FizzBuzz.ipynb

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   "source": [
    "# Fizz Buzz in Tensorflow!\n",
    "# see http://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "NUM_DIGITS = 10\n",
    "\n",
    "# Represent each input by an array of its binary digits.\n",
    "def binary_encode(i, num_digits):\n",
    "    return np.array([i >> d & 1 for d in range(num_digits)])\n",
    "\n",
    "# One-hot encode the desired outputs: [number, \"fizz\", \"buzz\", \"fizzbuzz\"]\n",
    "def fizz_buzz_encode(i):\n",
    "    if   i % 15 == 0: return np.array([0, 0, 0, 1])\n",
    "    elif i % 5  == 0: return np.array([0, 0, 1, 0])\n",
    "    elif i % 3  == 0: return np.array([0, 1, 0, 0])\n",
    "    else:             return np.array([1, 0, 0, 0])\n",
    "\n",
    "# Our goal is to produce fizzbuzz for the numbers 1 to 100. So it would be\n",
    "# unfair to include these in our training data. Accordingly, the training data\n",
    "# corresponds to the numbers 101 to (2 ** NUM_DIGITS - 1).\n",
    "trX = np.array([binary_encode(i, NUM_DIGITS) for i in range(101, 2 ** NUM_DIGITS)])\n",
    "trY = np.array([fizz_buzz_encode(i)          for i in range(101, 2 ** NUM_DIGITS)])\n"
   ]
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   "source": [
    "# We'll want to randomly initialize weights.\n",
    "def init_weights(shape):\n",
    "    return tf.Variable(tf.random_normal(shape, stddev=0.01))\n",
    "\n",
    "# Our model is a standard 1-hidden-layer multi-layer-perceptron with ReLU\n",
    "# activation. The softmax (which turns arbitrary real-valued outputs into\n",
    "# probabilities) gets applied in the cost function.\n",
    "def model(X, w_h, w_o):\n",
    "    h = tf.nn.relu(tf.matmul(X, w_h))\n",
    "    return tf.matmul(h, w_o)"
   ]
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   "execution_count": 3,
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   "source": [
    "# Our variables. The input has width NUM_DIGITS, and the output has width 4.\n",
    "X = tf.placeholder(\"float\", [None, NUM_DIGITS])\n",
    "Y = tf.placeholder(\"float\", [None, 4])\n",
    "\n",
    "# How many units in the hidden layer.\n",
    "NUM_HIDDEN = 100\n",
    "\n",
    "# Initialize the weights.\n",
    "w_h = init_weights([NUM_DIGITS, NUM_HIDDEN])\n",
    "w_o = init_weights([NUM_HIDDEN, 4])\n",
    "\n",
    "# Predict y given x using the model.\n",
    "py_x = model(X, w_h, w_o)\n",
    "\n",
    "# We'll train our model by minimizing a cost function.\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y))\n",
    "train_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost)\n",
    "\n",
    "# And we'll make predictions by choosing the largest output.\n",
    "predict_op = tf.argmax(py_x, 1)\n",
    "\n",
    "# Finally, we need a way to turn a prediction (and an original number)\n",
    "# into a fizz buzz output\n",
    "def fizz_buzz(i, prediction):\n",
    "    return [str(i), \"fizz\", \"buzz\", \"fizzbuzz\"][prediction]\n",
    "\n",
    "BATCH_SIZE = 128\n",
    "\n",
    "# Launch the graph in a session\n",
    "sess = tf.Session()\n",
    "init = tf.initialize_all_variables()\n",
    "sess.run(init)"
   ]
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      "9900 1.0\n",
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      "9905 1.0\n",
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      "9907 1.0\n",
      "9908 0.994582881907\n",
      "9909 1.0\n",
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      "9921 1.0\n",
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      "9940 1.0\n",
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      "9959 1.0\n",
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      "9970 1.0\n",
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      "9973 0.994582881907\n",
      "9974 1.0\n",
      "9975 1.0\n",
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      "9978 0.998916576381\n",
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      "9982 0.995666305525\n",
      "9983 0.998916576381\n",
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      "9985 0.998916576381\n",
      "9986 1.0\n",
      "9987 0.994582881907\n",
      "9988 1.0\n",
      "9989 0.998916576381\n",
      "9990 0.998916576381\n",
      "9991 1.0\n",
      "9992 0.998916576381\n",
      "9993 0.998916576381\n",
      "9994 0.998916576381\n",
      "9995 0.998916576381\n",
      "9996 0.998916576381\n",
      "9997 0.998916576381\n",
      "9998 0.994582881907\n",
      "9999 0.998916576381\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(10000):\n",
    "    # Shuffle the data before each training iteration.\n",
    "    p = np.random.permutation(range(len(trX)))\n",
    "    trX, trY = trX[p], trY[p]\n",
    "\n",
    "    # Train in batches of 128 inputs.\n",
    "    for start in range(0, len(trX), BATCH_SIZE):\n",
    "        end = start + BATCH_SIZE\n",
    "        sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})\n",
    "\n",
    "    # And print the current accuracy on the training data.\n",
    "    print(epoch, np.mean(np.argmax(trY, axis=1) == sess.run(predict_op, feed_dict={X: trX, Y: trY})))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1' '2' 'fizz' '4' 'buzz' 'fizz' '7' '8' 'fizz' 'buzz' '11' 'fizz' '13'\n",
      " '14' 'fizzbuzz' '16' '17' 'fizz' '19' 'buzz' 'fizz' '22' '23' 'fizz' '25'\n",
      " '26' 'fizz' '28' '29' 'fizzbuzz' '31' '32' 'fizz' '34' 'buzz' 'fizz' '37'\n",
      " '38' 'fizz' 'buzz' '41' 'fizz' '43' '44' 'fizzbuzz' '46' '47' 'fizz' '49'\n",
      " 'buzz' 'fizz' '52' '53' 'fizz' 'buzz' '56' 'fizz' '58' '59' 'fizzbuzz'\n",
      " '61' '62' 'fizz' '64' 'buzz' 'fizz' '67' '68' 'buzz' 'buzz' '71' 'fizz'\n",
      " '73' '74' 'fizzbuzz' '76' '77' 'fizz' '79' 'buzz' 'fizz' '82' '83' 'fizz'\n",
      " 'buzz' '86' 'fizz' '88' '89' 'fizzbuzz' '91' '92' 'fizz' '94' 'buzz'\n",
      " 'fizz' '97' '98' 'fizz' 'buzz']\n",
      "['1' '2' 'fizz' '4' 'buzz' 'fizz' '7' '8' 'fizz' 'buzz' '11' 'fizz' '13'\n",
      " '14' 'fizzbuzz' '16' '17' 'fizz' '19' 'buzz' 'fizz' '22' '23' 'fizz'\n",
      " 'buzz' '26' 'fizz' '28' '29' 'fizzbuzz' '31' '32' 'fizz' '34' 'buzz'\n",
      " 'fizz' '37' '38' 'fizz' 'buzz' '41' 'fizz' '43' '44' 'fizzbuzz' '46' '47'\n",
      " 'fizz' '49' 'buzz' 'fizz' '52' '53' 'fizz' 'buzz' '56' 'fizz' '58' '59'\n",
      " 'fizzbuzz' '61' '62' 'fizz' '64' 'buzz' 'fizz' '67' '68' 'fizz' 'buzz'\n",
      " '71' 'fizz' '73' '74' 'fizzbuzz' '76' '77' 'fizz' '79' 'buzz' 'fizz' '82'\n",
      " '83' 'fizz' 'buzz' '86' 'fizz' '88' '89' 'fizzbuzz' '91' '92' 'fizz' '94'\n",
      " 'buzz' 'fizz' '97' '98' 'fizz' 'buzz']\n",
      "Number correct, 98\n"
     ]
    }
   ],
   "source": [
    "# And now for some fizz buzz\n",
    "numbers = np.arange(1, 101)\n",
    "teX = np.transpose(binary_encode(numbers, NUM_DIGITS))\n",
    "teY = sess.run(predict_op, feed_dict={X: teX})\n",
    "output = np.vectorize(fizz_buzz)(numbers, teY)\n",
    "truth = np.vectorize(fizz_buzz)(numbers,[np.argmax(fizz_buzz_encode(elem)) for elem in numbers])\n",
    "print(output)\n",
    "print(truth)\n",
    "print(\"Number correct,\",np.sum( output==truth ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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