h5_converter.py
def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
    """
    Freezes the state of a session into a prunned computation graph.

    Creates a new computation graph where variable nodes are replaced by
    constants taking their current value in the session. The new graph will be
    prunned so subgraphs that are not neccesary to compute the requested
    outputs are removed.
    @param session The TensorFlow session to be frozen.
    @param keep_var_names A list of variable names that should not be frozen,
                          or None to freeze all the variables in the graph.
    @param output_names Names of the relevant graph outputs.
    @param clear_devices Remove the device directives from the graph for better portability.
    @return The frozen graph definition.
    """
    from tensorflow.python.framework.graph_util import convert_variables_to_constants
    graph = session.graph
    with graph.as_default():
        freeze_var_names = None #list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
        output_names = output_names or []
        # output_names += [v.op.name for v in tf.global_variables()]
        input_graph_def = graph.as_graph_def()
        if clear_devices:
            for node in input_graph_def.node:
                node.device = ""
        frozen_graph = convert_variables_to_constants(session, input_graph_def,
                                                      output_names, freeze_var_names)
        return frozen_graph

    Freezes the state of a session into a prunned computation graph.

    Creates a new computation graph where variable nodes are replaced by
    constants taking their current value in the session. The new graph will be
    prunned so subgraphs that are not neccesary to compute the requested
    outputs are removed.
    @param session The TensorFlow session to be frozen.
    @param keep_var_names A list of variable names that should not be frozen,
                          or None to freeze all the variables in the graph.
    @param output_names Names of the relevant graph outputs.
    @param clear_devices Remove the device directives from the graph for better portability.
    @return The frozen graph definition.
    
Freezes the state of a session into a prunned computation graph.
Creates a new computation graph where variable nodes are replaced by
constants taking their current value in the session. The new graph will be
prunned so subgraphs that are not neccesary to compute the requested
outputs are removed.
@param session The TensorFlow session to be frozen.
@param keep_var_names A list of variable names that should not be frozen,
or None to freeze all the variables in the graph.
@param output_names Names of the relevant graph outputs.
@param clear_devices Remove the device directives from the graph for better portability.
@return The frozen graph definition.
Freezes the state of a session into a prunned computation graph . Creates a new computation graph where variable nodes are replaced by constants taking their current value in the session . The new graph will be prunned so subgraphs that are not neccesary to compute the requested outputs are removed . @param session The TensorFlow session to be frozen . @param keep_var_names A list of variable names that should not be frozen or None to freeze all the variables in the graph . @param output_names Names of the relevant graph outputs . @param clear_devices Remove the device directives from the graph for better portability . @return The frozen graph definition .
def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
    from tensorflow.python.framework.graph_util import convert_variables_to_constants
    graph = session.graph
    with graph.as_default():
        freeze_var_names = None 
        output_names = output_names or []
        input_graph_def = graph.as_graph_def()
        if clear_devices:
            for node in input_graph_def.node:
                node.device = ""
        frozen_graph = convert_variables_to_constants(session, input_graph_def,
                                                      output_names, freeze_var_names)
        return frozen_graph
def freeze_session ( session , keep_var_names = None , output_names = None , clear_devices = True ) : from tensorflow . python . framework . graph_util import convert_variables_to_constants graph = session . graph with graph . as_default ( ) : freeze_var_names = None output_names = output_names or [ ] input_graph_def = graph . as_graph_def ( ) if clear_devices : for node in input_graph_def . node : node . device = "" frozen_graph = convert_variables_to_constants ( session , input_graph_def , output_names , freeze_var_names ) return frozen_graph
Freezes the state of a session into a prunned computation graph.
Freezes the state of a session into a prunned computation graph .
"""
    Freezes the state of a session into a prunned computation graph.

    Creates a new computation graph where variable nodes are replaced by
    constants taking their current value in the session. The new graph will be
    prunned so subgraphs that are not neccesary to compute the requested
    outputs are removed.
    @param session The TensorFlow session to be frozen.
    @param keep_var_names A list of variable names that should not be frozen,
                          or None to freeze all the variables in the graph.
    @param output_names Names of the relevant graph outputs.
    @param clear_devices Remove the device directives from the graph for better portability.
    @return The frozen graph definition.
    """ #list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
 # output_names += [v.op.name for v in tf.global_variables()]

session
keep_var_names
output_names
clear_devices