def chain_string(self):
"""Basic info about the items in the chain.
Returns
-------
str
"""
output = ''
ui = FancyStringifier()
output += ui.row('ID',
'Processor',
'Input',
'Output',
'Init parameters set',
widths=[5, 30, 18, 18, 50]) + '\n'
output += ui.row('-', '-', '-', '-', '-') + '\n'
if len(self):
for item_id, item in enumerate(self):
if isinstance(item, ProcessingChainItem):
current_processor = self.processor_class_reference(
processor_name=item['processor_name'])
processor_name = item['processor_name'].split('.')[-1]
output += ui.row(
item_id, processor_name, current_processor.input_type,
current_processor.output_type, ','.join(
item.get('init_parameters', {}).keys())) + '\n'
else:
output += ui.row('Empty', widths=[95])
return output
def chain_string(self):
"""Basic info about the items in the chain.
Returns
-------
str
"""
output = ''
ui = FancyStringifier()
output += ui.row('ID',
'Processor',
'INPUT',
'OUTPUT',
'INIT',
widths=[5, 52, 18, 18, 25]) + '\n'
output += ui.row('-', '-', '-', '-', '-') + '\n'
if len(self):
for item_id, item in enumerate(self):
if isinstance(item, ProcessingChainItem):
current_processor = self.processor_class_reference(
processor_name=item['processor_name'])
output += ui.row(item_id, item['processor_name'],
current_processor.input_type,
current_processor.output_type,
item.get('init_parameters')) + '\n'
else:
output += ui.row('Empty', widths=[95])
return output
class ProgressLoggerCallback(BaseCallback):
"""Keras callback to show metrics in logging interface. Implements Keras Callback API.
This callback is very similar to standard ``ProgbarLogger`` Keras callback, however it adds support for logging
interface, and external metrics (metrics calculated outside Keras training process).
"""
def __init__(self,
manual_update=False,
epochs=None,
external_metric_labels=None,
metric=None,
loss=None,
manual_update_interval=1,
output_type='logging',
**kwargs):
"""Constructor
Parameters
----------
epochs : int
Total amount of epochs
Default value None
metric : str
Metric name
Default value None
manual_update : bool
Manually update callback, use this to when injecting external metrics
Default value False
manual_update_interval : int
Epoch interval for manual update, used anticipate updates
Default value 1
output_type : str
Output type, either 'logging' or 'console'
Default value 'logging'
external_metric_labels : dict or OrderedDict
Dictionary with {'metric_label': 'metric_name'}
Default value None
"""
kwargs.update({
'manual_update': manual_update,
'epochs': epochs,
'external_metric_labels': external_metric_labels,
})
super(ProgressLoggerCallback, self).__init__(**kwargs)
if isinstance(metric, str):
self.metric = metric
elif callable(metric):
self.metric = metric.__name__
self.loss = loss
self.manual_update_interval = manual_update_interval
self.output_type = output_type
self.timer = Timer()
self.ui = FancyStringifier()
if self.output_type == 'logging':
self.output_target = FancyLogger()
elif self.output_type == 'console':
self.output_target = FancyPrinter()
self.seen = 0
self.log_values = []
self.most_recent_values = collections.OrderedDict()
self.most_recent_values['l_tra'] = None
self.most_recent_values['l_val'] = None
self.most_recent_values['m_tra'] = None
self.most_recent_values['m_val'] = None
self.data = {
'l_tra': numpy.empty((self.epochs, )),
'l_val': numpy.empty((self.epochs, )),
'm_tra': numpy.empty((self.epochs, )),
'm_val': numpy.empty((self.epochs, )),
}
self.data['l_tra'][:] = numpy.nan
self.data['l_val'][:] = numpy.nan
self.data['m_tra'][:] = numpy.nan
self.data['m_val'][:] = numpy.nan
for metric_label in self.external_metric_labels:
self.data[metric_label] = numpy.empty((self.epochs, ))
self.data[metric_label][:] = numpy.nan
self.header_shown = False
self.last_update_epoch = 0
self.target = None
def on_train_begin(self, logs=None):
if self.epochs is None:
self.epochs = self.params['epochs']
if not self.header_shown:
output = ''
output += self.ui.line('Training') + '\n'
if self.external_metric_labels:
output += self.ui.row(
'',
'Loss',
'Metric',
'Ext. metrics',
'',
widths=[
10, 26, 26,
len(self.external_metric_labels) * 15, 17
]) + '\n'
header2 = ['', self.loss, self.metric]
header3 = ['Epoch', 'Train', 'Val', 'Train', 'Val']
widths = [10, 13, 13, 13, 13]
sep = ['-', '-', '-', '-', '-']
for metric_label, metric_name in iteritems(
self.external_metric_labels):
header2.append('')
header3.append(metric_name)
widths.append(15)
sep.append('-')
header2.append('')
header3.append('time')
widths.append(17)
sep.append('-')
output += self.ui.row(*header2) + '\n'
output += self.ui.row(*header3, widths=widths) + '\n'
output += self.ui.row(*sep)
else:
output += self.ui.row(
'', 'Loss', 'Metric', '', widths=[10, 26, 26, 17]) + '\n'
output += self.ui.row('', self.loss, self.metric, '') + '\n'
output += self.ui.row('Epoch',
'Train',
'Val',
'Train',
'Val',
'Time',
widths=[10, 13, 13, 13, 13, 17]) + '\n'
output += self.ui.row('-', '-', '-', '-', '-', '-')
self.output_target.line(output)
# Show header only once
self.header_shown = True
def on_epoch_begin(self, epoch, logs=None):
self.epoch = epoch + 1
if 'steps' in self.params:
self.target = self.params['steps']
elif 'samples' in self.params:
self.target = self.params['samples']
self.seen = 0
self.timer.start()
def on_batch_begin(self, batch, logs=None):
if self.target and self.seen < self.target:
self.log_values = []
def on_batch_end(self, batch, logs=None):
logs = logs or {}
batch_size = logs.get('size', 0)
self.seen += batch_size
for k in self.params['metrics']:
if k in logs:
self.log_values.append((k, logs[k]))
def on_epoch_end(self, epoch, logs=None):
self.timer.stop()
self.epoch = epoch
logs = logs or {}
# Reset values
self.most_recent_values['l_tra'] = None
self.most_recent_values['l_val'] = None
self.most_recent_values['m_tra'] = None
self.most_recent_values['m_val'] = None
# Collect values
for k in self.params['metrics']:
if k in logs:
self.log_values.append((k, logs[k]))
if k == 'loss':
self.data['l_tra'][self.epoch] = logs[k]
self.most_recent_values['l_tra'] = '{:4.3f}'.format(
logs[k])
elif k == 'val_loss':
self.data['l_val'][self.epoch] = logs[k]
self.most_recent_values['l_val'] = '{:4.3f}'.format(
logs[k])
elif self.metric and k.endswith(self.metric):
if k.startswith('val_'):
self.data['m_val'][self.epoch] = logs[k]
self.most_recent_values['m_val'] = '{:4.3f}'.format(
logs[k])
else:
self.data['m_tra'][self.epoch] = logs[k]
self.most_recent_values['m_tra'] = '{:4.3f}'.format(
logs[k])
for metric_label in self.external_metric_labels:
if metric_label in self.external_metric:
metric_name = self.external_metric_labels[metric_label]
value = self.external_metric[metric_label]
if metric_name.endswith('f_measure') or metric_name.endswith(
'f_score'):
self.most_recent_values[metric_label] = '{:3.1f}'.format(
value * 100)
else:
self.most_recent_values[metric_label] = '{:4.3f}'.format(
value)
if (not self.manual_update
or (self.epoch - self.last_update_epoch > 0 and
(self.epoch + 1) % self.manual_update_interval)):
# Update logged progress
self.update_progress_log()
def update(self):
"""Update
"""
self.update_progress_log()
self.last_update_epoch = self.epoch
def update_progress_log(self):
"""Update progress to logging interface
"""
if self.epoch - self.last_update_epoch:
data = [
self.epoch, self.data['l_tra'][self.epoch],
self.data['l_val'][self.epoch]
if 'l_val' in self.most_recent_values else '-',
self.data['m_tra'][self.epoch], self.data['m_val'][self.epoch]
if self.most_recent_values['m_val'] else '-'
]
types = ['int', 'float4', 'float4', 'float4', 'float4']
for metric_label in self.external_metric_labels:
if metric_label in self.external_metric:
value = self.data[metric_label][self.epoch]
if numpy.isnan(value):
value = ' ' * 10
else:
if self.external_metric_labels[metric_label].endswith(
'f_measure') or self.external_metric_labels[
metric_label].endswith('f_score'):
value = float(value) * 100
types.append('float2')
else:
value = float(value)
types.append('float4')
data.append(value)
else:
data.append('')
data.append(self.timer.get_string())
types.append('str')
output = self.ui.row(*data, types=types)
self.output_target.line(output)
def add_external_metric(self, metric_id):
"""Add external metric to be monitored
Parameters
----------
metric_id : str
Metric name
"""
if metric_id not in self.external_metric_labels:
self.external_metric_labels[metric_id] = metric_id
if metric_id not in self.data:
self.data[metric_id] = numpy.empty((self.epochs, ))
self.data[metric_id][:] = numpy.nan
def set_external_metric_value(self, metric_label, metric_value):
"""Add external metric value
Parameters
----------
metric_label : str
Metric label
metric_value : numeric
Metric value
"""
self.external_metric[metric_label] = metric_value
self.data[metric_label][self.epoch] = metric_value
def model_summary_string(keras_model, mode='keras'):
"""Model summary in a formatted string, similar to Keras model summary function.
Parameters
----------
keras_model : keras model
Keras model
mode : str
Summary mode ['extended', 'keras']. In case 'keras', standard Keras summary is returned.
Default value keras
Returns
-------
str
Model summary
"""
ui = FancyStringifier()
output = ''
output += ui.line('Model summary') + '\n'
if mode == 'extended':
layer_name_map = {
'BatchNormalization': 'BatchNorm',
}
import keras
from distutils.version import LooseVersion
import keras.backend as keras_backend
output += ui.row('Layer type',
'Output',
'Param',
'Name',
'Connected to',
'Activ.',
'Init',
widths=[15, 25, 10, 20, 25, 10, 10],
indent=4) + '\n'
output += ui.row('-', '-', '-', '-', '-', '-', '-') + '\n'
for layer in keras_model.layers:
connections = []
if LooseVersion(keras.__version__) >= LooseVersion('2.1.3'):
for node_index, node in enumerate(layer._inbound_nodes):
for i in range(len(node.inbound_layers)):
inbound_layer = node.inbound_layers[i].name
inbound_node_index = node.node_indices[i]
inbound_tensor_index = node.tensor_indices[i]
connections.append(inbound_layer + '[' +
str(inbound_node_index) + '][' +
str(inbound_tensor_index) + ']')
else:
for node_index, node in enumerate(layer.inbound_nodes):
for i in range(len(node.inbound_layers)):
inbound_layer = node.inbound_layers[i].name
inbound_node_index = node.node_indices[i]
inbound_tensor_index = node.tensor_indices[i]
connections.append(inbound_layer + '[' +
str(inbound_node_index) + '][' +
str(inbound_tensor_index) + ']')
config = DictContainer(layer.get_config())
layer_name = layer.__class__.__name__
if layer_name in layer_name_map:
layer_name = layer_name_map[layer_name]
if config.get_path(
'kernel_initializer.class_name') == 'VarianceScaling':
init = str(
config.get_path('kernel_initializer.config.distribution',
'---'))
elif config.get_path(
'kernel_initializer.class_name') == 'RandomUniform':
init = 'uniform'
else:
init = '---'
output += ui.row(
layer_name, str(layer.output_shape), str(layer.count_params()),
str(layer.name),
str(connections[0]) if len(connections) > 0 else '---',
str(config.get('activation', '---')), init) + '\n'
trainable_count = int(
numpy.sum([
keras_backend.count_params(p)
for p in set(keras_model.trainable_weights)
]))
non_trainable_count = int(
numpy.sum([
keras_backend.count_params(p)
for p in set(keras_model.non_trainable_weights)
]))
output += ui.line('') + '\n'
output += ui.line(
'Parameters',
indent=4,
) + '\n'
output += ui.data(indent=6,
field='Total',
value=trainable_count + non_trainable_count) + '\n'
output += ui.data(indent=6, field='Trainable',
value=trainable_count) + '\n'
output += ui.data(
indent=6, field='Non-Trainable', value=non_trainable_count) + '\n'
else:
output_buffer = []
keras_model.summary(print_fn=output_buffer.append)
for line in output_buffer:
output += ui.line(line, indent=4) + '\n'
output += ui.line('') + '\n'
output += ui.data(
indent=4, field='Input shape', value=keras_model.input_shape) + '\n'
output += ui.data(
indent=4, field='Output shape', value=keras_model.output_shape) + '\n'
return output
