migrate tensorflow 1.0 or higher

README.md

@@ -1,3 +1,6 @@
+* Example code has been modified to support Tensorflow 1.0 or higher API by BGPark
+* If you are using an IDE such as PyCharm, it is strongly recommended that you create a project for each chapter. If not, pay attention to the directory relative path of the dataset.
+
 # _TensorFlow for Machine Intelligence_
 
 ![TensorFlow for Machine Intelligence book cover](img/book_cover.jpg)

chapters/03_tensorflow_fundamentals/TensorFlow Fundamentals - Basic Graph.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {
     "collapsed": true
    },
@@ -14,7 +14,7 @@
     "# Build our graph nodes, starting from the inputs\n",
     "a = tf.constant(5, name=\"input_a\")\n",
     "b = tf.constant(3, name=\"input_b\")\n",
-    "c = tf.mul(a,b, name=\"mul_c\")\n",
+    "c = tf.multiply(a,b, name=\"mul_c\")\n",
     "d = tf.add(a,b, name=\"add_d\")\n",
     "e = tf.add(c,d, name=\"add_e\")\n",
     "\n",
@@ -25,7 +25,7 @@
     "output = sess.run(e)\n",
     "\n",
     "# Open a TensorFlow SummaryWriter to write our graph to disk\n",
-    "writer = tf.train.SummaryWriter('./my_graph', sess.graph)\n",
+    "writer = tf.summary.FileWriter('./my_graph', sess.graph)\n",
     "\n",
     "# Close our SummaryWriter and Session objects\n",
     "writer.close()\n",
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {
     "collapsed": true
    },
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {
     "collapsed": true
    },
@@ -58,14 +58,14 @@
     "# Build our graph nodes, starting from the inputs\n",
     "a = tf.constant(5, name=\"input_a\")\n",
     "b = tf.constant(3, name=\"input_b\")\n",
-    "c = tf.mul(a,b, name=\"mul_c\")\n",
+    "c = tf.multiply(a,b, name=\"mul_c\")\n",
     "d = tf.add(a,b, name=\"add_d\")\n",
     "e = tf.add(c,d, name=\"add_e\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {
     "collapsed": true
    },
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -89,19 +89,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
     "# Open a TensorFlow SummaryWriter to write our graph to disk\n",
-    "writer = tf.train.SummaryWriter('./my_graph', sess.graph)"
+    "writer = tf.summary.FileWriter('./my_graph', sess.graph)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {
     "collapsed": true
    },
@@ -130,7 +130,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+    ""
+   ]
   }
  ],
  "metadata": {
@@ -142,7 +144,7 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 2.0
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
@@ -154,4 +156,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

chapters/03_tensorflow_fundamentals/TensorFlow Fundamentals - Name Scope Examples.ipynb

@@ -12,11 +12,11 @@
     "\n",
     "with tf.name_scope(\"Scope_A\"):\n",
     "    a = tf.add(1, 2, name=\"A_add\")\n",
-    "    b = tf.mul(a, 3, name=\"A_mul\")\n",
+    "    b = tf.multiply(a, 3, name=\"A_mul\")\n",
     "\n",
     "with tf.name_scope(\"Scope_B\"):\n",
     "    c = tf.add(4, 5, name=\"B_add\")\n",
-    "    d = tf.mul(c, 6, name=\"B_mul\")\n",
+    "    d = tf.multiply(c, 6, name=\"B_mul\")\n",
     "\n",
     "e = tf.add(b, d, name=\"output\")"
    ]
@@ -29,7 +29,7 @@
    },
    "outputs": [],
    "source": [
-    "writer = tf.train.SummaryWriter('./name_scope_1', graph=tf.get_default_graph())"
+    "writer = tf.summary.FileWriter('./name_scope_1', graph=tf.get_default_graph())"
    ]
   },
   {
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -61,12 +61,12 @@
     "    with tf.name_scope(\"Transformation\"):\n",
     "\n",
     "        with tf.name_scope(\"A\"):\n",
-    "            A_mul = tf.mul(in_1, const)\n",
-    "            A_out = tf.sub(A_mul, in_1)\n",
+    "            A_mul = tf.multiply(in_1, const)\n",
+    "            A_out = tf.subtract(A_mul, in_1)\n",
     "\n",
     "        with tf.name_scope(\"B\"):\n",
-    "            B_mul = tf.mul(in_2, const)\n",
-    "            B_out = tf.sub(B_mul, in_2)\n",
+    "            B_mul = tf.multiply(in_2, const)\n",
+    "            B_out = tf.subtract(B_mul, in_2)\n",
     "\n",
     "        with tf.name_scope(\"C\"):\n",
     "            C_div = tf.div(A_out, B_out)\n",
@@ -78,7 +78,7 @@
     "\n",
     "    out = tf.maximum(C_out, D_out)   \n",
     "\n",
-    "writer = tf.train.SummaryWriter('./name_scope_2', graph=graph)\n",
+    "writer = tf.summary.FileWriter('./name_scope_2', graph=graph)\n",
     "writer.close()"
    ]
   },
@@ -108,7 +108,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+    ""
+   ]
   }
  ],
  "metadata": {
@@ -120,7 +122,7 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 3.0
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
@@ -132,4 +134,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

chapters/03_tensorflow_fundamentals/TensorFlow Fundamentals - Vectorized Graph with Summaries and Name Scopes.ipynb

@@ -14,9 +14,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -61,30 +61,30 @@
     "        avg = tf.div(update_total, tf.cast(increment_step, tf.float32), name=\"average\")\n",
     "        \n",
     "        # Creates summaries for output node\n",
-    "        tf.scalar_summary(b'Output', output, name=\"output_summary\")\n",
-    "        tf.scalar_summary(b'Sum of outputs over time', update_total, name=\"total_summary\")\n",
-    "        tf.scalar_summary(b'Average of outputs over time', avg, name=\"average_summary\")\n",
+    "        tf.summary.scalar('Output', output)\n",
+    "        tf.summary.scalar('Sum_of_outputs_over_time', update_total)\n",
+    "        tf.summary.scalar('Average_of_outputs_over_time', avg)\n",
     "    \n",
     "    # Global Variables and Operations\n",
     "    with tf.name_scope(\"global_ops\"):\n",
     "        # Initialization Op\n",
-    "        init = tf.initialize_all_variables()    \n",
+    "        init = tf.global_variables_initializer()\n",
     "        # Merge all summaries into one Operation\n",
-    "        merged_summaries = tf.merge_all_summaries()\n",
+    "        merged_summaries = tf.summary.merge_all()\n",
     "\n",
     "# Start a Session, using the explicitly created Graph\n",
     "sess = tf.Session(graph=graph)\n",
     "\n",
     "# Open a SummaryWriter to save summaries\n",
-    "writer = tf.train.SummaryWriter('./improved_graph', graph)\n",
+    "writer = tf.summary.FileWriter('./improved_graph', graph)\n",
     "\n",
     "# Initialize Variables\n",
     "sess.run(init)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
    "metadata": {
     "collapsed": true
    },
@@ -135,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {
     "collapsed": true
    },
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "metadata": {
     "collapsed": true
    },
@@ -177,7 +177,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+    ""
+   ]
   }
  ],
  "metadata": {
@@ -189,7 +191,7 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 3.0
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
@@ -201,4 +203,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

chapters/03_tensorflow_fundamentals/basic_graph.py

@@ -4,7 +4,7 @@
 # Build our graph nodes, starting from the inputs
 a = tf.constant(5, name="input_a")
 b = tf.constant(3, name="input_b")
-c = tf.mul(a,b, name="mul_c")
+c = tf.multiply(a,b, name="mul_c")
 d = tf.add(a,b, name="add_d")
 e = tf.add(c,d, name="add_e")
 
@@ -15,7 +15,7 @@
 sess.run(e)
 
 # Open a TensorFlow SummaryWriter to write our graph to disk
-writer = tf.train.SummaryWriter('./my_graph', sess.graph)
+writer = tf.summary.FileWriter('./my_graph', sess.graph)
 
 # Close our SummaryWriter and Session objects
 writer.close()

chapters/03_tensorflow_fundamentals/name_scopes.py

@@ -3,15 +3,15 @@
 # Example 1
 with tf.name_scope("Scope_A"):
     a = tf.add(1, 2, name="A_add")
-    b = tf.mul(a, 3, name="A_mul")
+    b = tf.multiply(a, 3, name="A_mul")
 
 with tf.name_scope("Scope_B"):
     c = tf.add(4, 5, name="B_add")
-    d = tf.mul(c, 6, name="B_mul")
+    d = tf.multiply(c, 6, name="B_mul")
 
 e = tf.add(b, d, name="output")
 
-writer = tf.train.SummaryWriter('./name_scope_1', graph=tf.get_default_graph())
+writer = tf.summary.FileWriter('./name_scope_1', graph=tf.get_default_graph())
 writer.close()
 
 
@@ -26,12 +26,12 @@
     with tf.name_scope("Transformation"):
 
         with tf.name_scope("A"):
-            A_mul = tf.mul(in_1, const)
-            A_out = tf.sub(A_mul, in_1)
+            A_mul = tf.multiply(in_1, const)
+            A_out = tf.subtract(A_mul, in_1)
 
         with tf.name_scope("B"):
-            B_mul = tf.mul(in_2, const)
-            B_out = tf.sub(B_mul, in_2)
+            B_mul = tf.multiply(in_2, const)
+            B_out = tf.subtract(B_mul, in_2)
 
         with tf.name_scope("C"):
             C_div = tf.div(A_out, B_out)
@@ -43,7 +43,7 @@
 
     out = tf.maximum(C_out, D_out)   
 
-writer = tf.train.SummaryWriter('./name_scope_2', graph=graph)
+writer = tf.summary.FileWriter('./name_scope_2', graph=graph)
 writer.close()
 
 # To start TensorBoard after running this file, execute the following command: