrib.learn.LinearRegressor.set_params(**params)` {#LinearRegressor.set_params} - -Set the parameters of this estimator. - -The method works on simple estimators as well as on nested objects -(such as pipelines). The former have parameters of the form -``<component>__<parameter>`` so that it's possible to update each -component of a nested object. - -##### Args: - - -* <b>`**params`</b>: Parameters. - -##### Returns: - - self - -##### Raises: - - -* <b>`ValueError`</b>: If params contain invalid names. - - - - |