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tfmodel_save_load
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‎.DS_Store‎

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‎Untitled.ipynb‎

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"source": [
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"t.astype(str)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os, argparse"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"dir = os.path.dirname(os.path.realpath('./server'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'/Users/Alum/Documents/WorkSpace/python_learn'"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dir"
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]
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}
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],
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"metadata": {

‎tensorflow/.DS_Store‎

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‎tensorflow/Model/checkpoint‎

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model_checkpoint_path: "data-all"
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all_model_checkpoint_paths: "data-all"

‎tensorflow/Model/data-all.data-00000-of-00001‎

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‎tensorflow/Model/data-all.index‎

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‎tensorflow/Model/data-all.meta‎

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‎tensorflow/Model/frozen_model.pb‎

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‎tensorflow/Untitled.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/usr/local/Cellar/python/3.6.4_3/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
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" return f(*args, **kwds)\n"
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]
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}
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],
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"source": [
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"import tensorflow as tf\n",
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"\n",
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"def load_graph(frozen_graph_filename):\n",
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" # We load the protobuf file from the disk and parse it to retrieve the \n",
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" # unserialized graph_def\n",
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" with tf.gfile.GFile(frozen_graph_filename, \"rb\") as f:\n",
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" graph_def = tf.GraphDef()\n",
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" graph_def.ParseFromString(f.read())\n",
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"\n",
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" # Then, we import the graph_def into a new Graph and returns it \n",
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" with tf.Graph().as_default() as graph:\n",
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" # The name var will prefix every op/nodes in your graph\n",
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" # Since we load everything in a new graph, this is not needed\n",
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" tf.import_graph_def(graph_def, name=\"prefix\")\n",
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" return graph"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"graph = load_graph('./Model/frozen_model.pb')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"prefix/v1\n",
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"prefix/v2\n"
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]
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}
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],
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"source": [
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"for op in graph.get_operations():\n",
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" print(op.name)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"x = graph.get_tensor_by_name('prefix/v1:0')\n",
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"y = graph.get_tensor_by_name('prefix/v2:0')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"a = tf.add(x,y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"3.0\n"
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]
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}
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],
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"source": [
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"with tf.Session(graph=graph) as sess:\n",
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" print(sess.run(a))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎tensorflow/load-model.py‎

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import tensorflow as tf
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def load_graph(frozen_graph_filename):
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# We load the protobuf file from the disk and parse it to retrieve the
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# unserialized graph_def
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with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
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graph_def = tf.GraphDef()
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graph_def.ParseFromString(f.read())
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# Then, we import the graph_def into a new Graph and returns it
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with tf.Graph().as_default() as graph:
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# The name var will prefix every op/nodes in your graph
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# Since we load everything in a new graph, this is not needed
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tf.import_graph_def(graph_def, name="prefix")
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return graph
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if __name__=='__main__':
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graph = load_graph('./Model/frozen_model.pb')
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for op in graph.get_operations():
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print(op.name)
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x = graph.get_tensor_by_name('prefix/v1:0')
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y = graph.get_tensor_by_name('prefix/v2:0')
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a = tf.add(x,y)
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with tf.Session(graph=graph) as sess :
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print(sess.run(a))

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