diff --git a/tensorflow/g3doc/api_docs/python/contrib.learn.md b/tensorflow/g3doc/api_docs/python/contrib.learn.md index d1ece756b23798b594b7d5c482a7df0658be833d..5842f510dfc14f403fa57b63628b212f0510c827 100644 --- a/tensorflow/g3doc/api_docs/python/contrib.learn.md +++ b/tensorflow/g3doc/api_docs/python/contrib.learn.md @@ -82,7 +82,7 @@ Evaluates given model with provided evaluation data. - - - -#### `tf.contrib.learn.BaseEstimator.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None)` {#BaseEstimator.fit} +#### `tf.contrib.learn.BaseEstimator.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)` {#BaseEstimator.fit} Trains a model given training data `x` predictions and `y` targets. @@ -103,6 +103,11 @@ Trains a model given training data `x` predictions and `y` targets. dimension of `x`. Must be `None` if `input_fn` is provided. * `monitors`: List of `BaseMonitor` subclass instances. Used for callbacks inside the training loop. +* `max_steps`: Number of total steps for which to train model. If `None`, + train forever. Two calls to `fit(steps=100)` means 200 training + iterations. On the other hand, two calls to `fit(max_steps=100)` means + that the second call will not do any iteration since first call did + all 100 steps. ##### Returns: @@ -112,12 +117,9 @@ Trains a model given training data `x` predictions and `y` targets. * `ValueError`: If `x` or `y` are not `None` while `input_fn` is not `None`. - -##### Raises: - - * `ValueError`: If at least one of `x` and `y` is provided, and `input_fn` is provided. +* `ValueError`: If both `steps` and `max_steps` are not `None`. - - - @@ -358,7 +360,7 @@ Evaluates given model with provided evaluation data. - - - -#### `tf.contrib.learn.Estimator.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None)` {#Estimator.fit} +#### `tf.contrib.learn.Estimator.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)` {#Estimator.fit} Trains a model given training data `x` predictions and `y` targets. @@ -379,6 +381,11 @@ Trains a model given training data `x` predictions and `y` targets. dimension of `x`. Must be `None` if `input_fn` is provided. * `monitors`: List of `BaseMonitor` subclass instances. Used for callbacks inside the training loop. +* `max_steps`: Number of total steps for which to train model. If `None`, + train forever. Two calls to `fit(steps=100)` means 200 training + iterations. On the other hand, two calls to `fit(max_steps=100)` means + that the second call will not do any iteration since first call did + all 100 steps. ##### Returns: @@ -388,12 +395,9 @@ Trains a model given training data `x` predictions and `y` targets. * `ValueError`: If `x` or `y` are not `None` while `input_fn` is not `None`. - -##### Raises: - - * `ValueError`: If at least one of `x` and `y` is provided, and `input_fn` is provided. +* `ValueError`: If both `steps` and `max_steps` are not `None`. - - - @@ -976,7 +980,7 @@ Evaluates given model with provided evaluation data. - - - -#### `tf.contrib.learn.DNNClassifier.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None)` {#DNNClassifier.fit} +#### `tf.contrib.learn.DNNClassifier.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)` {#DNNClassifier.fit} Trains a model given training data `x` predictions and `y` targets. @@ -997,6 +1001,11 @@ Trains a model given training data `x` predictions and `y` targets. dimension of `x`. Must be `None` if `input_fn` is provided. * `monitors`: List of `BaseMonitor` subclass instances. Used for callbacks inside the training loop. +* `max_steps`: Number of total steps for which to train model. If `None`, + train forever. Two calls to `fit(steps=100)` means 200 training + iterations. On the other hand, two calls to `fit(max_steps=100)` means + that the second call will not do any iteration since first call did + all 100 steps. ##### Returns: @@ -1006,12 +1015,9 @@ Trains a model given training data `x` predictions and `y` targets. * `ValueError`: If `x` or `y` are not `None` while `input_fn` is not `None`. - -##### Raises: - - * `ValueError`: If at least one of `x` and `y` is provided, and `input_fn` is provided. +* `ValueError`: If both `steps` and `max_steps` are not `None`. - - - @@ -1356,7 +1362,7 @@ Evaluates given model with provided evaluation data. - - - -#### `tf.contrib.learn.DNNRegressor.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None)` {#DNNRegressor.fit} +#### `tf.contrib.learn.DNNRegressor.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)` {#DNNRegressor.fit} Trains a model given training data `x` predictions and `y` targets. @@ -1377,6 +1383,11 @@ Trains a model given training data `x` predictions and `y` targets. dimension of `x`. Must be `None` if `input_fn` is provided. * `monitors`: List of `BaseMonitor` subclass instances. Used for callbacks inside the training loop. +* `max_steps`: Number of total steps for which to train model. If `None`, + train forever. Two calls to `fit(steps=100)` means 200 training + iterations. On the other hand, two calls to `fit(max_steps=100)` means + that the second call will not do any iteration since first call did + all 100 steps. ##### Returns: @@ -1386,12 +1397,9 @@ Trains a model given training data `x` predictions and `y` targets. * `ValueError`: If `x` or `y` are not `None` while `input_fn` is not `None`. - -##### Raises: - - * `ValueError`: If at least one of `x` and `y` is provided, and `input_fn` is provided. +* `ValueError`: If both `steps` and `max_steps` are not `None`. - - - @@ -2525,7 +2533,7 @@ Evaluates given model with provided evaluation data. - - - -#### `tf.contrib.learn.LinearClassifier.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None)` {#LinearClassifier.fit} +#### `tf.contrib.learn.LinearClassifier.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)` {#LinearClassifier.fit} Trains a model given training data `x` predictions and `y` targets. @@ -2546,6 +2554,11 @@ Trains a model given training data `x` predictions and `y` targets. dimension of `x`. Must be `None` if `input_fn` is provided. * `monitors`: List of `BaseMonitor` subclass instances. Used for callbacks inside the training loop. +* `max_steps`: Number of total steps for which to train model. If `None`, + train forever. Two calls to `fit(steps=100)` means 200 training + iterations. On the other hand, two calls to `fit(max_steps=100)` means + that the second call will not do any iteration since first call did + all 100 steps. ##### Returns: @@ -2555,12 +2568,9 @@ Trains a model given training data `x` predictions and `y` targets. * `ValueError`: If `x` or `y` are not `None` while `input_fn` is not `None`. - -##### Raises: - - * `ValueError`: If at least one of `x` and `y` is provided, and `input_fn` is provided. +* `ValueError`: If both `steps` and `max_steps` are not `None`. - - - @@ -2887,7 +2897,7 @@ Evaluates given model with provided evaluation data. - - - -#### `tf.contrib.learn.LinearRegressor.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None)` {#LinearRegressor.fit} +#### `tf.contrib.learn.LinearRegressor.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)` {#LinearRegressor.fit} Trains a model given training data `x` predictions and `y` targets. @@ -2908,6 +2918,11 @@ Trains a model given training data `x` predictions and `y` targets. dimension of `x`. Must be `None` if `input_fn` is provided. * `monitors`: List of `BaseMonitor` subclass instances. Used for callbacks inside the training loop. +* `max_steps`: Number of total steps for which to train model. If `None`, + train forever. Two calls to `fit(steps=100)` means 200 training + iterations. On the other hand, two calls to `fit(max_steps=100)` means + that the second call will not do any iteration since first call did + all 100 steps. ##### Returns: @@ -2917,12 +2932,9 @@ Trains a model given training data `x` predictions and `y` targets. * `ValueError`: If `x` or `y` are not `None` while `input_fn` is not `None`. - -##### Raises: - - * `ValueError`: If at least one of `x` and `y` is provided, and `input_fn` is provided. +* `ValueError`: If both `steps` and `max_steps` are not `None`. - - - @@ -4674,7 +4686,7 @@ Run `output_dict` tensors `n` times, with the same `feed_dict` each run. - - - -### `tf.contrib.learn.train(graph, output_dir, train_op, loss_op, global_step_tensor=None, init_op=None, init_feed_dict=None, init_fn=None, log_every_steps=10, supervisor_is_chief=True, supervisor_master='', supervisor_save_model_secs=600, keep_checkpoint_max=5, supervisor_save_summaries_steps=100, feed_fn=None, steps=None, fail_on_nan_loss=True, monitors=None)` {#train} +### `tf.contrib.learn.train(graph, output_dir, train_op, loss_op, global_step_tensor=None, init_op=None, init_feed_dict=None, init_fn=None, log_every_steps=10, supervisor_is_chief=True, supervisor_master='', supervisor_save_model_secs=600, keep_checkpoint_max=5, supervisor_save_summaries_steps=100, feed_fn=None, steps=None, fail_on_nan_loss=True, monitors=None, max_steps=None)` {#train} Train a model. @@ -4726,6 +4738,10 @@ program is terminated with exit code 1. evaluates to `NaN`. If false, continue training as if nothing happened. * `monitors`: List of `BaseMonitor` subclass instances. Used for callbacks inside the training loop. +* `max_steps`: Number of total steps for which to train model. If `None`, + train forever. Two calls fit(steps=100) means 200 training iterations. + On the other hand two calls of fit(max_steps=100) means, second call + will not do any iteration since first call did all 100 steps. ##### Returns: @@ -4739,6 +4755,7 @@ program is terminated with exit code 1. look up the latter if not provided explicitly. * `NanLossDuringTrainingError`: If `fail_on_nan_loss` is `True`, and loss ever evaluates to `NaN`. +* `ValueError`: If both `steps` and `max_steps` are not `None`. 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 a4b807c2f0f3c07acb6d381f7145dcc1a3a13892..8a1c6dc9539cf26134da6f0417a3649874d84c0a 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 @@ -138,7 +138,7 @@ Evaluates given model with provided evaluation data. - - - -#### `tf.contrib.learn.LinearRegressor.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None)` {#LinearRegressor.fit} +#### `tf.contrib.learn.LinearRegressor.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)` {#LinearRegressor.fit} Trains a model given training data `x` predictions and `y` targets. @@ -159,6 +159,11 @@ Trains a model given training data `x` predictions and `y` targets. dimension of `x`. Must be `None` if `input_fn` is provided. * `monitors`: List of `BaseMonitor` subclass instances. Used for callbacks inside the training loop. +* `max_steps`: Number of total steps for which to train model. If `None`, + train forever. Two calls to `fit(steps=100)` means 200 training + iterations. On the other hand, two calls to `fit(max_steps=100)` means + that the second call will not do any iteration since first call did + all 100 steps. ##### Returns: @@ -168,12 +173,9 @@ Trains a model given training data `x` predictions and `y` targets. * `ValueError`: If `x` or `y` are not `None` while `input_fn` is not `None`. - -##### Raises: - - * `ValueError`: If at least one of `x` and `y` is provided, and `input_fn` is provided. +* `ValueError`: If both `steps` and `max_steps` are not `None`. - - - diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.LinearClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.LinearClassifier.md index e7f22891a820221b19349313f0a95440d8a9676b..db6699ccb8774e2e8b09a0c73018e2f8f11f5bb0 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.LinearClassifier.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.LinearClassifier.md @@ -157,7 +157,7 @@ Evaluates given model with provided evaluation data. - - - -#### `tf.contrib.learn.LinearClassifier.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None)` {#LinearClassifier.fit} +#### `tf.contrib.learn.LinearClassifier.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)` {#LinearClassifier.fit} Trains a model given training data `x` predictions and `y` targets. @@ -178,6 +178,11 @@ Trains a model given training data `x` predictions and `y` targets. dimension of `x`. Must be `None` if `input_fn` is provided. * `monitors`: List of `BaseMonitor` subclass instances. Used for callbacks inside the training loop. +* `max_steps`: Number of total steps for which to train model. If `None`, + train forever. Two calls to `fit(steps=100)` means 200 training + iterations. On the other hand, two calls to `fit(max_steps=100)` means + that the second call will not do any iteration since first call did + all 100 steps. ##### Returns: @@ -187,12 +192,9 @@ Trains a model given training data `x` predictions and `y` targets. * `ValueError`: If `x` or `y` are not `None` while `input_fn` is not `None`. - -##### Raises: - - * `ValueError`: If at least one of `x` and `y` is provided, and `input_fn` is provided. +* `ValueError`: If both `steps` and `max_steps` are not `None`. - - - diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.train.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.train.md index 5b16d8151f90a5f54fa3c94f3de72d1aabb48ce4..2715e0c9c948c8aadee41d04714873383b7a1f2c 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.train.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.train.md @@ -1,4 +1,4 @@ -### `tf.contrib.learn.train(graph, output_dir, train_op, loss_op, global_step_tensor=None, init_op=None, init_feed_dict=None, init_fn=None, log_every_steps=10, supervisor_is_chief=True, supervisor_master='', supervisor_save_model_secs=600, keep_checkpoint_max=5, supervisor_save_summaries_steps=100, feed_fn=None, steps=None, fail_on_nan_loss=True, monitors=None)` {#train} +### `tf.contrib.learn.train(graph, output_dir, train_op, loss_op, global_step_tensor=None, init_op=None, init_feed_dict=None, init_fn=None, log_every_steps=10, supervisor_is_chief=True, supervisor_master='', supervisor_save_model_secs=600, keep_checkpoint_max=5, supervisor_save_summaries_steps=100, feed_fn=None, steps=None, fail_on_nan_loss=True, monitors=None, max_steps=None)` {#train} Train a model. @@ -50,6 +50,10 @@ program is terminated with exit code 1. evaluates to `NaN`. If false, continue training as if nothing happened. * `monitors`: List of `BaseMonitor` subclass instances. Used for callbacks inside the training loop. +* `max_steps`: Number of total steps for which to train model. If `None`, + train forever. Two calls fit(steps=100) means 200 training iterations. + On the other hand two calls of fit(max_steps=100) means, second call + will not do any iteration since first call did all 100 steps. ##### Returns: @@ -63,4 +67,5 @@ program is terminated with exit code 1. look up the latter if not provided explicitly. * `NanLossDuringTrainingError`: If `fail_on_nan_loss` is `True`, and loss ever evaluates to `NaN`. +* `ValueError`: If both `steps` and `max_steps` are not `None`. diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md index 2d416c79b6705446f52a9604a2943e0cc7868946..7089ef25bb24b13a1b63c975b6e286946999c6ef 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md @@ -67,7 +67,7 @@ Evaluates given model with provided evaluation data. - - - -#### `tf.contrib.learn.BaseEstimator.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None)` {#BaseEstimator.fit} +#### `tf.contrib.learn.BaseEstimator.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)` {#BaseEstimator.fit} Trains a model given training data `x` predictions and `y` targets. @@ -88,6 +88,11 @@ Trains a model given training data `x` predictions and `y` targets. dimension of `x`. Must be `None` if `input_fn` is provided. * `monitors`: List of `BaseMonitor` subclass instances. Used for callbacks inside the training loop. +* `max_steps`: Number of total steps for which to train model. If `None`, + train forever. Two calls to `fit(steps=100)` means 200 training + iterations. On the other hand, two calls to `fit(max_steps=100)` means + that the second call will not do any iteration since first call did + all 100 steps. ##### Returns: @@ -97,12 +102,9 @@ Trains a model given training data `x` predictions and `y` targets. * `ValueError`: If `x` or `y` are not `None` while `input_fn` is not `None`. - -##### Raises: - - * `ValueError`: If at least one of `x` and `y` is provided, and `input_fn` is provided. +* `ValueError`: If both `steps` and `max_steps` are not `None`. - - - diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md index 9e576094da2d3a7c378fc25a48bae965a76f417f..ffbc5d544c0d8b74a96a8a4aa70cf6cee1fd4ee8 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md @@ -84,7 +84,7 @@ Evaluates given model with provided evaluation data. - - - -#### `tf.contrib.learn.Estimator.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None)` {#Estimator.fit} +#### `tf.contrib.learn.Estimator.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)` {#Estimator.fit} Trains a model given training data `x` predictions and `y` targets. @@ -105,6 +105,11 @@ Trains a model given training data `x` predictions and `y` targets. dimension of `x`. Must be `None` if `input_fn` is provided. * `monitors`: List of `BaseMonitor` subclass instances. Used for callbacks inside the training loop. +* `max_steps`: Number of total steps for which to train model. If `None`, + train forever. Two calls to `fit(steps=100)` means 200 training + iterations. On the other hand, two calls to `fit(max_steps=100)` means + that the second call will not do any iteration since first call did + all 100 steps. ##### Returns: @@ -114,12 +119,9 @@ Trains a model given training data `x` predictions and `y` targets. * `ValueError`: If `x` or `y` are not `None` while `input_fn` is not `None`. - -##### Raises: - - * `ValueError`: If at least one of `x` and `y` is provided, and `input_fn` is provided. +* `ValueError`: If both `steps` and `max_steps` are not `None`. - - - diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.DNNClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.DNNClassifier.md index a066f320edc26123ac6a8abd85d4939ff65a9e8e..47db93f2ee0e9b1edca51755cdda4d9ed951bb50 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.DNNClassifier.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.DNNClassifier.md @@ -158,7 +158,7 @@ Evaluates given model with provided evaluation data. - - - -#### `tf.contrib.learn.DNNClassifier.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None)` {#DNNClassifier.fit} +#### `tf.contrib.learn.DNNClassifier.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)` {#DNNClassifier.fit} Trains a model given training data `x` predictions and `y` targets. @@ -179,6 +179,11 @@ Trains a model given training data `x` predictions and `y` targets. dimension of `x`. Must be `None` if `input_fn` is provided. * `monitors`: List of `BaseMonitor` subclass instances. Used for callbacks inside the training loop. +* `max_steps`: Number of total steps for which to train model. If `None`, + train forever. Two calls to `fit(steps=100)` means 200 training + iterations. On the other hand, two calls to `fit(max_steps=100)` means + that the second call will not do any iteration since first call did + all 100 steps. ##### Returns: @@ -188,12 +193,9 @@ Trains a model given training data `x` predictions and `y` targets. * `ValueError`: If `x` or `y` are not `None` while `input_fn` is not `None`. - -##### Raises: - - * `ValueError`: If at least one of `x` and `y` is provided, and `input_fn` is provided. +* `ValueError`: If both `steps` and `max_steps` are not `None`. - - - diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md index 73dd2481638529dd0829fe3f5daa7e994a92c7a1..d8c00dffac836ae45bf9e73555f6df1223c27d37 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md @@ -156,7 +156,7 @@ Evaluates given model with provided evaluation data. - - - -#### `tf.contrib.learn.DNNRegressor.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None)` {#DNNRegressor.fit} +#### `tf.contrib.learn.DNNRegressor.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)` {#DNNRegressor.fit} Trains a model given training data `x` predictions and `y` targets. @@ -177,6 +177,11 @@ Trains a model given training data `x` predictions and `y` targets. dimension of `x`. Must be `None` if `input_fn` is provided. * `monitors`: List of `BaseMonitor` subclass instances. Used for callbacks inside the training loop. +* `max_steps`: Number of total steps for which to train model. If `None`, + train forever. Two calls to `fit(steps=100)` means 200 training + iterations. On the other hand, two calls to `fit(max_steps=100)` means + that the second call will not do any iteration since first call did + all 100 steps. ##### Returns: @@ -186,12 +191,9 @@ Trains a model given training data `x` predictions and `y` targets. * `ValueError`: If `x` or `y` are not `None` while `input_fn` is not `None`. - -##### Raises: - - * `ValueError`: If at least one of `x` and `y` is provided, and `input_fn` is provided. +* `ValueError`: If both `steps` and `max_steps` are not `None`. - - -