mhjensen
158 lines
6.1 KiB
Plaintext
158 lines
6.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
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"Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
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"Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
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"Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
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"Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
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"Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
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"Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
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"Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n",
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"WARNING:tensorflow:From <ipython-input-1-92265976c34d>:45: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"\n",
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"Future major versions of TensorFlow will allow gradients to flow\n",
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"into the labels input on backprop by default.\n",
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"\n",
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"See tf.nn.softmax_cross_entropy_with_logits_v2.\n",
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"\n",
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"WARNING:tensorflow:From /anaconda3/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py:118: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
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"Instructions for updating:\n",
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"Use `tf.global_variables_initializer` instead.\n",
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"Epoch 0 completed out of 10 loss: 1664569.60834\n",
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"Epoch 1 completed out of 10 loss: 414550.968437\n",
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"Epoch 2 completed out of 10 loss: 229022.354944\n",
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"Epoch 3 completed out of 10 loss: 136420.393392\n",
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"Epoch 4 completed out of 10 loss: 88019.3560204\n",
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"Epoch 5 completed out of 10 loss: 56024.820509\n",
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"Epoch 6 completed out of 10 loss: 37434.4423951\n",
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"Epoch 7 completed out of 10 loss: 29640.3100017\n",
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"Epoch 8 completed out of 10 loss: 24399.9572706\n",
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"Epoch 9 completed out of 10 loss: 23351.0056713\n",
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"Accuracy: 0.9534\n"
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]
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}
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],
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"source": [
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"import tensorflow as tf\n",
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"from tensorflow.examples.tutorials.mnist import input_data\n",
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"mnist=input_data.read_data_sets(\"/tmp/data/\", one_hot=True) #one component is on, all others are off\n",
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"#10 classes, 0 through 9\n",
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"#one-hot outputs 0=[1,0,0,0,0,0,0,0,0], being the 1 is in the algorithm's guess (0)\n",
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"#3=[0,0,0,1,0,0,0,0,0]\n",
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"n_nodes_hl1=500 #hl1 = hidden layer 1\n",
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"n_nodes_hl2=500\n",
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"n_nodes_hl3=500\n",
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"n_classes=10 #number of categories\n",
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"batch_size=100 #divies up the data to be more efficient, as opposed to loading all samples at once\n",
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"\n",
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"x=tf.placeholder('float',[None, 784])\n",
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"y=tf.placeholder('float')\n",
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"\n",
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"def neural_network_model(data):\n",
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" #(inputs*weights)+biases\n",
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" hidden_1_layer={'weights':tf.Variable(tf.random_normal([784, n_nodes_hl1])), \n",
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" 'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))}\n",
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" \n",
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" hidden_2_layer={'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])), \n",
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" 'biases': tf.Variable(tf.random_normal([n_nodes_hl2]))}\n",
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" \n",
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" hidden_3_layer={'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])), \n",
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" 'biases': tf.Variable(tf.random_normal([n_nodes_hl3]))}\n",
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" \n",
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" output_layer={'weights':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),\n",
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" 'biases': tf.Variable(tf.random_normal([n_classes]))}\n",
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" \n",
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" l1=tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['biases'])\n",
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" l1=tf.nn.relu(l1)\n",
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" \n",
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" l2=tf.add(tf.matmul(l1, hidden_2_layer['weights']), hidden_2_layer['biases'])\n",
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" l2=tf.nn.relu(l2)\n",
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" \n",
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" l3=tf.add(tf.matmul(l2, hidden_3_layer['weights']), hidden_3_layer['biases'])\n",
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" l3=tf.nn.relu(l3)\n",
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" \n",
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" output=tf.matmul(l3, output_layer['weights'])+ output_layer['biases']\n",
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" \n",
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" return output\n",
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" \n",
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"def train_neural_network(x):\n",
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" prediction=neural_network_model(x)\n",
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" cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y))\n",
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" optimizer=tf.train.AdamOptimizer().minimize(cost)\n",
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" \n",
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" hm_epochs=10\n",
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" \n",
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" with tf.Session() as sess:\n",
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" sess.run(tf.initialize_all_variables())\n",
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" \n",
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" for epoch in range(hm_epochs):\n",
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" epoch_loss=0\n",
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" for _ in range(int(mnist.train.num_examples/batch_size)):\n",
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" epoch_x,epoch_y=mnist.train.next_batch(batch_size)\n",
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" _,c=sess.run([optimizer,cost], feed_dict={x:epoch_x, y:epoch_y})\n",
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" epoch_loss+=c\n",
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" print('Epoch', epoch, 'completed out of ', hm_epochs, 'loss:', epoch_loss)\n",
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" \n",
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" correct=tf.equal(tf.argmax(prediction,1), tf.argmax(y,1))\n",
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" \n",
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" accuracy=tf.reduce_mean(tf.cast(correct, 'float'))\n",
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" print('Accuracy:', accuracy.eval({x:mnist.test.images, y:mnist.test.labels}))\n",
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" \n",
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"\n",
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" \n",
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" \n",
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"train_neural_network(x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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"nbformat": 4,
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}
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