{
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"metadata": {
"colab": {
"name": "Untitled0.ipynb",
"version": "0.3.2",
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"name": "python3",
"display_name": "Python 3"
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"accelerator": "GPU"
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"cells": [
{
"metadata": {
"id": "UBpbr4JZKYTz",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# MNIST Digit Classification Using Recurrent Neural Networks"
]
},
{
"metadata": {
"id": "CxeZAiQkLMNR",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 704
},
"outputId": "77ad5785-4e41-40d0-896e-ed9e6873b4ae"
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"cell_type": "code",
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import argparse\n",
"\n",
"######################\n",
"# Optimization Flags #\n",
"######################\n",
"\n",
"learning_rate = 0.001 # initial learning rate\n",
"seed = 111\n",
"\n",
"##################\n",
"# Training Flags #\n",
"##################\n",
"batch_size = 128 # Batch size for training\n",
"num_epoch = 10 # Number of training iterations\n",
"\n",
"###############\n",
"# Model Flags #\n",
"###############\n",
"hidden_size = 128 # Number of neurons for RNN hodden layer\n",
"\n",
"# Reset the graph set the random numbers to be the same using \"seed\"\n",
"tf.reset_default_graph()\n",
"tf.set_random_seed(seed)\n",
"np.random.seed(seed)\n",
"\n",
"# Divide 28x28 images to rows of data to feed to RNN as sequantial information\n",
"step_size = 28\n",
"input_size = 28\n",
"output_size = 10\n",
"\n",
"# Input tensors\n",
"X = tf.placeholder(tf.float32, [None, step_size, input_size])\n",
"y = tf.placeholder(tf.int32, [None])\n",
"\n",
"# Rnn\n",
"cell = tf.nn.rnn_cell.BasicRNNCell(num_units=hidden_size)\n",
"output, state = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)\n",
"\n",
"# Forward pass and loss calcualtion\n",
"logits = tf.layers.dense(state, output_size)\n",
"cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\n",
"loss = tf.reduce_mean(cross_entropy)\n",
"\n",
"# optimizer\n",
"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)\n",
"\n",
"# Prediction\n",
"prediction = tf.nn.in_top_k(logits, y, 1)\n",
"accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32))\n",
"\n",
"# input data\n",
"from tensorflow.examples.tutorials.mnist import input_data\n",
"mnist = input_data.read_data_sets(\"MNIST_data/\")\n",
"\n",
"# Process MNIST\n",
"X_test = mnist.test.images # X_test shape: [num_test, 28*28]\n",
"X_test = X_test.reshape([-1, step_size, input_size])\n",
"y_test = mnist.test.labels\n",
"\n",
"# initialize the variables\n",
"init = tf.global_variables_initializer()\n",
"\n",
"# Empty list for tracking\n",
"loss_train_list = []\n",
"acc_train_list = []\n",
"\n",
"# train the model\n",
"with tf.Session() as sess:\n",
" sess.run(init)\n",
" n_batches = mnist.train.num_examples // batch_size\n",
" for epoch in range(num_epoch):\n",
" for batch in range(n_batches):\n",
" X_train, y_train = mnist.train.next_batch(batch_size)\n",
" X_train = X_train.reshape([-1, step_size, input_size])\n",
" sess.run(optimizer, feed_dict={X: X_train, y: y_train})\n",
" loss_train, acc_train = sess.run(\n",
" [loss, accuracy], feed_dict={X: X_train, y: y_train})\n",
" loss_train_list.append(loss_train)\n",
" acc_train_list.append(acc_train)\n",
" print('Epoch: {}, Train Loss: {:.3f}, Train Acc: {:.3f}'.format(\n",
" epoch + 1, loss_train, acc_train))\n",
" loss_test, acc_test = sess.run(\n",
" [loss, accuracy], feed_dict={X: X_test, y: y_test})\n",
" print('Test Loss: {:.3f}, Test Acc: {:.3f}'.format(loss_test, acc_test))\n"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From <ipython-input-2-dc6c3a05d58e>:56: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please write your own downloading logic.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:252: _internal_retry.<locals>.wrap.<locals>.wrapped_fn (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use urllib or similar directly.\n",
"Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use tf.data to implement this functionality.\n",
"Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
"Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use tf.data to implement this functionality.\n",
"Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
"Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
"Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
"Epoch: 1, Train Loss: 0.279, Train Acc: 0.898\n",
"Epoch: 2, Train Loss: 0.124, Train Acc: 0.969\n",
"Epoch: 3, Train Loss: 0.145, Train Acc: 0.977\n",
"Epoch: 4, Train Loss: 0.231, Train Acc: 0.914\n",
"Epoch: 5, Train Loss: 0.088, Train Acc: 0.961\n",
"Epoch: 6, Train Loss: 0.104, Train Acc: 0.961\n",
"Epoch: 7, Train Loss: 0.174, Train Acc: 0.961\n",
"Epoch: 8, Train Loss: 0.099, Train Acc: 0.961\n",
"Epoch: 9, Train Loss: 0.075, Train Acc: 0.961\n",
"Epoch: 10, Train Loss: 0.081, Train Acc: 0.969\n",
"Test Loss: 0.124, Test Acc: 0.965\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "nkPppIILLN5Z",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}