766 lines
22 KiB
Plaintext
766 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Chapter 12 – Distributed TensorFlow**"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"_This notebook contains all the sample code and solutions to the exercises in chapter 12._"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Setup"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# To support both python 2 and python 3\n",
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"from __future__ import division, print_function, unicode_literals\n",
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"\n",
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"# Common imports\n",
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"import numpy as np\n",
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"import os\n",
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"\n",
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"# to make this notebook's output stable across runs\n",
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"def reset_graph(seed=42):\n",
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" tf.reset_default_graph()\n",
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" tf.set_random_seed(seed)\n",
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" np.random.seed(seed)\n",
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"\n",
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"# To plot pretty figures\n",
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"%matplotlib inline\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"plt.rcParams['axes.labelsize'] = 14\n",
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"plt.rcParams['xtick.labelsize'] = 12\n",
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"plt.rcParams['ytick.labelsize'] = 12\n",
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"\n",
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"# Where to save the figures\n",
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"PROJECT_ROOT_DIR = \".\"\n",
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"CHAPTER_ID = \"distributed\"\n",
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"\n",
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"def save_fig(fig_id, tight_layout=True):\n",
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" path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
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" print(\"Saving figure\", fig_id)\n",
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" if tight_layout:\n",
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" plt.tight_layout()\n",
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" plt.savefig(path, format='png', dpi=300)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Local server"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf"
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"c = tf.constant(\"Hello distributed TensorFlow!\")\n",
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"server = tf.train.Server.create_local_server()"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"with tf.Session(server.target) as sess:\n",
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" print(sess.run(c))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Cluster"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"cluster_spec = tf.train.ClusterSpec({\n",
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" \"ps\": [\n",
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" \"127.0.0.1:2221\", # /job:ps/task:0\n",
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" \"127.0.0.1:2222\", # /job:ps/task:1\n",
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" ],\n",
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" \"worker\": [\n",
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" \"127.0.0.1:2223\", # /job:worker/task:0\n",
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" \"127.0.0.1:2224\", # /job:worker/task:1\n",
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" \"127.0.0.1:2225\", # /job:worker/task:2\n",
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" ]})"
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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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"outputs": [],
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"source": [
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"task_ps0 = tf.train.Server(cluster_spec, job_name=\"ps\", task_index=0)\n",
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"task_ps1 = tf.train.Server(cluster_spec, job_name=\"ps\", task_index=1)\n",
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"task_worker0 = tf.train.Server(cluster_spec, job_name=\"worker\", task_index=0)\n",
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"task_worker1 = tf.train.Server(cluster_spec, job_name=\"worker\", task_index=1)\n",
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"task_worker2 = tf.train.Server(cluster_spec, job_name=\"worker\", task_index=2)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Pinning operations across devices and servers"
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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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"reset_graph()\n",
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"\n",
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"with tf.device(\"/job:ps\"):\n",
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" a = tf.Variable(1.0, name=\"a\")\n",
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"\n",
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"with tf.device(\"/job:worker\"):\n",
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" b = a + 2\n",
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"\n",
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"with tf.device(\"/job:worker/task:1\"):\n",
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" c = a + b"
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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"with tf.Session(\"grpc://127.0.0.1:2221\") as sess:\n",
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" sess.run(a.initializer)\n",
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" print(c.eval())"
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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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"reset_graph()\n",
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"\n",
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"with tf.device(tf.train.replica_device_setter(\n",
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" ps_tasks=2,\n",
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" ps_device=\"/job:ps\",\n",
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" worker_device=\"/job:worker\")):\n",
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" v1 = tf.Variable(1.0, name=\"v1\") # pinned to /job:ps/task:0 (defaults to /cpu:0)\n",
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" v2 = tf.Variable(2.0, name=\"v2\") # pinned to /job:ps/task:1 (defaults to /cpu:0)\n",
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" v3 = tf.Variable(3.0, name=\"v3\") # pinned to /job:ps/task:0 (defaults to /cpu:0)\n",
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" s = v1 + v2 # pinned to /job:worker (defaults to task:0/cpu:0)\n",
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" with tf.device(\"/task:1\"):\n",
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" p1 = 2 * s # pinned to /job:worker/task:1 (defaults to /cpu:0)\n",
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" with tf.device(\"/cpu:0\"):\n",
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" p2 = 3 * s # pinned to /job:worker/task:1/cpu:0\n",
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"\n",
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"config = tf.ConfigProto()\n",
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"config.log_device_placement = True\n",
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"\n",
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"with tf.Session(\"grpc://127.0.0.1:2221\", config=config) as sess:\n",
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" v1.initializer.run()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Readers – the old way"
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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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"reset_graph()"
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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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"default1 = tf.constant([5.])\n",
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"default2 = tf.constant([6])\n",
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"default3 = tf.constant([7])\n",
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"dec = tf.decode_csv(tf.constant(\"1.,,44\"),\n",
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" record_defaults=[default1, default2, default3])\n",
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"with tf.Session() as sess:\n",
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" print(sess.run(dec))"
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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": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"reset_graph()\n",
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"\n",
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"test_csv = open(\"my_test.csv\", \"w\")\n",
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"test_csv.write(\"x1, x2 , target\\n\")\n",
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"test_csv.write(\"1.,, 0\\n\")\n",
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"test_csv.write(\"4., 5. , 1\\n\")\n",
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"test_csv.write(\"7., 8. , 0\\n\")\n",
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"test_csv.close()\n",
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"\n",
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"filename_queue = tf.FIFOQueue(capacity=10, dtypes=[tf.string], shapes=[()])\n",
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"filename = tf.placeholder(tf.string)\n",
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"enqueue_filename = filename_queue.enqueue([filename])\n",
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"close_filename_queue = filename_queue.close()\n",
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"\n",
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"reader = tf.TextLineReader(skip_header_lines=1)\n",
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"key, value = reader.read(filename_queue)\n",
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"\n",
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"x1, x2, target = tf.decode_csv(value, record_defaults=[[-1.], [-1.], [-1]])\n",
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"features = tf.stack([x1, x2])\n",
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"\n",
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"instance_queue = tf.RandomShuffleQueue(\n",
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" capacity=10, min_after_dequeue=2,\n",
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" dtypes=[tf.float32, tf.int32], shapes=[[2],[]],\n",
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" name=\"instance_q\", shared_name=\"shared_instance_q\")\n",
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"enqueue_instance = instance_queue.enqueue([features, target])\n",
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"close_instance_queue = instance_queue.close()\n",
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"\n",
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"minibatch_instances, minibatch_targets = instance_queue.dequeue_up_to(2)\n",
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"\n",
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"with tf.Session() as sess:\n",
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" sess.run(enqueue_filename, feed_dict={filename: \"my_test.csv\"})\n",
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" sess.run(close_filename_queue)\n",
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" try:\n",
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" while True:\n",
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" sess.run(enqueue_instance)\n",
|
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" except tf.errors.OutOfRangeError as ex:\n",
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" print(\"No more files to read\")\n",
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" sess.run(close_instance_queue)\n",
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" try:\n",
|
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" while True:\n",
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" print(sess.run([minibatch_instances, minibatch_targets]))\n",
|
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" except tf.errors.OutOfRangeError as ex:\n",
|
||
" print(\"No more training instances\")"
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]
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},
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{
|
||
"cell_type": "code",
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"execution_count": 13,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
|
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"#coord = tf.train.Coordinator()\n",
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"#threads = tf.train.start_queue_runners(coord=coord)\n",
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"#filename_queue = tf.train.string_input_producer([\"test.csv\"])\n",
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"#coord.request_stop()\n",
|
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"#coord.join(threads)"
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||
]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"# Queue runners and coordinators"
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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": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"reset_graph()\n",
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"\n",
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"filename_queue = tf.FIFOQueue(capacity=10, dtypes=[tf.string], shapes=[()])\n",
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"filename = tf.placeholder(tf.string)\n",
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"enqueue_filename = filename_queue.enqueue([filename])\n",
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"close_filename_queue = filename_queue.close()\n",
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"\n",
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"reader = tf.TextLineReader(skip_header_lines=1)\n",
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"key, value = reader.read(filename_queue)\n",
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"\n",
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"x1, x2, target = tf.decode_csv(value, record_defaults=[[-1.], [-1.], [-1]])\n",
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"features = tf.stack([x1, x2])\n",
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"\n",
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"instance_queue = tf.RandomShuffleQueue(\n",
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" capacity=10, min_after_dequeue=2,\n",
|
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" dtypes=[tf.float32, tf.int32], shapes=[[2],[]],\n",
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" name=\"instance_q\", shared_name=\"shared_instance_q\")\n",
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"enqueue_instance = instance_queue.enqueue([features, target])\n",
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"close_instance_queue = instance_queue.close()\n",
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"\n",
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"minibatch_instances, minibatch_targets = instance_queue.dequeue_up_to(2)\n",
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"\n",
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"n_threads = 5\n",
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"queue_runner = tf.train.QueueRunner(instance_queue, [enqueue_instance] * n_threads)\n",
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"coord = tf.train.Coordinator()\n",
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"\n",
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"with tf.Session() as sess:\n",
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" sess.run(enqueue_filename, feed_dict={filename: \"my_test.csv\"})\n",
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" sess.run(close_filename_queue)\n",
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" enqueue_threads = queue_runner.create_threads(sess, coord=coord, start=True)\n",
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" try:\n",
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" while True:\n",
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" print(sess.run([minibatch_instances, minibatch_targets]))\n",
|
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" except tf.errors.OutOfRangeError as ex:\n",
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" print(\"No more training instances\")"
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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": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"reset_graph()\n",
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"\n",
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"def read_and_push_instance(filename_queue, instance_queue):\n",
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" reader = tf.TextLineReader(skip_header_lines=1)\n",
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" key, value = reader.read(filename_queue)\n",
|
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" x1, x2, target = tf.decode_csv(value, record_defaults=[[-1.], [-1.], [-1]])\n",
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" features = tf.stack([x1, x2])\n",
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" enqueue_instance = instance_queue.enqueue([features, target])\n",
|
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" return enqueue_instance\n",
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"\n",
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"filename_queue = tf.FIFOQueue(capacity=10, dtypes=[tf.string], shapes=[()])\n",
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"filename = tf.placeholder(tf.string)\n",
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"enqueue_filename = filename_queue.enqueue([filename])\n",
|
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"close_filename_queue = filename_queue.close()\n",
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"\n",
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"instance_queue = tf.RandomShuffleQueue(\n",
|
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" capacity=10, min_after_dequeue=2,\n",
|
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" dtypes=[tf.float32, tf.int32], shapes=[[2],[]],\n",
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" name=\"instance_q\", shared_name=\"shared_instance_q\")\n",
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"\n",
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"minibatch_instances, minibatch_targets = instance_queue.dequeue_up_to(2)\n",
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"\n",
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"read_and_enqueue_ops = [read_and_push_instance(filename_queue, instance_queue) for i in range(5)]\n",
|
||
"queue_runner = tf.train.QueueRunner(instance_queue, read_and_enqueue_ops)\n",
|
||
"\n",
|
||
"with tf.Session() as sess:\n",
|
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" sess.run(enqueue_filename, feed_dict={filename: \"my_test.csv\"})\n",
|
||
" sess.run(close_filename_queue)\n",
|
||
" coord = tf.train.Coordinator()\n",
|
||
" enqueue_threads = queue_runner.create_threads(sess, coord=coord, start=True)\n",
|
||
" try:\n",
|
||
" while True:\n",
|
||
" print(sess.run([minibatch_instances, minibatch_targets]))\n",
|
||
" except tf.errors.OutOfRangeError as ex:\n",
|
||
" print(\"No more training instances\")\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Setting a timeout"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"reset_graph()\n",
|
||
"\n",
|
||
"q = tf.FIFOQueue(capacity=10, dtypes=[tf.float32], shapes=[()])\n",
|
||
"v = tf.placeholder(tf.float32)\n",
|
||
"enqueue = q.enqueue([v])\n",
|
||
"dequeue = q.dequeue()\n",
|
||
"output = dequeue + 1\n",
|
||
"\n",
|
||
"config = tf.ConfigProto()\n",
|
||
"config.operation_timeout_in_ms = 1000\n",
|
||
"\n",
|
||
"with tf.Session(config=config) as sess:\n",
|
||
" sess.run(enqueue, feed_dict={v: 1.0})\n",
|
||
" sess.run(enqueue, feed_dict={v: 2.0})\n",
|
||
" sess.run(enqueue, feed_dict={v: 3.0})\n",
|
||
" print(sess.run(output))\n",
|
||
" print(sess.run(output, feed_dict={dequeue: 5}))\n",
|
||
" print(sess.run(output))\n",
|
||
" print(sess.run(output))\n",
|
||
" try:\n",
|
||
" print(sess.run(output))\n",
|
||
" except tf.errors.DeadlineExceededError as ex:\n",
|
||
" print(\"Timed out while dequeuing\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Data API"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The Data API, introduced in TensorFlow 1.4, makes reading data efficiently much easier."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"tf.reset_default_graph()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Let's start with a simple dataset composed of three times the integers 0 to 9, in batches of 7:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"dataset = tf.data.Dataset.from_tensor_slices(np.arange(10))\n",
|
||
"dataset = dataset.repeat(3).batch(7)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The first line creates a dataset containing the integers 0 through 9. The second line creates a new dataset based on the first one, repeating its elements three times and creating batches of 7 elements. As you can see, we start with a source dataset, then we chain calls to various methods to apply transformations to the data."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Next, we create a one-shot-iterator to go through this dataset just once, and we call its `get_next()` method to get a tensor that represents the next element."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"iterator = dataset.make_one_shot_iterator()\n",
|
||
"next_element = iterator.get_next()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Let's repeatedly evaluate `next_element` to go through the dataset. When there are not more elements, we get an `OutOfRangeError`:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"with tf.Session() as sess:\n",
|
||
" try:\n",
|
||
" while True:\n",
|
||
" print(next_element.eval())\n",
|
||
" except tf.errors.OutOfRangeError:\n",
|
||
" print(\"Done\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Great! It worked fine."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Note that, as always, a tensor is only evaluated once each time we run the graph (`sess.run()`): so even if we evaluate multiple tensors that all depend on `next_element`, it is only evaluated once. This is true as well if we ask for `next_element` to be evaluated twice in just one run:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"with tf.Session() as sess:\n",
|
||
" try:\n",
|
||
" while True:\n",
|
||
" print(sess.run([next_element, next_element]))\n",
|
||
" except tf.errors.OutOfRangeError:\n",
|
||
" print(\"Done\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The `interleave()` method is powerful but a bit tricky to grasp at first. The easiest way to understand it is to look at an example:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"tf.reset_default_graph()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"dataset = tf.data.Dataset.from_tensor_slices(np.arange(10))\n",
|
||
"dataset = dataset.repeat(3).batch(7)\n",
|
||
"dataset = dataset.interleave(\n",
|
||
" lambda v: tf.data.Dataset.from_tensor_slices(v),\n",
|
||
" cycle_length=3,\n",
|
||
" block_length=2)\n",
|
||
"iterator = dataset.make_one_shot_iterator()\n",
|
||
"next_element = iterator.get_next()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"with tf.Session() as sess:\n",
|
||
" try:\n",
|
||
" while True:\n",
|
||
" print(next_element.eval(), end=\",\")\n",
|
||
" except tf.errors.OutOfRangeError:\n",
|
||
" print(\"Done\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Because `cycle_length=3`, the new dataset starts by pulling 3 elements from the previous dataset: that's `[0,1,2,3,4,5,6]`, `[7,8,9,0,1,2,3]` and `[4,5,6,7,8,9,0]`. Then it calls the lambda function we gave it to create one dataset for each of the elements. Since we use `Dataset.from_tensor_slices()`, each dataset is going to return its elements one by one. Next, it pulls two items (since `block_length=2`) from each of these three datasets, and it iterates until all three datasets are out of items: 0,1 (from 1st), 7,8 (from 2nd), 4,5 (from 3rd), 2,3 (from 1st), 9,0 (from 2nd), and so on until 8,9 (from 3rd), 6 (from 1st), 3 (from 2nd), 0 (from 3rd). Next it tries to pull the next 3 elements from the original dataset, but there are just two left: `[1,2,3,4,5,6,7]` and `[8,9]`. Again, it creates datasets from these elements, and it pulls two items from each until both datasets are out of items: 1,2 (from 1st), 8,9 (from 2nd), 3,4 (from 1st), 5,6 (from 1st), 7 (from 1st). Notice that there's no interleaving at the end since the arrays do not have the same length."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Readers – the new way"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Instead of using a source dataset based on `from_tensor_slices()` or `from_tensor()`, we can use a reader dataset. It handles most of the complexity for us (e.g., threads):"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"tf.reset_default_graph()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"filenames = [\"my_test.csv\"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"dataset = tf.data.TextLineDataset(filenames)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"We still need to tell it how to decode each line:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def decode_csv_line(line):\n",
|
||
" x1, x2, y = tf.decode_csv(\n",
|
||
" line, record_defaults=[[-1.], [-1.], [-1.]])\n",
|
||
" X = tf.stack([x1, x2])\n",
|
||
" return X, y"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Next, we can apply this decoding function to each element in the dataset using `map()`:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"dataset = dataset.skip(1).map(decode_csv_line)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Finally, let's create a one-shot iterator:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"it = dataset.make_one_shot_iterator()\n",
|
||
"X, y = it.get_next()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"with tf.Session() as sess:\n",
|
||
" try:\n",
|
||
" while True:\n",
|
||
" X_val, y_val = sess.run([X, y])\n",
|
||
" print(X_val, y_val)\n",
|
||
" except tf.errors.OutOfRangeError as ex:\n",
|
||
" print(\"Done\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"source": [
|
||
"# Exercise solutions"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Coming soon**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.6.5"
|
||
},
|
||
"nav_menu": {},
|
||
"toc": {
|
||
"navigate_menu": true,
|
||
"number_sections": true,
|
||
"sideBar": true,
|
||
"threshold": 6,
|
||
"toc_cell": false,
|
||
"toc_section_display": "block",
|
||
"toc_window_display": false
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 1
|
||
}
|