02_Graph_and_Session.ipynb_uploaded_180414_11_53.html
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import tensorflow as tf
import numpy as np
# You can create new tensorflow session
# tf.Session.init(target='', graph=None, config=None)
# Session object may own resources such as variables, queues, and readers
# So, it's important to release these resources,
# when resources are no longer required
# To do this, you can either invoke close() on session object,
# or you can use session as context manager
# Session and Graph
# from : https://www.tensorflow.org/versions/r0.11/api_docs/python/client.html
# If you are using multiple graphs,
# (which can be created with tf.Graph() in same process),
# you will have to use different sessions for each graph,
# but "each graph" can be used in "multiple sessions"
# graph1 should be in session1
# graph2 should be in session2
# graph3 should be in session3
# graph1 can be in session1
# graph1 can be in session2
# graph1 can be in session3
# In this case, it is often clearer to pass the graph,
# to be launched explicitly to session constructor
# You can create graph
graph1_node = tf.Graph()
with graph1_node.as_default():
constant1_node = tf.constant(10.0, name="a")
constant2_node = tf.constant(20.0, name="b")
sum_operation_for_c1_and_c2_node = constant1_node + constant2_node
# check constant1_node's graph
# print(sum_operation_for_c1_and_c2_node.graph)
# <tensorflow.python.framework.ops.Graph object at 0x7f2dcb337710>
graph2_node = tf.Graph()
with graph2_node.as_default():
constant1_for_graph2_node = tf.constant(40.0, name="x")
constant2_for_graph2_node = tf.constant(50.0, name="y")
substraction_operation_for_c1g2_and_c2g2_node\
=constant1_for_graph2_node-constant2_for_graph2_node
# check graph2_node's graph
# print("substraction_operation_for_c1g2_and_c2g2_node.graph",substraction_operation_for_c1g2_and_c2g2_node.graph)
# <tensorflow.python.framework.ops.Graph object at 0x7ff619467710>
# You create session object for graph1_node
with tf.Session( graph=graph1_node ) as sess_object:
print(sess_object.run(sum_operation_for_c1_and_c2_node))
# 30.0
# You create session object for graph2_node
with tf.Session( graph=graph2_node ) as sess_object:
print(sess_object.run(substraction_operation_for_c1g2_and_c2g2_node))
# -10.0
# Following codes generate errors
# You will create session for graph1_node,
# but you will try to use node which is included in graph2_node
# with tf.Session( graph=graph1_node ) as sess_object:
# # following code should make errors
# print(sess_object.run(substraction_operation_for_c1g2_and_c2g2_node))
# print(graph1_node.as_graph_def())
# node {
# name: "a"
# op: "Const"
# attr {
# key: "dtype"
# value {
# type: DT_FLOAT
# }
# }
# attr {
# key: "value"
# value {
# tensor {
# dtype: DT_FLOAT
# tensor_shape {
# }
# float_val: 10.0
# }
# }
# }
# }
# node {
# name: "b"
# op: "Const"
# attr {
# key: "dtype"
# value {
# type: DT_FLOAT
# }
# }
# attr {
# key: "value"
# value {
# tensor {
# dtype: DT_FLOAT
# tensor_shape {
# }
# float_val: 20.0
# }
# }
# }
# }
# node {
# name: "add"
# op: "Add"
# input: "a"
# input: "b"
# attr {
# key: "T"
# value {
# type: DT_FLOAT
# }
# }
# }
# versions {
# producer: 26
# }
# You can perfrm graph visualization
from graph_visualizer import show_graph
# print(show_graph(graph1_node.as_graph_def()))
# print(show_graph(graph2_node.as_graph_def()))
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