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Diffstat (limited to 'tensorflow/contrib/autograph/examples/notebooks/ag_vs_eager_mnist_speed_test.ipynb')
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diff --git a/tensorflow/contrib/autograph/examples/notebooks/ag_vs_eager_mnist_speed_test.ipynb b/tensorflow/contrib/autograph/examples/notebooks/ag_vs_eager_mnist_speed_test.ipynb new file mode 100644 index 0000000000..952ec091fb --- /dev/null +++ b/tensorflow/contrib/autograph/examples/notebooks/ag_vs_eager_mnist_speed_test.ipynb @@ -0,0 +1,652 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "etTmZVFN8fYO" + }, + "source": [ + "This notebook runs a basic speed test for a short training loop of a neural network training on the MNIST dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eqOvRhOz8SWs" + }, + "source": [ + "### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "nHY0tntRizGb" + }, + "outputs": [], + "source": [ + "!pip install -U -q tf-nightly" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "Pa2qpEmoVOGe" + }, + "outputs": [], + "source": [ + "import gzip\n", + "import os\n", + "import shutil\n", + "import time\n", + "\n", + "import numpy as np\n", + "import six\n", + "from six.moves import urllib\n", + "import tensorflow as tf\n", + "\n", + "from tensorflow.contrib import autograph as ag\n", + "from tensorflow.contrib.eager.python import tfe\n", + "from tensorflow.python.eager import context\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "PZWxEJFM9A7b" + }, + "source": [ + "### Testing boilerplate" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "kfZk9EFZ5TeQ" + }, + "outputs": [], + "source": [ + "# Test-only parameters. Test checks successful completion not correctness. \n", + "burn_ins = 1\n", + "trials = 1\n", + "max_steps = 2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "k0GKbZBJ9Gt9" + }, + "source": [ + "### Speed test configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "gWXV8WHn43iZ" + }, + "outputs": [], + "source": [ + "#@test {\"skip\": true} \n", + "burn_ins = 3\n", + "trials = 10\n", + "max_steps = 500\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kZV_3pGy8033" + }, + "source": [ + "### Data source setup" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "YfnHJbBOBKae" + }, + "outputs": [], + "source": [ + "def download(directory, filename):\n", + " filepath = os.path.join(directory, filename)\n", + " if tf.gfile.Exists(filepath):\n", + " return filepath\n", + " if not tf.gfile.Exists(directory):\n", + " tf.gfile.MakeDirs(directory)\n", + " url = 'https://storage.googleapis.com/cvdf-datasets/mnist/' + filename + '.gz'\n", + " zipped_filepath = filepath + '.gz'\n", + " print('Downloading %s to %s' % (url, zipped_filepath))\n", + " urllib.request.urlretrieve(url, zipped_filepath)\n", + " with gzip.open(zipped_filepath, 'rb') as f_in, open(filepath, 'wb') as f_out:\n", + " shutil.copyfileobj(f_in, f_out)\n", + " os.remove(zipped_filepath)\n", + " return filepath\n", + "\n", + "\n", + "def dataset(directory, images_file, labels_file):\n", + " images_file = download(directory, images_file)\n", + " labels_file = download(directory, labels_file)\n", + "\n", + " def decode_image(image):\n", + " # Normalize from [0, 255] to [0.0, 1.0]\n", + " image = tf.decode_raw(image, tf.uint8)\n", + " image = tf.cast(image, tf.float32)\n", + " image = tf.reshape(image, [784])\n", + " return image / 255.0\n", + "\n", + " def decode_label(label):\n", + " label = tf.decode_raw(label, tf.uint8)\n", + " label = tf.reshape(label, [])\n", + " return tf.to_int32(label)\n", + "\n", + " images = tf.data.FixedLengthRecordDataset(\n", + " images_file, 28 * 28, header_bytes=16).map(decode_image)\n", + " labels = tf.data.FixedLengthRecordDataset(\n", + " labels_file, 1, header_bytes=8).map(decode_label)\n", + " return tf.data.Dataset.zip((images, labels))\n", + "\n", + "\n", + "def mnist_train(directory):\n", + " return dataset(directory, 'train-images-idx3-ubyte',\n", + " 'train-labels-idx1-ubyte')\n", + "\n", + "def mnist_test(directory):\n", + " return dataset(directory, 't10k-images-idx3-ubyte', 't10k-labels-idx1-ubyte')\n", + "\n", + "def setup_mnist_data(is_training, hp, batch_size):\n", + " if is_training:\n", + " ds = mnist_train('/tmp/autograph_mnist_data')\n", + " ds = ds.cache()\n", + " ds = ds.shuffle(batch_size * 10)\n", + " else:\n", + " ds = mnist_test('/tmp/autograph_mnist_data')\n", + " ds = ds.cache()\n", + " ds = ds.repeat()\n", + " ds = ds.batch(batch_size)\n", + " return ds\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "qzkZyZcS9THu" + }, + "source": [ + "### Keras model definition" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "x_MU13boiok2" + }, + "outputs": [], + "source": [ + "def mlp_model(input_shape):\n", + " model = tf.keras.Sequential((\n", + " tf.keras.layers.Dense(100, activation='relu', input_shape=input_shape),\n", + " tf.keras.layers.Dense(100, activation='relu'),\n", + " tf.keras.layers.Dense(10, activation='softmax')))\n", + " model.build()\n", + " return model\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "DXt4GoTxtvn2" + }, + "source": [ + "# AutoGraph" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "W51sfbONiz_5" + }, + "outputs": [], + "source": [ + "def predict(m, x, y):\n", + " y_p = m(x)\n", + " losses = tf.keras.losses.categorical_crossentropy(y, y_p)\n", + " l = tf.reduce_mean(losses)\n", + " accuracies = tf.keras.metrics.categorical_accuracy(y, y_p)\n", + " accuracy = tf.reduce_mean(accuracies)\n", + " return l, accuracy\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "CsAD0ajbi9iZ" + }, + "outputs": [], + "source": [ + "def fit(m, x, y, opt):\n", + " l, accuracy = predict(m, x, y)\n", + " opt.minimize(l)\n", + " return l, accuracy\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "RVw57HdTjPzi" + }, + "outputs": [], + "source": [ + "def get_next_batch(ds):\n", + " itr = ds.make_one_shot_iterator()\n", + " image, label = itr.get_next()\n", + " x = tf.to_float(tf.reshape(image, (-1, 28 * 28)))\n", + " y = tf.one_hot(tf.squeeze(label), 10)\n", + " return x, y\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "UUI0566FjZPx" + }, + "outputs": [], + "source": [ + "def train(train_ds, test_ds, hp):\n", + " m = mlp_model((28 * 28,))\n", + " opt = tf.train.MomentumOptimizer(hp.learning_rate, 0.9)\n", + "\n", + " train_losses = []\n", + " test_losses = []\n", + " train_accuracies = []\n", + " test_accuracies = []\n", + " ag.set_element_type(train_losses, tf.float32)\n", + " ag.set_element_type(test_losses, tf.float32)\n", + " ag.set_element_type(train_accuracies, tf.float32)\n", + " ag.set_element_type(test_accuracies, tf.float32)\n", + "\n", + " i = tf.constant(0)\n", + " while i \u003c hp.max_steps:\n", + " train_x, train_y = get_next_batch(train_ds)\n", + " test_x, test_y = get_next_batch(test_ds)\n", + " step_train_loss, step_train_accuracy = fit(m, train_x, train_y, opt)\n", + " step_test_loss, step_test_accuracy = predict(m, test_x, test_y)\n", + "\n", + " train_losses.append(step_train_loss)\n", + " test_losses.append(step_test_loss)\n", + " train_accuracies.append(step_train_accuracy)\n", + " test_accuracies.append(step_test_accuracy)\n", + "\n", + " i += 1\n", + " return (ag.stack(train_losses), ag.stack(test_losses),\n", + " ag.stack(train_accuracies), ag.stack(test_accuracies))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 215 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 12156, + "status": "ok", + "timestamp": 1531752050611, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "K1m8TwOKjdNd", + "outputId": "bd5746f2-bf91-44aa-9eff-38eb11ced33f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Duration:', 0.6226680278778076)\n", + "('Duration:', 0.6082069873809814)\n", + "('Duration:', 0.6223258972167969)\n", + "('Duration:', 0.6176440715789795)\n", + "('Duration:', 0.6309840679168701)\n", + "('Duration:', 0.6180410385131836)\n", + "('Duration:', 0.6219630241394043)\n", + "('Duration:', 0.6183009147644043)\n", + "('Duration:', 0.6176400184631348)\n", + "('Duration:', 0.6476900577545166)\n", + "('Mean duration:', 0.62254641056060789, '+/-', 0.0099792188690656976)\n" + ] + } + ], + "source": [ + "#@test {\"timeout\": 90}\n", + "with tf.Graph().as_default():\n", + " hp = tf.contrib.training.HParams(\n", + " learning_rate=0.05,\n", + " max_steps=max_steps,\n", + " )\n", + " train_ds = setup_mnist_data(True, hp, 500)\n", + " test_ds = setup_mnist_data(False, hp, 100)\n", + " tf_train = ag.to_graph(train)\n", + " losses = tf_train(train_ds, test_ds, hp)\n", + "\n", + " with tf.Session() as sess:\n", + " durations = []\n", + " for t in range(burn_ins + trials):\n", + " sess.run(tf.global_variables_initializer())\n", + "\n", + " start = time.time()\n", + " (train_losses, test_losses, train_accuracies,\n", + " test_accuracies) = sess.run(losses)\n", + "\n", + " if t \u003c burn_ins:\n", + " continue\n", + "\n", + " duration = time.time() - start\n", + " durations.append(duration)\n", + " print('Duration:', duration)\n", + "\n", + " print('Mean duration:', np.mean(durations), '+/-', np.std(durations))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "A06kdgtZtlce" + }, + "source": [ + "# Eager" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "hBKOKGrWty4e" + }, + "outputs": [], + "source": [ + "def predict(m, x, y):\n", + " y_p = m(x)\n", + " losses = tf.keras.losses.categorical_crossentropy(tf.cast(y, tf.float32), y_p)\n", + " l = tf.reduce_mean(losses)\n", + " accuracies = tf.keras.metrics.categorical_accuracy(y, y_p)\n", + " accuracy = tf.reduce_mean(accuracies)\n", + " return l, accuracy\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "HCgTZ0MTt6vt" + }, + "outputs": [], + "source": [ + "def train(ds, hp):\n", + " m = mlp_model((28 * 28,))\n", + " opt = tf.train.MomentumOptimizer(hp.learning_rate, 0.9)\n", + "\n", + " train_losses = []\n", + " test_losses = []\n", + " train_accuracies = []\n", + " test_accuracies = []\n", + "\n", + " i = 0\n", + " train_test_itr = tfe.Iterator(ds)\n", + " for (train_x, train_y), (test_x, test_y) in train_test_itr:\n", + " train_x = tf.to_float(tf.reshape(train_x, (-1, 28 * 28)))\n", + " train_y = tf.one_hot(tf.squeeze(train_y), 10)\n", + " test_x = tf.to_float(tf.reshape(test_x, (-1, 28 * 28)))\n", + " test_y = tf.one_hot(tf.squeeze(test_y), 10)\n", + "\n", + " if i \u003e hp.max_steps:\n", + " break\n", + "\n", + " with tf.GradientTape() as tape:\n", + " step_train_loss, step_train_accuracy = predict(m, train_x, train_y)\n", + " grad = tape.gradient(step_train_loss, m.variables)\n", + " opt.apply_gradients(zip(grad, m.variables))\n", + " step_test_loss, step_test_accuracy = predict(m, test_x, test_y)\n", + "\n", + " train_losses.append(step_train_loss)\n", + " test_losses.append(step_test_loss)\n", + " train_accuracies.append(step_train_accuracy)\n", + " test_accuracies.append(step_test_accuracy)\n", + "\n", + " i += 1\n", + " return train_losses, test_losses, train_accuracies, test_accuracies\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 215 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 52499, + "status": "ok", + "timestamp": 1531752103279, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "plv_yrn_t8Dy", + "outputId": "55d5ab3d-252d-48ba-8fb4-20ec3c3e6d00" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Duration:', 3.9973549842834473)\n", + "('Duration:', 4.018772125244141)\n", + "('Duration:', 3.9740989208221436)\n", + "('Duration:', 3.9922947883605957)\n", + "('Duration:', 3.9795801639556885)\n", + "('Duration:', 3.966722011566162)\n", + "('Duration:', 3.986541986465454)\n", + "('Duration:', 3.992305040359497)\n", + "('Duration:', 4.012261867523193)\n", + "('Duration:', 4.004716157913208)\n", + "('Mean duration:', 3.9924648046493529, '+/-', 0.015681688635624851)\n" + ] + } + ], + "source": [ + "#@test {\"timeout\": 90}\n", + "with context.eager_mode():\n", + " durations = []\n", + " for t in range(burn_ins + trials):\n", + " hp = tf.contrib.training.HParams(\n", + " learning_rate=0.05,\n", + " max_steps=max_steps,\n", + " )\n", + " train_ds = setup_mnist_data(True, hp, 500)\n", + " test_ds = setup_mnist_data(False, hp, 100)\n", + " ds = tf.data.Dataset.zip((train_ds, test_ds))\n", + " start = time.time()\n", + " (train_losses, test_losses, train_accuracies,\n", + " test_accuracies) = train(ds, hp)\n", + " \n", + " train_losses[-1].numpy()\n", + " test_losses[-1].numpy()\n", + " train_accuracies[-1].numpy()\n", + " test_accuracies[-1].numpy()\n", + "\n", + " if t \u003c burn_ins:\n", + " continue\n", + "\n", + " duration = time.time() - start\n", + " durations.append(duration)\n", + " print('Duration:', duration)\n", + "\n", + " print('Mean duration:', np.mean(durations), '+/-', np.std(durations))\n" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "eqOvRhOz8SWs", + "PZWxEJFM9A7b", + "kZV_3pGy8033" + ], + "default_view": {}, + "name": "Autograph vs. Eager MNIST speed test", + "provenance": [ + { + "file_id": "1tAQW5tHUgAc8M4-iwwJm6Xs6dV9nEqtD", + "timestamp": 1530297010607 + }, + { + "file_id": "18dCjshrmHiPTIe1CNsL8tnpdGkuXgpM9", + "timestamp": 1530289467317 + }, + { + "file_id": "1DcfimonWU11tmyivKBGVrbpAl3BIOaRG", + "timestamp": 1522272821237 + }, + { + "file_id": "1wCZUh73zTNs1jzzYjqoxMIdaBWCdKJ2K", + "timestamp": 1522238054357 + }, + { + "file_id": "1_HpC-RrmIv4lNaqeoslUeWaX8zH5IXaJ", + "timestamp": 1521743157199 + }, + { + "file_id": "1mjO2fQ2F9hxpAzw2mnrrUkcgfb7xSGW-", + "timestamp": 1520522344607 + } + ], + "version": "0.3.2", + "views": {} + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} |