tf_conceptual_graph
Project description
tf_conceptual_graph
Create tensorflow(1.x) conceptual graph. Conceputual graph is not aimed to reconstruct a neural network. The main purpose of this conceputual graph is for treating a neural network as a heterogeneous graph. Once we can treat neural networks as heterogeneous graphs, we can apply graph neural network methods for them to predict inference results from trained neural networks. From the view point, we can optimize neural network structures.
Installtion
$ pip install tfcg
Usage
read a graph_def object from object api(sess.graph_def
)
import numpy as np
import tensorflow as tf
import tfcg
with tf.Graph().as_default() as graph:
model = tf.keras.Sequential()
x = np.random.rand(128, 28, 28, 3)
model.add(tf.keras.layers.Conv2D(16, 3, input_shape=[28, 28, 3], name='conv1'))
model.add(tf.keras.layers.Conv2D(32, 1, name='conv2'))
model.add(tf.keras.layers.Conv2D(64, 2, name='conv3'))
model.add(tf.keras.layers.Conv2D(128, 2, name='conv4'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(32, name='dense1'))
model.add(tf.keras.layers.ReLU())
model.add(tf.keras.layers.Dense(16, name='dense2'))
x_p = tf.placeholder(tf.float32, [None, 28, 28, 3], name='input')
out_p = model(x_p)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
o = sess.run(out_p, feed_dict={x_p: x})
_ = tf.identity(o, name="output")
tf.io.write_graph(sess.graph, './', 'train.pbtxt')
parser = tfcg.from_graph_def(sess.graph_def)
parser.dump_json("conceptual_graph.json")
parser.dump_img("output.png")
read a graph from a file, After dumpping a tensorflow graph file.
import tfcg
parser = tfcg.from_file("./train.pbtxt")
parser.dump_json("conceptual_graph.json")
mparser.dump_img("output.png")
Examples
Project details
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tfcg-0.1.4.tar.gz
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