Tensorboard basic visualizations.
This callback writes a log for TensorBoard, which allows you to visualize dynamic graphs of your training and test metrics, as well as activation histograms for the different layers in your model.
TensorBoard is a visualization tool provided with TensorFlow.
If you have installed TensorFlow with pip, you should be able to launch TensorBoard from the command line:
You can find more information about TensorBoard here.
log_dir: the path of the directory where to save the log files to be parsed by TensorBoard.
histogram_freq: frequency (in epochs) at which to compute activation and weight histograms for the layers of the model. If set to 0, histograms won't be computed. Validation data (or split) must be specified for histogram visualizations.
write_graph: whether to visualize the graph in TensorBoard. The log file can become quite large when write_graph is set to True.
write_grads: whether to visualize gradient histograms in TensorBoard.
histogram_freqmust be greater than 0.
batch_size: size of batch of inputs to feed to the network for histograms computation.
write_images: whether to write model weights to visualize as image in TensorBoard.
embeddings_freq: frequency (in epochs) at which selected embedding layers will be saved. If set to 0, embeddings won't be computed. Data to be visualized in TensorBoard's Embedding tab must be passed as
embeddings_layer_names: a list of names of layers to keep eye on. If None or empty list all the embedding layer will be watched.
embeddings_metadata: a dictionary which maps layer name to a file name in which metadata for this embedding layer is saved. See the details about metadata files format. In case if the same metadata file is used for all embedding layers, string can be passed.
embeddings_data: data to be embedded at layers specified in
embeddings_layer_names. Numpy array (if the model has a single input) or list of Numpy arrays (if the model has multiple inputs). Learn more about embeddings
'epoch'or integer. When using
'batch', writes the losses and metrics to TensorBoard after each batch. The same applies for
'epoch'. If using an integer, let's say
1000, the callback will write the metrics and losses to TensorBoard every 1000 samples. Note that writing too frequently to TensorBoard can slow down your training.
ValueError: If histogram_freq is set and no validation data is provided.
Tensorboard callback will work while eager execution is enabled,
however outputting histogram summaries of weights and gradients is not
supported, and thus
histogram_freq will be ignored.
__init__( log_dir='./logs', histogram_freq=0, batch_size=32, write_graph=True, write_grads=False, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None, embeddings_data=None, update_freq='epoch' )
Initialize self. See help(type(self)) for accurate signature.
on_batch_begin( batch, logs=None )
on_batch_end( batch, logs=None )
Writes scalar summaries for metrics on every training batch.
on_epoch_begin( epoch, logs=None )
Add histogram op to Model eval_function callbacks, reset batch count.
on_epoch_end( epoch, logs=None )
Checks if summary ops should run next epoch, logs scalar summaries.
on_train_batch_begin( batch, logs=None )
on_train_batch_end( batch, logs=None )
Sets Keras model and creates summary ops.