get_summary
tflearn.summaries.get_summary (stype, tag, value=None, collection_key=None, break_if_exists=False)
Create or retrieve a summary. It keep tracks of all graph summaries through summary_tags collection. If a summary tags already exists, it will return that summary tensor or raise an error (according to 'break_if_exists').
Arguments
- stype:
str. Summary type: 'histogram', 'scalar' or 'image'. - tag:
str. The summary tag (name). - value:
Tensor. The summary initialization value. Default: None. - collection_key:
str. If specified, the created summary will be added to that collection (optional). - break_if_exists:
bool. If True, if a summary with same tag already exists, it will raise an exception (instead of returning that existing summary).
Returns
The summary Tensor.
add_activations_summary
tflearn.summaries.add_activations_summary (activation_ops, name_prefix='', name_suffix='', collection_key=None)
Add histogram summary for given activations.
Arguments
- activation_ops: A list of
Tensor. The activations to summarize. - name_prefix:
str. A prefix to add to summary scope. - name_suffix:
str. A suffix to add to summary scope. - collection_key:
str. A collection to store the summaries.
Returns
The list of created activation summaries.
add_gradients_summary
tflearn.summaries.add_gradients_summary (grads, name_prefix='', name_suffix='', collection_key=None)
Add histogram summary for given gradients.
Arguments
- grads: A list of
Tensor. The gradients to summarize. - name_prefix:
str. A prefix to add to summary scope. - name_suffix:
str. A suffix to add to summary scope. - collection_key:
str. A collection to store the summaries.
Returns
The list of created gradient summaries.
add_trainable_vars_summary
tflearn.summaries.add_trainable_vars_summary (variables, name_prefix='', name_suffix='', collection_key=None)
Add histogram summary for given variables weights.
Arguments
- variables: A list of
Variable. The variables to summarize. - name_prefix:
str. A prefix to add to summary scope. - name_suffix:
str. A suffix to add to summary scope. - collection_key:
str. A collection to store the summaries.
Returns
The list of created weights summaries.
get_value_from_summary_string
tflearn.summaries.get_value_from_summary_string (tag, summary_str)
Retrieve a summary value from a summary string.
Arguments
- tag:
str. The summary tag (name). - summary_str:
str. The summary string to look in.
Returns
A float. The retrieved value.
add_loss_summaries
tflearn.summaries.add_loss_summaries (total_loss, loss, regul_losses_collection_key, name_prefix='', summaries_collection_key=None, exp_moving_avg=0.9, ema_num_updates=None)
Add scalar summaries (raw and averages) for given losses.
Generates moving average for all losses and associated summaries for visualizing the performance of the network.
Arguments
- total_loss:
Tensor. The total loss (Regression loss + regularization losses). - loss:
Tensor. Regression loss. - name_prefix:
str. A prefix to add to the summary name. - regul_losses_collection_key:
str. A collection name to retrieve regularization losses. - exp_moving_avg:
float. Exponential moving average. - ema_num_updates:
int. Step to be used with exp moving avg.
Returns
loss_averages_op: op for generating moving averages of losses.
summary_exists
tflearn.summaries.summary_exists (tag)
Check if a summary exists.
Arguments
- tag:
str. The summary name.
Returns
A bool. Whether the summary exists or not.