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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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