diff options
Diffstat (limited to 'tensorflow/contrib/kfac/examples/convnet.py')
| -rw-r--r-- | tensorflow/contrib/kfac/examples/convnet.py | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/tensorflow/contrib/kfac/examples/convnet.py b/tensorflow/contrib/kfac/examples/convnet.py index d6b1a61b71..44e01e1aeb 100644 --- a/tensorflow/contrib/kfac/examples/convnet.py +++ b/tensorflow/contrib/kfac/examples/convnet.py @@ -202,7 +202,7 @@ def minimize_loss_single_machine(loss, accuracy: 0-D Tensor. Accuracy of classifier on current minibatch. layer_collection: LayerCollection instance describing model architecture. Used by K-FAC to construct preconditioner. - device: string, Either '/cpu:0' or '/gpu:0'. The covaraince and invserse + device: string, Either '/cpu:0' or '/gpu:0'. The covariance and inverse update ops are run on this device. session_config: None or tf.ConfigProto. Configuration for tf.Session(). @@ -470,7 +470,7 @@ def train_mnist_single_machine(data_dir, data_dir: string. Directory to read MNIST examples from. num_epochs: int. Number of passes to make over the training set. use_fake_data: bool. If True, generate a synthetic dataset. - device: string, Either '/cpu:0' or '/gpu:0'. The covaraince and inverse + device: string, Either '/cpu:0' or '/gpu:0'. The covariance and inverse update ops are run on this device. Returns: @@ -509,7 +509,7 @@ def train_mnist_multitower(data_dir, num_epochs, num_towers, num_epochs: int. Number of passes to make over the training set. num_towers: int. Number of CPUs to split inference across. use_fake_data: bool. If True, generate a synthetic dataset. - devices: string, Either list of CPU or GPU. The covaraince and inverse + devices: string, Either list of CPU or GPU. The covariance and inverse update ops are run on this device. Returns: @@ -621,7 +621,7 @@ def train_mnist_distributed_sync_replicas(task_id, data_dir: string. Directory to read MNIST examples from. num_epochs: int. Number of passes to make over the training set. op_strategy: `string`, Strategy to run the covariance and inverse - ops. If op_strategy == `chief_worker` then covaraiance and inverse + ops. If op_strategy == `chief_worker` then covariance and inverse update ops are run on chief worker otherwise they are run on dedicated workers. |