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TensorFlow-Tutorials/00_multiply.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "a = tf.placeholder(\"float\") # Create a symbolic variable 'a'\n",
+    "b = tf.placeholder(\"float\") # Create a symbolic variable 'b'\n",
+    "\n",
+    "y = tf.multiply(a, b) # multiply the symbolic variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.000000 should equal 2.0\n",
+      "9.000000 should equal 9.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "with tf.Session() as sess: # create a session to evaluate the symbolic expressions\n",
+    "    print(\"%f should equal 2.0\" % sess.run(y, feed_dict={a: 1, b: 2})) # eval expressions with parameters for a and b\n",
+    "    print(\"%f should equal 9.0\" % sess.run(y, feed_dict={a: 3, b: 3}))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

TensorFlow-Tutorials/01_linear_regression.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "trX = np.linspace(-1, 1, 101)\n",
+    "trY = 2 * trX + np.random.randn(*trX.shape) * 0.33 # create a y value which is approximately linear but with some random noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X = tf.placeholder(\"float\") # create symbolic variables\n",
+    "Y = tf.placeholder(\"float\")\n",
+    "\n",
+    "def model(X, w):\n",
+    "    return tf.multiply(X, w) # lr is just X*w so this model line is pretty simple\n",
+    "\n",
+    "w = tf.Variable(0.0, name=\"weights\") # create a shared variable (like theano.shared) for the weight matrix\n",
+    "y_model = model(X, w)\n",
+    "\n",
+    "cost = tf.square(Y - y_model) # use square error for cost function\n",
+    "\n",
+    "train_op = tf.train.GradientDescentOptimizer(0.01).minimize(cost) # construct an optimizer to minimize cost and fit line to my data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.00863\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Launch the graph in a session\n",
+    "with tf.Session() as sess:\n",
+    "    # you need to initialize variables (in this case just variable W)\n",
+    "    tf.global_variables_initializer().run()\n",
+    "\n",
+    "    for i in range(100):\n",
+    "        for (x, y) in zip(trX, trY):\n",
+    "            sess.run(train_op, feed_dict={X: x, Y: y})\n",
+    "\n",
+    "    print(sess.run(w))  # It should be something around 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

TensorFlow-Tutorials/02_logistic_regression.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "from tensorflow.examples.tutorials.mnist import input_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def init_weights(shape):\n",
+    "    return tf.Variable(tf.random_normal(shape, stddev=0.01))\n",
+    "\n",
+    "def model(X, w):\n",
+    "    return tf.matmul(X, w) # notice we use the same model as linear regression, this is because there is a baked in cost function which performs softmax and cross entropy\n",
+    "\n",
+    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n",
+    "trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "X = tf.placeholder(\"float\", [None, 784]) # create symbolic variables\n",
+    "Y = tf.placeholder(\"float\", [None, 10])\n",
+    "\n",
+    "w = init_weights([784, 10]) # like in linear regression, we need a shared variable weight matrix for logistic regression\n",
+    "\n",
+    "py_x = model(X, w)\n",
+    "\n",
+    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y)) # compute mean cross entropy (softmax is applied internally)\n",
+    "train_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost) # construct optimizer\n",
+    "predict_op = tf.argmax(py_x, 1) # at predict time, evaluate the argmax of the logistic regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Launch the graph in a session\n",
+    "with tf.Session() as sess:\n",
+    "    # you need to initialize all variables\n",
+    "    tf.global_variables_initializer().run()\n",
+    "\n",
+    "    for i in range(100):\n",
+    "        for start, end in zip(range(0, len(trX), 128), range(128, len(trX)+1, 128)):\n",
+    "            sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})\n",
+    "        print(i, np.mean(np.argmax(teY, axis=1) ==\n",
+    "                         sess.run(predict_op, feed_dict={X: teX})))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

TensorFlow-Tutorials/03_net.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "from tensorflow.examples.tutorials.mnist import input_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def init_weights(shape):\n",
+    "    return tf.Variable(tf.random_normal(shape, stddev=0.01))\n",
+    "\n",
+    "def model(X, w_h, w_o):\n",
+    "    h = tf.nn.sigmoid(tf.matmul(X, w_h)) # this is a basic mlp, think 2 stacked logistic regressions\n",
+    "    return tf.matmul(h, w_o) # note that we dont take the softmax at the end because our cost fn does that for us\n",
+    "\n",
+    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n",
+    "trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "X = tf.placeholder(\"float\", [None, 784])\n",
+    "Y = tf.placeholder(\"float\", [None, 10])\n",
+    "\n",
+    "w_h = init_weights([784, 625]) # create symbolic variables\n",
+    "w_o = init_weights([625, 10])\n",
+    "\n",
+    "py_x = model(X, w_h, w_o)\n",
+    "\n",
+    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y)) # compute costs\n",
+    "train_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost) # construct an optimizer\n",
+    "predict_op = tf.argmax(py_x, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Launch the graph in a session\n",
+    "with tf.Session() as sess:\n",
+    "    # you need to initialize all variables\n",
+    "    tf.global_variables_initializer().run()\n",
+    "\n",
+    "    for i in range(100):\n",
+    "        for start, end in zip(range(0, len(trX), 128), range(128, len(trX)+1, 128)):\n",
+    "            sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})\n",
+    "        print(i, np.mean(np.argmax(teY, axis=1) ==\n",
+    "                         sess.run(predict_op, feed_dict={X: teX})))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}