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Add tensorboard_advanced.ipynb (aymericdamien#152)
* Make print function Python 3 compatible * Add notebook for tensorboard advanced tutorial
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‎README.md‎

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@@ -31,7 +31,8 @@ Note: If you are using older TensorFlow version (before 0.12), please have a [lo
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#### 4 - Utilities
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- Save and Restore a model ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/save_restore_model.py))
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- Tensorboard - Graph and loss visualization ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_basic.py))
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- Tensorboard - Advanced visualization ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_advanced.py))
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- Tensorboard - Advanced visualization
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([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_advanced.py))
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#### 5 - Multi GPU
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- Basic Operations on multi-GPU ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_basics.py))

‎notebooks/4_Utils/save_restore_model.ipynb‎

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@@ -21,9 +21,7 @@
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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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"collapsed": false
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},
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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}
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],
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"source": [
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"from __future__ import print_function\n",
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"\n",
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"# Import MINST data\n",
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"from tensorflow.examples.tutorials.mnist import input_data\n",
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"mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n",
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# Parameters\n",
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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],
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"source": [
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"# Running first session\n",
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"print \"Starting 1st session...\"\n",
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"print(\"Starting 1st session...\")\n",
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"with tf.Session() as sess:\n",
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" # Initialize variables\n",
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" sess.run(init)\n",
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" if epoch % display_step == 0:\n",
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" print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \\\n",
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" \"{:.9f}\".format(avg_cost)\n",
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" print \"First Optimization Finished!\"\n",
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" print(\"First Optimization Finished!\")\n",
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"\n",
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" # Test model\n",
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" correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n",
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" # Calculate accuracy\n",
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" accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
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" print \"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})\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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" # Save model weights to disk\n",
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" save_path = saver.save(sess, model_path)\n",
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" print \"Model saved in file: %s\" % save_path"
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" print(\"Model saved in file: %s\" % save_path)"
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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": 6,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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],
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"source": [
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"# Running a new session\n",
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"print \"Starting 2nd session...\"\n",
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"print(\"Starting 2nd session...\")\n",
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"with tf.Session() as sess:\n",
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" # Initialize variables\n",
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" sess.run(init)\n",
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"\n",
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" # Restore model weights from previously saved model\n",
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" load_path = saver.restore(sess, model_path)\n",
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" print \"Model restored from file: %s\" % save_path\n",
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" print(\"Model restored from file: %s\" % save_path)\n",
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"\n",
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" # Resume training\n",
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" for epoch in range(7):\n",
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" avg_cost += c / total_batch\n",
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" # Display logs per epoch step\n",
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" if epoch % display_step == 0:\n",
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" print \"Epoch:\", '%04d' % (epoch + 1), \"cost=\", \\\n",
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" \"{:.9f}\".format(avg_cost)\n",
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" print \"Second Optimization Finished!\"\n",
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" print(\"Epoch:\", '%04d' % (epoch + 1), \"cost=\", \\\n",
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" \"{:.9f}\".format(avg_cost))\n",
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" print(\"Second Optimization Finished!\")\n",
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"\n",
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" # Test model\n",
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" correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n",
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" # Calculate accuracy\n",
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" accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
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" print \"Accuracy:\", accuracy.eval(\n",
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" {x: mnist.test.images, y: mnist.test.labels})"
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" print(\"Accuracy:\", accuracy.eval(\n",
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" {x: mnist.test.images, y: mnist.test.labels}))"
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]
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},
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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 2",
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"display_name": "Python 3",
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"language": "python",
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"name": "python2"
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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": 2
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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": "ipython2",
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"version": "2.7.13"
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"pygments_lexer": "ipython3",
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"version": "3.5.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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"nbformat_minor": 1
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}

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