def __str__(self):
ui = FancyStringifier()
output = super(OneHotEncoder, self).__str__()
output += ui.line(field='Data') + '\n'
output += ui.data(indent=4, field='data', value=self.data) + '\n'
output += ui.line(indent=4, field='Dimensions') + '\n'
output += ui.data(indent=6, field='time_axis',
value=self.time_axis) + '\n'
output += ui.data(indent=6, field='data_axis',
value=self.data_axis) + '\n'
output += ui.line(indent=4, field='Timing information') + '\n'
output += ui.data(indent=6,
field='time_resolution',
value=self.time_resolution,
unit="sec") + '\n'
output += ui.line(field='Duration') + '\n'
output += ui.data(indent=6, field='Frames', value=self.length) + '\n'
if self.time_resolution:
output += ui.data(indent=6,
field='Seconds',
value=self._frame_to_time(frame_id=self.length),
unit='sec') + '\n'
output += ui.line(indent=4, field='Labels') + '\n'
output += ui.data(indent=6, field='label_list',
value=self.label_list) + '\n'
return output
def __str__(self):
"""File information
Returns
-------
str
"""
ui = FancyStringifier()
output = ''
output += ui.title(text=self.__class__.__name__) + '\n'
output += ui.data(field='Content type', value=self.content_type) + '\n'
output += ui.line(field='Local') + '\n'
output += ui.data(indent=4, field='filename', value=self.filename) + '\n'
output += ui.data(indent=4, field='local_md5', value=self.local_md5) + '\n'
output += ui.data(indent=4, field='Exists', value='Yes' if self.local_exists() else 'No') + '\n'
output += ui.data(indent=4, field='Size', value=self.local_size_string()) + '\n'
if self._remote_file is not None:
output += ui.line(field='Remote') + '\n'
output += ui.data(indent=4, field='remote_file', value=self.remote_file) + '\n'
output += ui.data(indent=4, field='remote_md5', value=self.remote_md5) + '\n'
output += ui.data(indent=4, field='Exists', value='Yes' if self.remote_exists() else 'No') + '\n'
output += ui.data(indent=4, field='Size', value=self.remote_size_string()) + '\n'
return output
def to_string(self, ui=None, indent=0):
"""Get container information in a string
Parameters
----------
ui : FancyStringifier or FancyHTMLStringifier
Stringifier class
Default value FancyStringifier
indent : int
Amount of indention used
Default value 0
Returns
-------
str
"""
if ui is None:
ui = FancyStringifier()
output = ''
output += ui.class_name(self.__class__.__name__, indent=indent) + '\n'
output += ui.data(field='Content type',
value=self.content_type,
indent=indent) + '\n'
output += ui.line(field='Local', indent=indent) + '\n'
output += ui.data(
indent=indent + 2, field='filename', value=self.filename) + '\n'
output += ui.data(
indent=indent + 2, field='local_md5', value=self.local_md5) + '\n'
output += ui.data(indent=indent + 2,
field='Exists',
value='Yes' if self.local_exists() else 'No') + '\n'
output += ui.data(indent=indent + 2,
field='Size',
value=self.local_size_string()) + '\n'
if self._remote_file is not None:
output += ui.line(field='Remote', indent=indent) + '\n'
output += ui.data(indent=indent + 2,
field='remote_file',
value=self.remote_file) + '\n'
output += ui.data(indent=indent + 2,
field='remote_md5',
value=self.remote_md5) + '\n'
output += ui.data(
indent=indent + 2,
field='Exists',
value='Yes' if self.remote_exists() else 'No') + '\n'
output += ui.data(indent=indent + 2,
field='Size',
value=self.remote_size_string()) + '\n'
return output
def to_string(self, ui=None, indent=0):
"""Get container information in a string
Parameters
----------
ui : FancyStringifier or FancyHTMLStringifier
Stringifier class
Default value FancyStringifier
indent : int
Amount of indention used
Default value 0
Returns
-------
str
"""
if ui is None:
ui = FancyStringifier()
output = ''
output += ui.class_name(self.__class__.__name__, indent=indent) + '\n'
if hasattr(self, 'filename') and self.filename:
output += ui.data(
field='filename', value=self.filename, indent=indent) + '\n'
if self._file_info is None:
self.get_info()
output += ui.line('Size', indent=indent) + '\n'
output += ui.data(field='Uncompressed',
value=get_byte_string(self._size_uncompressed),
indent=indent + 2) + '\n'
if self.format == FileFormat.ZIP:
output += ui.data(field='Compressed',
value=get_byte_string(self._size_compressed),
indent=indent + 2) + '\n'
output += ui.data(field='Ratio',
value=self._size_compressed /
float(self._size_uncompressed) * 100,
unit='%',
indent=indent + 2) + '\n'
output += ui.line('Files', indent=indent) + '\n'
output += ui.data(field='Count',
value=len(self._file_info),
indent=indent + 2) + '\n'
return output
def to_string(self, ui=None, indent=0):
"""Get container information in a string
Parameters
----------
ui : FancyStringifier or FancyHTMLStringifier
Stringifier class
Default value FancyStringifier
indent : int
Amount of indent
Default value 0
Returns
-------
str
"""
if ui is None:
ui = FancyStringifier()
output = ''
output += ui.class_name(self.__class__.__name__, indent=indent) + '\n'
if hasattr(self, 'filename') and self.filename:
output += ui.data(field='filename', value=self.filename) + '\n'
for item_id, item in enumerate(self):
output += ui.line(field='[' + str(item_id) + ']', indent=indent) + '\n'
output += DictContainer(item).to_string(ui=ui, indent=indent + 1) + '\n'
return output
def to_string(self, ui=None, indent=0):
"""Get information in a string
Parameters
----------
ui : FancyStringifier or FancyHTMLStringifier
Stringifier class
Default value FancyStringifier
indent : int
Amount of indent
Default value 0
Returns
-------
str
"""
if ui is None:
ui = FancyStringifier()
output = ''
output += ui.class_name(self.__class__.__name__, indent=indent) + '\n'
output += ui.data(field='Best model weights at epoch', value=self.best_model_epoch, indent=indent) + '\n'
output += ui.data(field='Metric type', value=self.monitor, indent=indent+2) + '\n'
output += ui.data(field='Metric value', value=self.best, indent=indent+2) + '\n'
output += ui.line()
return output
def to_string(self, ui=None, indent=0):
"""Get container information in a string
Parameters
----------
ui : FancyStringifier or FancyHTMLStringifier
Stringifier class
Default value FancyStringifier
indent : int
Amount of indention used
Default value 0
Returns
-------
str
"""
if ui is None:
ui = FancyStringifier()
output = super(Sequencer, self).to_string(ui=ui, indent=indent)
output += ui.data(field='frames', value=self.sequence_length, indent=indent) + '\n'
output += ui.data(field='hop_length_frames', value=self.hop_length, indent=indent) + '\n'
output += ui.data(field='padding', value=self.padding, indent=indent) + '\n'
output += ui.data(field='required_data_amount_per_segment', value=self.required_data_amount_per_segment, indent=indent) + '\n'
output += ui.line(field='Shifting', indent=indent) + '\n'
output += ui.data(indent=indent + 2, field='shift', value=self.shift) + '\n'
output += ui.data(indent=indent + 2, field='shift_border', value=self.shift_border) + '\n'
return output
def __str__(self):
ui = FancyStringifier()
output = ''
output += FancyStringifier().class_name(self.__class__.__name__) + '\n'
if hasattr(self, 'filename') and self.filename:
output += FancyStringifier().data(field='filename',
value=self.filename) + '\n'
output += ui.data(field='fs', value=self.fs) + '\n'
output += ui.line(field='Frame blocking') + '\n'
output += ui.data(indent=4,
field='hop_length_samples',
value=self.hop_length_samples) + '\n'
output += ui.data(indent=4,
field='hop_length_seconds',
value=self.hop_length_seconds,
unit='sec') + '\n'
output += ui.data(indent=4,
field='win_length_samples',
value=self.win_length_samples) + '\n'
output += ui.data(indent=4,
field='win_length_seconds',
value=self.win_length_seconds,
unit='sec') + '\n'
return output
def __str__(self):
ui = FancyStringifier()
output = super(ZeroCrossingRateExtractor, self).__str__()
output += ui.line(field='ZCR') + '\n'
output += ui.data(indent=4, field='center', value=self.center) + '\n'
return output
def __str__(self):
ui = FancyStringifier()
output = super(SpectralCentroidExtractor, self).__str__()
output += ui.line(field='SpectralCentroid') + '\n'
output += ui.data(indent=4, field='center', value=self.center) + '\n'
return output
def __str__(self):
ui = FancyStringifier()
output = super(MfccDeltaExtractor, self).__str__()
output += ui.line(field='Delta') + '\n'
output += ui.data(indent=4, field='width', value=self.width) + '\n'
return output
def __str__(self):
ui = FancyStringifier()
output = super(RMSEnergyExtractor, self).__str__()
output += ui.line(field='RMSEnergy') + '\n'
output += ui.data(indent=4, field='center', value=self.center) + '\n'
return output
def __str__(self):
ui = FancyStringifier()
output = ui.class_name(self.__class__.__name__) + '\n'
output += ui.line(field='Meta') + '\n'
if self.filename:
output += ui.data(indent=4, field='filename',
value=self.filename) + '\n'
if self.label:
output += ui.data(indent=4, field='label', value=self.label) + '\n'
if self.probability is not None:
output += ui.data(
indent=4, field='probability', value=self.probability) + '\n'
output += ui.line(field='Item') + '\n'
output += ui.data(indent=4, field='id', value=self.id) + '\n'
return output
def to_string(self, ui=None, indent=0):
"""Get container information in a string
Parameters
----------
ui : FancyStringifier or FancyHTMLStringifier
Stringifier class
Default value FancyStringifier
indent : int
Amount of indention used
Default value 0
Returns
-------
str
"""
if ui is None:
ui = FancyStringifier()
output = super(EventRollEncoder, self).to_string(ui=ui, indent=indent)
output += ui.line(field='Data', indent=indent) + '\n'
output += ui.data(indent=indent + 2, field='data', value=self.data) + '\n'
output += ui.line(indent=indent + 2, field='Dimensions') + '\n'
output += ui.data(indent=indent + 4, field='time_axis', value=self.time_axis) + '\n'
output += ui.data(indent=indent + 4, field='data_axis', value=self.data_axis) + '\n'
output += ui.line(indent=indent + 2, field='Timing information') + '\n'
output += ui.data(indent=indent + 4, field='time_resolution', value=self.time_resolution, unit="sec") + '\n'
output += ui.line(field='Duration', indent=indent) + '\n'
output += ui.data(indent=indent + 4, field='Frames', value=self.length) + '\n'
if self.time_resolution:
output += ui.data(indent=indent + 4, field='Seconds', value=self._frame_to_time(frame_id=self.length), unit='sec') + '\n'
output += ui.line(indent=indent + 2, field='Labels') + '\n'
output += ui.data(indent=indent + 4, field='Label list', value=self.label_list) + '\n'
output += ui.data(indent=indent + 4, field='label_field', value=self.label) + '\n'
return output
def to_string(self, ui=None, indent=0):
"""Get container information in a string
Parameters
----------
ui : FancyStringifier or FancyHTMLStringifier
Stringifier class
Default value FancyStringifier
indent : int
Amount of indent
Default value 0
Returns
-------
str
"""
if ui is None:
ui = FancyStringifier()
output = ''
output += ui.class_name(self.__class__.__name__, indent=indent) + '\n'
output += ui.line(field='Meta', indent=indent) + '\n'
if self.filename:
output += ui.data(indent=indent + 2,
field='filename',
value=self.filename) + '\n'
if self.label:
output += ui.data(
indent=indent + 2, field='label', value=self.label) + '\n'
if self.probability is not None:
output += ui.data(indent=indent + 2,
field='probability',
value=self.probability) + '\n'
output += ui.line(field='Item', indent=indent) + '\n'
output += ui.data(indent=indent + 2, field='id', value=self.id) + '\n'
return output
def __str__(self):
ui = FancyStringifier()
output = super(DataBuffer, self).__str__()
output += ui.data(field='size', value=self.size) + '\n'
output += ui.data(field='index', value=self.index) + '\n'
output += ui.line(field='Buffer') + '\n'
output += ui.data(indent=4, field='data_buffer', value=self.data_buffer) + '\n'
output += ui.data(indent=4, field='meta_buffer', value=self.meta_buffer) + '\n'
return output
def __str__(self):
ui = FancyStringifier()
output = super(SpectralFeatureExtractor, self).__str__()
output += ui.line(field='Spectrogram') + '\n'
output += ui.data(indent=4,
field='spectrogram_type',
value=self.spectrogram_type) + '\n'
output += ui.data(indent=4, field='n_fft', value=self.n_fft) + '\n'
output += ui.data(
indent=4, field='window_type', value=self.window_type) + '\n'
return output
def __str__(self):
ui = FancyStringifier()
output = ''
output += FancyStringifier().class_name(self.__class__.__name__) + '\n'
if hasattr(self, 'filename') and self.filename:
output += FancyStringifier().data(field='filename',
value=self.filename) + '\n'
if self._file_info is None:
self.get_info()
output += ui.line('Size', indent=2) + '\n'
output += FancyStringifier().data(field='Uncompressed',
value=get_byte_string(
self._size_uncompressed),
indent=4) + '\n'
if self.format == FileFormat.ZIP:
output += FancyStringifier().data(field='Compressed',
value=get_byte_string(
self._size_compressed),
indent=4) + '\n'
output += FancyStringifier().data(
field='Ratio',
value=self._size_compressed / float(self._size_uncompressed) *
100,
unit='%',
indent=4) + '\n'
output += ui.line('Files', indent=2) + '\n'
output += FancyStringifier().data(
field='Count', value=len(self._file_info), indent=4) + '\n'
return output
def __str__(self):
ui = FancyStringifier()
output = ''
output += FancyStringifier().class_name(self.__class__.__name__) + '\n'
if hasattr(self, 'filename') and self.filename:
output += FancyStringifier().data(field='filename',
value=self.filename) + '\n'
for item_id, item in enumerate(self):
output += ui.line('[' + str(item_id) + ']', indent=0) + '\n'
output += ui.line(str(DictContainer(item)), indent=3) + '\n'
return output
def __str__(self):
ui = FancyStringifier()
output = super(MfccStaticExtractor, self).__str__()
output += ui.line(field='MFCC') + '\n'
output += ui.data(indent=4, field='n_mels', value=self.n_mels) + '\n'
output += ui.data(indent=4, field='fmin', value=self.fmin) + '\n'
output += ui.data(indent=4, field='fmax', value=self.fmax) + '\n'
output += ui.data(indent=4,
field='normalize_mel_bands',
value=self.normalize_mel_bands) + '\n'
output += ui.data(indent=4, field='htk', value=self.htk) + '\n'
output += ui.data(indent=4, field='n_mfcc', value=self.n_mfcc) + '\n'
return output
def __str__(self):
ui = FancyStringifier()
output = ''
output += ui.class_name(self.__class__.__name__) + '\n'
output += ui.data(
indent=2, field='Batch size', value=self.batch_size) + '\n'
output += ui.data(
indent=2, field='Epoch size', value=len(self),
unit='batches') + '\n'
shape = self.data_shape
axis = self.data_axis
output += ui.data(field='Data item shape', value=shape) + '\n'
output += ui.data(
indent=4, field='Time', value=shape[axis['time_axis']]) + '\n'
output += ui.data(
indent=4, field='Data', value=shape[axis['data_axis']]) + '\n'
if 'sequence_axis' in axis:
output += ui.data(indent=4,
field='Sequence',
value=shape[axis['sequence_axis']]) + '\n'
output += ui.data(indent=4, field='Axis', value=axis) + '\n'
if self.buffer_size is not None:
output += ui.line(field='Buffer') + '\n'
output += ui.data(indent=4,
field='buffer_size',
value=self.buffer_size,
unit='items') + '\n'
output += ui.data(indent=4,
field='buffer usage',
value=self.data_buffer.count,
unit='items') + '\n'
output += ui.data(
indent=4,
field='buffer usage',
value=(self.data_buffer.count / float(self.buffer_size)) *
100,
unit='%') + '\n'
return output
def __str__(self):
ui = FancyStringifier()
output = super(Sequencer, self).__str__()
output += ui.data(field='frames', value=self.frames) + '\n'
output += ui.data(field='hop_length_frames',
value=self.hop_length_frames) + '\n'
output += ui.data(field='padding', value=self.padding) + '\n'
output += ui.data(field='required_data_amount_per_segment',
value=self.required_data_amount_per_segment) + '\n'
output += ui.line(field='Shifting') + '\n'
output += ui.data(indent=4, field='shift', value=self.shift) + '\n'
output += ui.data(
indent=4, field='shift_border', value=self.shift_border) + '\n'
return output
def to_string(self, ui=None, indent=0):
"""Get container information in a string
Parameters
----------
ui : FancyStringifier or FancyHTMLStringifier
Stringifier class
Default value FancyStringifier
indent : int
Amount of indention used
Default value 0
Returns
-------
str
"""
if ui is None:
ui = FancyStringifier()
output = super(RepositoryNormalizer, self).to_string(ui=ui, indent=indent)
output += ui.data(
indent=indent + 2,
field='Labels',
value=list(self.normalizers.keys())
) + '\n'
output += ui.line(field='Content') + '\n'
for label, label_data in iteritems(self.normalizers):
if label_data:
output += ui.data(
indent=indent + 2,
field='['+str(label)+']',
value=label_data
) + '\n'
output += '\n'
return output
def to_string(self, ui=None, indent=0):
"""Get container information in a string
Parameters
----------
ui : FancyStringifier or FancyHTMLStringifier
Stringifier class
Default value FancyStringifier
indent : int
Amount of indention used
Default value 0
Returns
-------
str
"""
if ui is None:
ui = FancyStringifier()
output = super(DataBuffer, self).to_string(ui=ui, indent=indent)
output += ui.data(field='size', value=self.size, indent=indent) + '\n'
output += ui.data(field='index', value=self.index,
indent=indent) + '\n'
output += ui.line(field='Buffer', indent=indent) + '\n'
output += ui.data(indent=indent + 2,
field='data_buffer',
value=self.data_buffer) + '\n'
output += ui.data(indent=indent + 2,
field='meta_buffer',
value=self.meta_buffer) + '\n'
return output
def to_string(self, ui=None, indent=0):
"""Get container information in a string
Parameters
----------
ui : FancyStringifier or FancyHTMLStringifier
Stringifier class
Default value FancyStringifier
indent : int
Amount of indention used
Default value 0
Returns
-------
str
"""
if ui is None:
ui = FancyStringifier()
output = ''
output += ui.class_name(self.__class__.__name__,
indent=indent) + '\n'
output += ui.data(indent=indent,
field='Batch size',
value=self.batch_size) + '\n'
output += ui.data(indent=indent,
field='Epoch size',
value=len(self),
unit='batches') + '\n'
shape = self.data_shape
axis = self.data_axis
output += ui.data(
field='Data item shape', value=shape, indent=indent) + '\n'
output += ui.data(indent=indent + 2,
field='Time',
value=shape[axis['time_axis']]) + '\n'
output += ui.data(indent=indent + 2,
field='Data',
value=shape[axis['data_axis']]) + '\n'
if 'sequence_axis' in axis:
output += ui.data(indent=indent + 2,
field='Sequence',
value=shape[axis['sequence_axis']]) + '\n'
output += ui.data(indent=indent + 2, field='Axis',
value=axis) + '\n'
if self.buffer_size is not None:
output += ui.line(field='Buffer', indent=indent) + '\n'
output += ui.data(indent=indent + 2,
field='buffer_size',
value=self.buffer_size,
unit='items') + '\n'
output += ui.data(indent=indent + 2,
field='buffer usage',
value=self.data_buffer.count,
unit='items') + '\n'
output += ui.data(
indent=indent + 2,
field='buffer usage',
value=(self.data_buffer.count / float(self.buffer_size)) *
100,
unit='%') + '\n'
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
