restore Basic TensorFlow Examples

0.2.Basic/Basic_usageOfConstant.py

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+import tensorflow as tf
+
+# Create a Constant op that produces a 1x2 matrix.  The op is
+# added as a node to the default graph.
+#
+# The value returned by the constructor represents the output
+# of the Constant op.
+matrix1 = tf.constant([[3., 3.]])
+
+# Create another Constant that produces a 2x1 matrix.
+matrix2 = tf.constant([[2.],[2.]])
+
+# Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.
+# The returned value, 'product', represents the result of the matrix
+# multiplication.
+product = tf.matmul(matrix1, matrix2)
+
+# Launch the default graph.
+sess = tf.Session()
+
+with tf.Session() as sess:
+  result = sess.run([product])
+  print(result)

0.2.Basic/Basic_usageOfPlaceholder.py

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+import tensorflow as tf
+
+
+input1 = tf.placeholder(tf.float32)
+input2 = tf.placeholder(tf.float32)
+output = tf.mul(input1, input2)
+
+with tf.Session() as sess:
+  print(sess.run([output], feed_dict={input1:[7.], input2:[2.]}))
+
+# output:
+# [array([ 14.], dtype=float32)]

0.2.Basic/Basic_usageOfVariables.py

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+import tensorflow as tf
+
+# Create a Variable, that will be initialized to the scalar value 0.
+state = tf.Variable(0, name="counter")
+
+# Create an Op to add one to `state`.
+
+one = tf.constant(1)
+new_value = tf.add(state, one)
+update = tf.assign(state, new_value)
+
+# Variables must be initialized by running an `init` Op after having
+# launched the graph.  We first have to add the `init` Op to the graph.
+init_op = tf.initialize_all_variables()
+
+# Launch the graph and run the ops.
+with tf.Session() as sess:
+  # Run the 'init' op
+  sess.run(init_op)
+  # Print the initial value of 'state'
+  print(sess.run(state))
+  # Run the op that updates 'state' and print 'state'.
+  for _ in range(3):
+    sess.run(update)
+    print(sess.run(state))
+
+# output:
+
+# 0
+# 1
+# 2
+# 3

0.2.Basic/TensorFlow Basic of concept.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TensorFlow 기본 개념 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From <ipython-input-36-265b04346152>:17 in <module>.: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\nInstructions for updating:\nUse `tf.global_variables_initializer` instead.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 0, output: 0.800000011920929\nepoch 10, output: 0.47898948192596436\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 20, output: 0.2867887020111084\nepoch 30, output: 0.17171096801757812\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 40, output: 0.10280969738960266\nepoch 50, output: 0.06155595928430557\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 60, output: 0.036855824291706085\nepoch 70, output: 0.022066941484808922\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 80, output: 0.01321229338645935\nepoch 90, output: 0.007910688407719135\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "x = tf.constant(1.0, name='input')\n",
+    "w = tf.Variable(0.8, name='weight')\n",
+    "y = tf.mul(w, x, name='output')\n",
+    "y_ = tf.constant(0.0, name='correct_value')\n",
+    "loss = tf.pow(y - y_, 2, name='loss')\n",
+    "train_step = tf.train.GradientDescentOptimizer(0.025).minimize(loss)\n",
+    "\n",
+    "for value in [x, w, y, y_, loss]:\n",
+    "    tf.scalar_summary(value.op.name, value)\n",
+    "\n",
+    "summaries = tf.merge_all_summaries()\n",
+    "\n",
+    "sess = tf.Session()\n",
+    "summary_writer = tf.train.SummaryWriter('log_simple_stats', sess.graph)\n",
+    "\n",
+    "sess.run(tf.initialize_all_variables())\n",
+    "for i in range(100):\n",
+    "    if i % 10 ==0:\n",
+    "        print(\"epoch {}, output: {}\".format(i, sess.run(y)))\n",
+    "    summary_writer.add_summary(sess.run(summaries), i)\n",
+    "    sess.run(train_step)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    ""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2.0
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

0.2.Basic/basic.py

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+'''
+Created on Nov 17, 2015
+
+@author: root
+'''
+
+import tensorflow as tf
+
+a = tf.placeholder(tf.int16)
+b = tf.placeholder(tf.int16)
+
+# Define some operations
+add = tf.add(a, b)
+mul = tf.mul(a, b)
+
+with tf.Session() as sess:
+    print ("Addition with variables: %i" % sess.run(add, feed_dict={a:2, b:3}))
+    print ("Multiplication with variables: %d" % sess.run(mul, feed_dict={a:2, b:3}))
+
+
+

0.2.Basic/hellowTensorFlow.py

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+'''
+Created on Nov 17, 2015
+
+@author: root
+'''
+import tensorflow as tf
+hello = tf.constant('Hello, TensorFlow!')
+sess = tf.Session()
+print (sess.run(hello))
+
+a = tf.constant(10)
+b = tf.constant(32)
+print (sess.run(a+b))