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How to use _output_summary method in stestr

Best Python code snippet using stestr_python

utils_nn.py

Source:utils_nn.py

...87	tf.summary.histogram(params.op.name, params)88	tf.summary.histogram(params.op.name + '/spartisty', tf.nn.zero_fraction(params))89def _image_summary(params):90	tf.summary.image(params.op.name, params, max_outputs=1)91def _output_summary(outputs):92	tf.summary.histogram(outputs.op.name + '/outputs', outputs)93	tf.summary.scalar(outputs.op.name + '/outputs_sparsity',94		tf.nn.zero_fraction(outputs))95def get_weight_placeholders():96    return tf.get_collection('weight_placeholder')97def get_mask_placeholders():98    return tf.get_collection('mask_placeholder')99#################################################################################100#LAYERS101#################################################################################102def fully_connected_layer(x, units, name, gpu, config):103	'''104	Args:105		x: inputs from the previous layer, shape = [None, x.shape[0]].106		units: # of nodes of this layer, scalar.107		name: the name of this layer in graph.108		config: yaml file for the configuration.109	Returns:110		h: outputs, 1D tensor, shape = [None, units]111	'''112	with tf.variable_scope(name) as scope:113		# initialize layer parameters114		weights = _weights_initializer(name='weights',115			shape=[x.get_shape().as_list()[1], units],116			stddev=np.sqrt(2/units),117			gpu=gpu,118			config=config)119		biases = _biases_initializer(name='biases',120			shape=[units],121			val=0.1,122			gpu=gpu,123			config=config)124		# initilize and apply mask if specified to125		if config['use_mask']:126			mask = _mask_initializer(name='mask',127				shape=weights.get_shape().as_list(),128				gpu=gpu)129			# Apply mask to the weights130			masked_weights = tf.multiply(mask, weights, name='masked_weights')131			_parameter_summary(masked_weights)132			new_shape = masked_weights.get_shape().as_list()133			new_shape.insert(0,1)134			new_shape.append(1)135			_image_summary(tf.reshape(masked_weights, new_shape))136			# Calculate the output of the fully connected layer137			h = tf.add(tf.matmul(x, masked_weights), biases, name=name)138		else:139			h = tf.add(tf.matmul(x, weights), biases, name=name)140		# Adds summary to the weights and outputs141		_parameter_summary(weights)142		_parameter_summary(mask)143		_output_summary(h)144		if config['use_owl'] | config['use_growl'] | config['use_group_lasso']:145			# create interface for OWL146			w_placeholder = tf.placeholder(tf.float32, [x.get_shape().as_list()[1], units])147			assign_op_w = tf.assign(weights, w_placeholder, validate_shape=True)148			tf.add_to_collection('weight_placeholder', (weights, w_placeholder, assign_op_w))149			mask_placeholder = tf.placeholder(tf.float32, [x.get_shape().as_list()[1], units])150			assign_op_m = tf.assign(mask, mask_placeholder, validate_shape=True)151			tf.add_to_collection('mask_placeholder', (mask, mask_placeholder, assign_op_m))152	return h153def relu_layer(x, name):154	'''155	Args:156		x: inputs from the previous layer.157	Returns:158		h: activation.159	'''160	with tf.variable_scope(name) as scope:161		h = tf.nn.relu(x, name=name)162		_output_summary(h)163	return h164def batch_normalization_layer(x, axis, phase, name):165	'''166	Args:167		x: inputs.168		phase: boolean, true for training, false for testing.169	Returns:170		h: results.171	'''172	h = tf.layers.batch_normalization(x, axis=axis, training=phase, name=name)...

utils_nn-checkpoint.py

Source:utils_nn-checkpoint.py

...87	tf.summary.histogram(params.op.name, params)88	tf.summary.histogram(params.op.name + '/spartisty', tf.nn.zero_fraction(params))89def _image_summary(params):90	tf.summary.image(params.op.name, params, max_outputs=1)91def _output_summary(outputs):92	tf.summary.histogram(outputs.op.name + '/outputs', outputs)93	tf.summary.scalar(outputs.op.name + '/outputs_sparsity',94		tf.nn.zero_fraction(outputs))95def get_weight_placeholders():96    return tf.get_collection('weight_placeholder')97def get_mask_placeholders():98    return tf.get_collection('mask_placeholder')99#################################################################################100#LAYERS101#################################################################################102def fully_connected_layer(x, units, name, gpu, config):103	'''104	Args:105		x: inputs from the previous layer, shape = [None, x.shape[0]].106		units: # of nodes of this layer, scalar.107		name: the name of this layer in graph.108		config: yaml file for the configuration.109	Returns:110		h: outputs, 1D tensor, shape = [None, units]111	'''112	with tf.variable_scope(name) as scope:113		# initialize layer parameters114		weights = _weights_initializer(name='weights',115			shape=[x.get_shape().as_list()[1], units],116			stddev=np.sqrt(2/units),117			gpu=gpu,118			config=config)119		biases = _biases_initializer(name='biases',120			shape=[units],121			val=0.1,122			gpu=gpu,123			config=config)124		# initilize and apply mask if specified to125		if config['use_mask']:126			mask = _mask_initializer(name='mask',127				shape=weights.get_shape().as_list(),128				gpu=gpu)129			# Apply mask to the weights130			masked_weights = tf.multiply(mask, weights, name='masked_weights')131			_parameter_summary(masked_weights)132			new_shape = masked_weights.get_shape().as_list()133			new_shape.insert(0,1)134			new_shape.append(1)135			_image_summary(tf.reshape(masked_weights, new_shape))136			# Calculate the output of the fully connected layer137			h = tf.add(tf.matmul(x, masked_weights), biases, name=name)138		else:139			h = tf.add(tf.matmul(x, weights), biases, name=name)140		# Adds summary to the weights and outputs141		_parameter_summary(weights)142		_parameter_summary(mask)143		_output_summary(h)144		if config['use_owl'] | config['use_growl'] | config['use_group_lasso']:145			# create interface for OWL146			w_placeholder = tf.placeholder(tf.float32, [x.get_shape().as_list()[1], units])147			assign_op_w = tf.assign(weights, w_placeholder, validate_shape=True)148			tf.add_to_collection('weight_placeholder', (weights, w_placeholder, assign_op_w))149			mask_placeholder = tf.placeholder(tf.float32, [x.get_shape().as_list()[1], units])150			assign_op_m = tf.assign(mask, mask_placeholder, validate_shape=True)151			tf.add_to_collection('mask_placeholder', (mask, mask_placeholder, assign_op_m))152	return h153def relu_layer(x, name):154	'''155	Args:156		x: inputs from the previous layer.157	Returns:158		h: activation.159	'''160	with tf.variable_scope(name) as scope:161		h = tf.nn.relu(x, name=name)162		_output_summary(h)163	return h164def batch_normalization_layer(x, axis, phase, name):165	'''166	Args:167		x: inputs.168		phase: boolean, true for training, false for testing.169	Returns:170		h: results.171	'''172	h = tf.layers.batch_normalization(x, axis=axis, training=phase, name=name)...

tiny_svr.py

Source:tiny_svr.py

...92            return False93    def _reload(self):94        self.warn("reloading ...")95        self._clear_timer()96        self._output_summary()97        self._handler.close()98        reload(svr_util)99        reload(svr_conf)100        self.on_reload()101        self._start()102        self._is_reload = False103    def _close(self):104        self.warn("close ...")105        self._clear_timer()106        self._output_summary()107        self._is_run = False108        self._handler.close()109    def _start(self):110        self._conf = svr_conf.CONF111        self._get_logger()112        self._handler = self.get_handler()113        self._handler.start()114        self._init_timer()115        self.on_start()116    def _signal(self, sig, _):117        self.warn('signal %s' % sig)118        if sig in (signal.SIGINT, signal.SIGTERM):119            self._is_close = True120        elif sig == signal.SIGUSR1:121            self._is_reload = True122    def _get_logger(self):123        self._logger = svr_util.get_logger(self.get_name(), self._conf)124        self.info = self._logger.info125        self.warn = self._logger.warn126        self.error = self._logger.error127    def _states_check(self):128        if not self._last_report_time:129            return130        report_time = self._conf['svr.timer.run_status_check_time_span']131        delta_time = time.time() - self._last_report_time132        if delta_time < report_time:133            return134        self.error('run_states_check: not receive report in %s s, start auto-exit!' % report_time)135        # close safely136        os.kill(os.getpid(), signal.SIGINT)137        # if can't close safely force_exit138        delay = self._conf['svr.close.force_close_delay']139        threading.Timer(interval=delay, function=svr_util.force_exit, args=[-3]).start()140    def _output_summary(self):141        self.warn('<SUMMARY> %s' % self._handler.get_summary())142    def _clear_timer(self):143        self._timer.clear()144        self._thread_timer.clear()145    def _init_timer(self):146        self._timer = TinyTimerObserver(self._logger)147        self._thread_timer = TinyThreadTimerObserver(self._logger, self._conf['svr.timer.min_span'])148        check_timer = PeriodTimer(self._states_check, self._conf['svr.timer.run_status_check_time_span'])149        summary_timer = FixedPeriodTimer(self._output_summary, self._conf['svr.timer.summary_output_time_point'])150        self._thread_timer.add('check_timer', check_timer)151        self._thread_timer.add('summary_timer', summary_timer)...

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