Python tensorflow.python.ops.clip_ops.global_norm() Examples
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Example #1
Source File: learning.py From ctw-baseline with MIT License | 6 votes |
def add_gradients_summaries(grads_and_vars): """Add summaries to gradients. Args: grads_and_vars: A list of gradient to variable pairs (tuples). Returns: The list of created summaries. """ summaries = [] for grad, var in grads_and_vars: if grad is not None: if isinstance(grad, ops.IndexedSlices): grad_values = grad.values else: grad_values = grad summaries.append( summary.histogram(var.op.name + '/gradient', grad_values)) summaries.append( summary.scalar(var.op.name + '/gradient_norm', clip_ops.global_norm([grad_values]))) else: logging.info('Var %s has no gradient', var.op.name) return summaries
Example #2
Source File: learning.py From lambda-packs with MIT License | 6 votes |
def add_gradients_summaries(grads_and_vars): """Add summaries to gradients. Args: grads_and_vars: A list of gradient to variable pairs (tuples). Returns: The list of created summaries. """ summaries = [] for grad, var in grads_and_vars: if grad is not None: if isinstance(grad, ops.IndexedSlices): grad_values = grad.values else: grad_values = grad summaries.append( summary.histogram(var.op.name + '/gradient', grad_values)) summaries.append( summary.scalar(var.op.name + '/gradient_norm', clip_ops.global_norm([grad_values]))) else: logging.info('Var %s has no gradient', var.op.name) return summaries
Example #3
Source File: training.py From lambda-packs with MIT License | 6 votes |
def add_gradients_summaries(grads_and_vars): """Add summaries to gradients. Args: grads_and_vars: A list of gradient to variable pairs (tuples). Returns: The list of created summaries. """ summaries = [] for grad, var in grads_and_vars: if grad is not None: if isinstance(grad, ops.IndexedSlices): grad_values = grad.values else: grad_values = grad summaries.append( summary.histogram(var.op.name + '_gradient', grad_values)) summaries.append( summary.scalar(var.op.name + '_gradient_norm', clip_ops.global_norm([grad_values]))) else: logging.info('Var %s has no gradient', var.op.name) return summaries
Example #4
Source File: learning.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def add_gradients_summaries(grads_and_vars): """Add summaries to gradients. Args: grads_and_vars: A list of gradient to variable pairs (tuples). Returns: The list of created summaries. """ summaries = [] for grad, var in grads_and_vars: if grad is not None: if isinstance(grad, ops.IndexedSlices): grad_values = grad.values else: grad_values = grad summaries.append( summary.histogram(var.op.name + '/gradient', grad_values)) summaries.append( summary.histogram(var.op.name + '/gradient_norm', clip_ops.global_norm([grad_values]))) else: logging.info('Var %s has no gradient', var.op.name) return summaries
Example #5
Source File: training.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def add_gradients_summaries(grads_and_vars): """Add summaries to gradients. Args: grads_and_vars: A list of gradient to variable pairs (tuples). Returns: The list of created summaries. """ summaries = [] for grad, var in grads_and_vars: if grad is not None: if isinstance(grad, ops.IndexedSlices): grad_values = grad.values else: grad_values = grad summaries.append( summary.histogram(var.op.name + '_gradient', grad_values)) summaries.append( summary.histogram(var.op.name + '_gradient_norm', clip_ops.global_norm([grad_values]))) else: logging.info('Var %s has no gradient', var.op.name) return summaries
Example #6
Source File: learning.py From CVTron with Apache License 2.0 | 6 votes |
def add_gradients_summaries(grads_and_vars): """Add summaries to gradients. Args: grads_and_vars: A list of gradient to variable pairs (tuples). Returns: The list of created summaries. """ summaries = [] for grad, var in grads_and_vars: if grad is not None: if isinstance(grad, ops.IndexedSlices): grad_values = grad.values else: grad_values = grad summaries.append( summary.histogram(var.op.name + '/gradient', grad_values)) summaries.append( summary.scalar(var.op.name + '/gradient_norm', clip_ops.global_norm([grad_values]))) else: logging.info('Var %s has no gradient', var.op.name) return summaries
Example #7
Source File: learning.py From CVTron with Apache License 2.0 | 6 votes |
def add_gradients_summaries(grads_and_vars): """Add summaries to gradients. Args: grads_and_vars: A list of gradient to variable pairs (tuples). Returns: The list of created summaries. """ summaries = [] for grad, var in grads_and_vars: if grad is not None: if isinstance(grad, ops.IndexedSlices): grad_values = grad.values else: grad_values = grad summaries.append( summary.histogram(var.op.name + '/gradient', grad_values)) summaries.append( summary.scalar(var.op.name + '/gradient_norm', clip_ops.global_norm([grad_values]))) else: logging.info('Var %s has no gradient', var.op.name) return summaries
Example #8
Source File: learning.py From CVTron with Apache License 2.0 | 6 votes |
def add_gradients_summaries(grads_and_vars): """Add summaries to gradients. Args: grads_and_vars: A list of gradient to variable pairs (tuples). Returns: The list of created summaries. """ summaries = [] for grad, var in grads_and_vars: if grad is not None: if isinstance(grad, ops.IndexedSlices): grad_values = grad.values else: grad_values = grad summaries.append( summary.histogram(var.op.name + '/gradient', grad_values)) summaries.append( summary.scalar(var.op.name + '/gradient_norm', clip_ops.global_norm([grad_values]))) else: logging.info('Var %s has no gradient', var.op.name) return summaries
Example #9
Source File: learning.py From tf-slim with Apache License 2.0 | 6 votes |
def add_gradients_summaries(grads_and_vars): """Add summaries to gradients. Args: grads_and_vars: A list of gradient to variable pairs (tuples). Returns: The list of created summaries. """ summaries = [] for grad, var in grads_and_vars: if grad is not None: if isinstance(grad, ops.IndexedSlices): grad_values = grad.values else: grad_values = grad summaries.append( summary.histogram(var.op.name + '/gradient', grad_values)) summaries.append( summary.scalar(var.op.name + '/gradient_norm', clip_ops.global_norm([grad_values]))) else: logging.info('Var %s has no gradient', var.op.name) return summaries
Example #10
Source File: training.py From tf-slim with Apache License 2.0 | 6 votes |
def add_gradients_summaries(grads_and_vars): """Add summaries to gradients. Args: grads_and_vars: A list of gradient to variable pairs (tuples). Returns: The list of created summaries. """ summaries = [] for grad, var in grads_and_vars: if grad is not None: if isinstance(grad, ops.IndexedSlices): grad_values = grad.values else: grad_values = grad summaries.append( summary.histogram(var.op.name + '_gradient', grad_values)) summaries.append( summary.scalar(var.op.name + '_gradient_norm', clip_ops.global_norm([grad_values]))) else: logging.info('Var %s has no gradient', var.op.name) return summaries
Example #11
Source File: learning.py From deep_image_model with Apache License 2.0 | 6 votes |
def add_gradients_summaries(grads_and_vars): """Add summaries to gradients. Args: grads_and_vars: A list of gradient to variable pairs (tuples). Returns: The list of created summaries. """ summaries = [] for grad, var in grads_and_vars: if grad is not None: if isinstance(grad, ops.IndexedSlices): grad_values = grad.values else: grad_values = grad summaries.append( summary.histogram(var.op.name + ':gradient', grad_values)) summaries.append( summary.histogram(var.op.name + ':gradient_norm', clip_ops.global_norm([grad_values]))) else: logging.info('Var %s has no gradient', var.op.name) return summaries
Example #12
Source File: training.py From deep_image_model with Apache License 2.0 | 6 votes |
def add_gradients_summaries(grads_and_vars): """Add summaries to gradients. Args: grads_and_vars: A list of gradient to variable pairs (tuples). Returns: The list of created summaries. """ summaries = [] for grad, var in grads_and_vars: if grad is not None: if isinstance(grad, ops.IndexedSlices): grad_values = grad.values else: grad_values = grad summaries.append(summary.histogram_summary( var.op.name + ':gradient', grad_values)) summaries.append(summary.histogram_summary( var.op.name + ':gradient_norm', clip_ops.global_norm([grad_values]))) else: logging.info('Var %s has no gradient', var.op.name) return summaries
Example #13
Source File: learning.py From mtl-ssl with Apache License 2.0 | 6 votes |
def add_gradients_summaries(grads_and_vars): """Add summaries to gradients. Args: grads_and_vars: A list of gradient to variable pairs (tuples). Returns: The list of created summaries. """ summaries = [] for grad, var in grads_and_vars: if grad is not None: if isinstance(grad, ops.IndexedSlices): grad_values = grad.values else: grad_values = grad summaries.append( summary.histogram(var.op.name + '/gradient', grad_values)) summaries.append( summary.scalar(var.op.name + '/gradient_norm', clip_ops.global_norm([grad_values]))) else: log.warn('Var %s has no gradient', var.op.name) return summaries
Example #14
Source File: learning.py From keras-lambda with MIT License | 6 votes |
def add_gradients_summaries(grads_and_vars): """Add summaries to gradients. Args: grads_and_vars: A list of gradient to variable pairs (tuples). Returns: The list of created summaries. """ summaries = [] for grad, var in grads_and_vars: if grad is not None: if isinstance(grad, ops.IndexedSlices): grad_values = grad.values else: grad_values = grad summaries.append( summary.histogram(var.op.name + '/gradient', grad_values)) summaries.append( summary.histogram(var.op.name + '/gradient_norm', clip_ops.global_norm([grad_values]))) else: logging.info('Var %s has no gradient', var.op.name) return summaries
Example #15
Source File: training.py From keras-lambda with MIT License | 6 votes |
def add_gradients_summaries(grads_and_vars): """Add summaries to gradients. Args: grads_and_vars: A list of gradient to variable pairs (tuples). Returns: The list of created summaries. """ summaries = [] for grad, var in grads_and_vars: if grad is not None: if isinstance(grad, ops.IndexedSlices): grad_values = grad.values else: grad_values = grad summaries.append( summary.histogram(var.op.name + '_gradient', grad_values)) summaries.append( summary.histogram(var.op.name + '_gradient_norm', clip_ops.global_norm([grad_values]))) else: logging.info('Var %s has no gradient', var.op.name) return summaries
Example #16
Source File: graph_single.py From SketchySceneColorization with MIT License | 5 votes |
def optimize(gradients, optim, global_step, summaries, global_norm=None, global_norm_clipped=None, appendix=''): """Modified from sugartensor""" # Add Summary if summaries is None: summaries = ["loss", "learning_rate"] # if "gradient_norm" in summaries: # if global_norm is None: # tf.summary.scalar("global_norm/gradient_norm" + appendix, # clip_ops.global_norm(list(zip(*gradients))[0])) # else: # tf.summary.scalar("global_norm/gradient_norm" + appendix, # global_norm) # if global_norm_clipped is not None: # tf.summary.scalar("global_norm/gradient_norm_clipped" + appendix, # global_norm_clipped) # Add histograms for variables, gradients and gradient norms. for gradient, variable in gradients: if isinstance(gradient, ops.IndexedSlices): grad_values = gradient.values else: grad_values = gradient if grad_values is not None: var_name = variable.name.replace(":", "_") # if "gradients" in summaries: # tf.summary.histogram("gradients/%s" % var_name, grad_values) # if "gradient_norm" in summaries: # tf.summary.scalar("gradient_norm/%s" % var_name, # clip_ops.global_norm([grad_values])) # Gradient Update OP return optim.apply_gradients(gradients, global_step=global_step)