notebooks/3_NeuralNetworks/recurrent_network.ipynb

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    "# Recurrent Neural Network Example\n",
    "\n",
    "Build a recurrent neural network (LSTM) with TensorFlow.\n",
    "\n",
    "- Author: Aymeric Damien\n",
    "- Project: https://github.com/aymericdamien/TensorFlow-Examples/"
   ]
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    "## RNN Overview\n",
    "\n",
    "<img src=\"http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/RNN-unrolled.png\" alt=\"nn\" style=\"width: 600px;\"/>\n",
    "\n",
    "References:\n",
    "- [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997.\n",
    "\n",
    "## MNIST Dataset Overview\n",
    "\n",
    "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n",
    "\n",
    "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n",
    "\n",
    "To classify images using a recurrent neural network, we consider every image row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 timesteps for every sample.\n",
    "\n",
    "More info: http://yann.lecun.com/exdb/mnist/"
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     "text": [
      "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
      "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
      "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
      "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.contrib import rnn\n",
    "\n",
    "# Import MNIST data\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "# Training Parameters\n",
    "learning_rate = 0.001\n",
    "training_steps = 10000\n",
    "batch_size = 128\n",
    "display_step = 200\n",
    "\n",
    "# Network Parameters\n",
    "num_input = 28 # MNIST data input (img shape: 28*28)\n",
    "timesteps = 28 # timesteps\n",
    "num_hidden = 128 # hidden layer num of features\n",
    "num_classes = 10 # MNIST total classes (0-9 digits)\n",
    "\n",
    "# tf Graph input\n",
    "X = tf.placeholder(\"float\", [None, timesteps, num_input])\n",
    "Y = tf.placeholder(\"float\", [None, num_classes])"
   ]
  },
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   "source": [
    "# Define weights\n",
    "weights = {\n",
    "    'out': tf.Variable(tf.random_normal([num_hidden, num_classes]))\n",
    "}\n",
    "biases = {\n",
    "    'out': tf.Variable(tf.random_normal([num_classes]))\n",
    "}"
   ]
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   "cell_type": "code",
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   "source": [
    "def RNN(x, weights, biases):\n",
    "\n",
    "    # Prepare data shape to match `rnn` function requirements\n",
    "    # Current data input shape: (batch_size, timesteps, n_input)\n",
    "    # Required shape: 'timesteps' tensors list of shape (batch_size, n_input)\n",
    "\n",
    "    # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input)\n",
    "    x = tf.unstack(x, timesteps, 1)\n",
    "\n",
    "    # Define a lstm cell with tensorflow\n",
    "    lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)\n",
    "\n",
    "    # Get lstm cell output\n",
    "    outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)\n",
    "\n",
    "    # Linear activation, using rnn inner loop last output\n",
    "    return tf.matmul(outputs[-1], weights['out']) + biases['out']"
   ]
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   "source": [
    "logits = RNN(X, weights, biases)\n",
    "prediction = tf.nn.softmax(logits)\n",
    "\n",
    "# Define loss and optimizer\n",
    "loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n",
    "    logits=logits, labels=Y))\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
    "train_op = optimizer.minimize(loss_op)\n",
    "\n",
    "# Evaluate model (with test logits, for dropout to be disabled)\n",
    "correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n",
    "\n",
    "# Initialize the variables (i.e. assign their default value)\n",
    "init = tf.global_variables_initializer()"
   ]
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     "text": [
      "Step 1, Minibatch Loss= 2.6268, Training Accuracy= 0.102\n",
      "Step 200, Minibatch Loss= 2.0722, Training Accuracy= 0.328\n",
      "Step 400, Minibatch Loss= 1.9181, Training Accuracy= 0.336\n",
      "Step 600, Minibatch Loss= 1.8858, Training Accuracy= 0.336\n",
      "Step 800, Minibatch Loss= 1.7022, Training Accuracy= 0.422\n",
      "Step 1000, Minibatch Loss= 1.6365, Training Accuracy= 0.477\n",
      "Step 1200, Minibatch Loss= 1.6691, Training Accuracy= 0.516\n",
      "Step 1400, Minibatch Loss= 1.4626, Training Accuracy= 0.547\n",
      "Step 1600, Minibatch Loss= 1.4707, Training Accuracy= 0.539\n",
      "Step 1800, Minibatch Loss= 1.4087, Training Accuracy= 0.570\n",
      "Step 2000, Minibatch Loss= 1.3033, Training Accuracy= 0.570\n",
      "Step 2200, Minibatch Loss= 1.3773, Training Accuracy= 0.508\n",
      "Step 2400, Minibatch Loss= 1.3092, Training Accuracy= 0.570\n",
      "Step 2600, Minibatch Loss= 1.2272, Training Accuracy= 0.609\n",
      "Step 2800, Minibatch Loss= 1.1827, Training Accuracy= 0.633\n",
      "Step 3000, Minibatch Loss= 1.0453, Training Accuracy= 0.641\n",
      "Step 3200, Minibatch Loss= 1.0400, Training Accuracy= 0.648\n",
      "Step 3400, Minibatch Loss= 1.1145, Training Accuracy= 0.656\n",
      "Step 3600, Minibatch Loss= 0.9884, Training Accuracy= 0.688\n",
      "Step 3800, Minibatch Loss= 1.0395, Training Accuracy= 0.703\n",
      "Step 4000, Minibatch Loss= 1.0096, Training Accuracy= 0.664\n",
      "Step 4200, Minibatch Loss= 0.8806, Training Accuracy= 0.758\n",
      "Step 4400, Minibatch Loss= 0.9090, Training Accuracy= 0.766\n",
      "Step 4600, Minibatch Loss= 1.0060, Training Accuracy= 0.703\n",
      "Step 4800, Minibatch Loss= 0.8954, Training Accuracy= 0.703\n",
      "Step 5000, Minibatch Loss= 0.8163, Training Accuracy= 0.750\n",
      "Step 5200, Minibatch Loss= 0.7620, Training Accuracy= 0.773\n",
      "Step 5400, Minibatch Loss= 0.7388, Training Accuracy= 0.758\n",
      "Step 5600, Minibatch Loss= 0.7604, Training Accuracy= 0.695\n",
      "Step 5800, Minibatch Loss= 0.7459, Training Accuracy= 0.734\n",
      "Step 6000, Minibatch Loss= 0.7448, Training Accuracy= 0.734\n",
      "Step 6200, Minibatch Loss= 0.7208, Training Accuracy= 0.773\n",
      "Step 6400, Minibatch Loss= 0.6557, Training Accuracy= 0.773\n",
      "Step 6600, Minibatch Loss= 0.8616, Training Accuracy= 0.758\n",
      "Step 6800, Minibatch Loss= 0.6089, Training Accuracy= 0.773\n",
      "Step 7000, Minibatch Loss= 0.5020, Training Accuracy= 0.844\n",
      "Step 7200, Minibatch Loss= 0.5980, Training Accuracy= 0.812\n",
      "Step 7400, Minibatch Loss= 0.6786, Training Accuracy= 0.766\n",
      "Step 7600, Minibatch Loss= 0.4891, Training Accuracy= 0.859\n",
      "Step 7800, Minibatch Loss= 0.7042, Training Accuracy= 0.797\n",
      "Step 8000, Minibatch Loss= 0.4200, Training Accuracy= 0.859\n",
      "Step 8200, Minibatch Loss= 0.6442, Training Accuracy= 0.742\n",
      "Step 8400, Minibatch Loss= 0.5569, Training Accuracy= 0.828\n",
      "Step 8600, Minibatch Loss= 0.5838, Training Accuracy= 0.836\n",
      "Step 8800, Minibatch Loss= 0.5579, Training Accuracy= 0.812\n",
      "Step 9000, Minibatch Loss= 0.4337, Training Accuracy= 0.867\n",
      "Step 9200, Minibatch Loss= 0.4366, Training Accuracy= 0.844\n",
      "Step 9400, Minibatch Loss= 0.5051, Training Accuracy= 0.844\n",
      "Step 9600, Minibatch Loss= 0.5244, Training Accuracy= 0.805\n",
      "Step 9800, Minibatch Loss= 0.4932, Training Accuracy= 0.805\n",
      "Step 10000, Minibatch Loss= 0.4833, Training Accuracy= 0.852\n",
      "Optimization Finished!\n",
      "Testing Accuracy: 0.882812\n"
     ]
    }
   ],
   "source": [
    "# Start training\n",
    "with tf.Session() as sess:\n",
    "\n",
    "    # Run the initializer\n",
    "    sess.run(init)\n",
    "\n",
    "    for step in range(1, training_steps+1):\n",
    "        batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
    "        # Reshape data to get 28 seq of 28 elements\n",
    "        batch_x = batch_x.reshape((batch_size, timesteps, num_input))\n",
    "        # Run optimization op (backprop)\n",
    "        sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})\n",
    "        if step % display_step == 0 or step == 1:\n",
    "            # Calculate batch loss and accuracy\n",
    "            loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n",
    "                                                                 Y: batch_y})\n",
    "            print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n",
    "                  \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n",
    "                  \"{:.3f}\".format(acc))\n",
    "\n",
    "    print(\"Optimization Finished!\")\n",
    "\n",
    "    # Calculate accuracy for 128 mnist test images\n",
    "    test_len = 128\n",
    "    test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input))\n",
    "    test_label = mnist.test.labels[:test_len]\n",
    "    print(\"Testing Accuracy:\", \\\n",
    "        sess.run(accuracy, feed_dict={X: test_data, Y: test_label}))"
   ]
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