{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Chapter 12 – Distributed TensorFlow**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_This notebook contains all the sample code and solutions to the exercises in chapter 12._"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# To support both python 2 and python 3\n",
"from __future__ import division, print_function, unicode_literals\n",
"\n",
"# Common imports\n",
"import numpy as np\n",
"import os\n",
"\n",
"# to make this notebook's output stable across runs\n",
"def reset_graph(seed=42):\n",
" tf.reset_default_graph()\n",
" tf.set_random_seed(seed)\n",
" np.random.seed(seed)\n",
"\n",
"# To plot pretty figures\n",
"%matplotlib inline\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams['axes.labelsize'] = 14\n",
"plt.rcParams['xtick.labelsize'] = 12\n",
"plt.rcParams['ytick.labelsize'] = 12\n",
"\n",
"# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n",
"CHAPTER_ID = \"distributed\"\n",
"\n",
"def save_fig(fig_id, tight_layout=True):\n",
" path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
" print(\"Saving figure\", fig_id)\n",
" if tight_layout:\n",
" plt.tight_layout()\n",
" plt.savefig(path, format='png', dpi=300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Local server"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"c = tf.constant(\"Hello distributed TensorFlow!\")\n",
"server = tf.train.Server.create_local_server()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"b'Hello distributed TensorFlow!'\n"
]
}
],
"source": [
"with tf.Session(server.target) as sess:\n",
" print(sess.run(c))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cluster"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"cluster_spec = tf.train.ClusterSpec({\n",
" \"ps\": [\n",
" \"127.0.0.1:2221\", # /job:ps/task:0\n",
" \"127.0.0.1:2222\", # /job:ps/task:1\n",
" ],\n",
" \"worker\": [\n",
" \"127.0.0.1:2223\", # /job:worker/task:0\n",
" \"127.0.0.1:2224\", # /job:worker/task:1\n",
" \"127.0.0.1:2225\", # /job:worker/task:2\n",
" ]})"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"task_ps0 = tf.train.Server(cluster_spec, job_name=\"ps\", task_index=0)\n",
"task_ps1 = tf.train.Server(cluster_spec, job_name=\"ps\", task_index=1)\n",
"task_worker0 = tf.train.Server(cluster_spec, job_name=\"worker\", task_index=0)\n",
"task_worker1 = tf.train.Server(cluster_spec, job_name=\"worker\", task_index=1)\n",
"task_worker2 = tf.train.Server(cluster_spec, job_name=\"worker\", task_index=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pinning operations across devices and servers"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"reset_graph()\n",
"\n",
"with tf.device(\"/job:ps\"):\n",
" a = tf.Variable(1.0, name=\"a\")\n",
"\n",
"with tf.device(\"/job:worker\"):\n",
" b = a + 2\n",
"\n",
"with tf.device(\"/job:worker/task:1\"):\n",
" c = a + b"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.0\n"
]
}
],
"source": [
"with tf.Session(\"grpc://127.0.0.1:2221\") as sess:\n",
" sess.run(a.initializer)\n",
" print(c.eval())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"reset_graph()\n",
"\n",
"with tf.device(tf.train.replica_device_setter(\n",
" ps_tasks=2,\n",
" ps_device=\"/job:ps\",\n",
" worker_device=\"/job:worker\")):\n",
" v1 = tf.Variable(1.0, name=\"v1\") # pinned to /job:ps/task:0 (defaults to /cpu:0)\n",
" v2 = tf.Variable(2.0, name=\"v2\") # pinned to /job:ps/task:1 (defaults to /cpu:0)\n",
" v3 = tf.Variable(3.0, name=\"v3\") # pinned to /job:ps/task:0 (defaults to /cpu:0)\n",
" s = v1 + v2 # pinned to /job:worker (defaults to task:0/cpu:0)\n",
" with tf.device(\"/task:1\"):\n",
" p1 = 2 * s # pinned to /job:worker/task:1 (defaults to /cpu:0)\n",
" with tf.device(\"/cpu:0\"):\n",
" p2 = 3 * s # pinned to /job:worker/task:1/cpu:0\n",
"\n",
"config = tf.ConfigProto()\n",
"config.log_device_placement = True\n",
"\n",
"with tf.Session(\"grpc://127.0.0.1:2221\", config=config) as sess:\n",
" v1.initializer.run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Readers – the old way"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"reset_graph()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1.0, 6, 44]\n"
]
}
],
"source": [
"default1 = tf.constant([5.])\n",
"default2 = tf.constant([6])\n",
"default3 = tf.constant([7])\n",
"dec = tf.decode_csv(tf.constant(\"1.,,44\"),\n",
" record_defaults=[default1, default2, default3])\n",
"with tf.Session() as sess:\n",
" print(sess.run(dec))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No more files to read\n",
"[array([[ 4., 5.],\n",
" [ 1., -1.]], dtype=float32), array([1, 0], dtype=int32)]\n",
"[array([[7., 8.]], dtype=float32), array([0], dtype=int32)]\n",
"No more training instances\n"
]
}
],
"source": [
"reset_graph()\n",
"\n",
"test_csv = open(\"my_test.csv\", \"w\")\n",
"test_csv.write(\"x1, x2 , target\\n\")\n",
"test_csv.write(\"1.,, 0\\n\")\n",
"test_csv.write(\"4., 5. , 1\\n\")\n",
"test_csv.write(\"7., 8. , 0\\n\")\n",
"test_csv.close()\n",
"\n",
"filename_queue = tf.FIFOQueue(capacity=10, dtypes=[tf.string], shapes=[()])\n",
"filename = tf.placeholder(tf.string)\n",
"enqueue_filename = filename_queue.enqueue([filename])\n",
"close_filename_queue = filename_queue.close()\n",
"\n",
"reader = tf.TextLineReader(skip_header_lines=1)\n",
"key, value = reader.read(filename_queue)\n",
"\n",
"x1, x2, target = tf.decode_csv(value, record_defaults=[[-1.], [-1.], [-1]])\n",
"features = tf.stack([x1, x2])\n",
"\n",
"instance_queue = tf.RandomShuffleQueue(\n",
" capacity=10, min_after_dequeue=2,\n",
" dtypes=[tf.float32, tf.int32], shapes=[[2],[]],\n",
" name=\"instance_q\", shared_name=\"shared_instance_q\")\n",
"enqueue_instance = instance_queue.enqueue([features, target])\n",
"close_instance_queue = instance_queue.close()\n",
"\n",
"minibatch_instances, minibatch_targets = instance_queue.dequeue_up_to(2)\n",
"\n",
"with tf.Session() as sess:\n",
" sess.run(enqueue_filename, feed_dict={filename: \"my_test.csv\"})\n",
" sess.run(close_filename_queue)\n",
" try:\n",
" while True:\n",
" sess.run(enqueue_instance)\n",
" except tf.errors.OutOfRangeError as ex:\n",
" print(\"No more files to read\")\n",
" sess.run(close_instance_queue)\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\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"#coord = tf.train.Coordinator()\n",
"#threads = tf.train.start_queue_runners(coord=coord)\n",
"#filename_queue = tf.train.string_input_producer([\"test.csv\"])\n",
"#coord.request_stop()\n",
"#coord.join(threads)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Queue runners and coordinators"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[array([[ 7., 8.],\n",
" [ 1., -1.]], dtype=float32), array([0, 0], dtype=int32)]\n",
"[array([[4., 5.]], dtype=float32), array([1], dtype=int32)]\n",
"No more training instances\n"
]
}
],
"source": [
"reset_graph()\n",
"\n",
"filename_queue = tf.FIFOQueue(capacity=10, dtypes=[tf.string], shapes=[()])\n",
"filename = tf.placeholder(tf.string)\n",
"enqueue_filename = filename_queue.enqueue([filename])\n",
"close_filename_queue = filename_queue.close()\n",
"\n",
"reader = tf.TextLineReader(skip_header_lines=1)\n",
"key, value = reader.read(filename_queue)\n",
"\n",
"x1, x2, target = tf.decode_csv(value, record_defaults=[[-1.], [-1.], [-1]])\n",
"features = tf.stack([x1, x2])\n",
"\n",
"instance_queue = tf.RandomShuffleQueue(\n",
" capacity=10, min_after_dequeue=2,\n",
" dtypes=[tf.float32, tf.int32], shapes=[[2],[]],\n",
" name=\"instance_q\", shared_name=\"shared_instance_q\")\n",
"enqueue_instance = instance_queue.enqueue([features, target])\n",
"close_instance_queue = instance_queue.close()\n",
"\n",
"minibatch_instances, minibatch_targets = instance_queue.dequeue_up_to(2)\n",
"\n",
"n_threads = 5\n",
"queue_runner = tf.train.QueueRunner(instance_queue, [enqueue_instance] * n_threads)\n",
"coord = tf.train.Coordinator()\n",
"\n",
"with tf.Session() as sess:\n",
" sess.run(enqueue_filename, feed_dict={filename: \"my_test.csv\"})\n",
" sess.run(close_filename_queue)\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\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[array([[ 4., 5.],\n",
" [ 1., -1.]], dtype=float32), array([1, 0], dtype=int32)]\n",
"[array([[7., 8.]], dtype=float32), array([0], dtype=int32)]\n",
"No more training instances\n"
]
}
],
"source": [
"reset_graph()\n",
"\n",
"def read_and_push_instance(filename_queue, instance_queue):\n",
" reader = tf.TextLineReader(skip_header_lines=1)\n",
" key, value = reader.read(filename_queue)\n",
" x1, x2, target = tf.decode_csv(value, record_defaults=[[-1.], [-1.], [-1]])\n",
" features = tf.stack([x1, x2])\n",
" enqueue_instance = instance_queue.enqueue([features, target])\n",
" return enqueue_instance\n",
"\n",
"filename_queue = tf.FIFOQueue(capacity=10, dtypes=[tf.string], shapes=[()])\n",
"filename = tf.placeholder(tf.string)\n",
"enqueue_filename = filename_queue.enqueue([filename])\n",
"close_filename_queue = filename_queue.close()\n",
"\n",
"instance_queue = tf.RandomShuffleQueue(\n",
" capacity=10, min_after_dequeue=2,\n",
" dtypes=[tf.float32, tf.int32], shapes=[[2],[]],\n",
" name=\"instance_q\", shared_name=\"shared_instance_q\")\n",
"\n",
"minibatch_instances, minibatch_targets = instance_queue.dequeue_up_to(2)\n",
"\n",
"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",
" 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": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.0\n",
"6.0\n",
"3.0\n",
"4.0\n",
"Timed out while dequeuing\n"
]
}
],
"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": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2 3 4 5 6]\n",
"[7 8 9 0 1 2 3]\n",
"[4 5 6 7 8 9 0]\n",
"[1 2 3 4 5 6 7]\n",
"[8 9]\n",
"Done\n"
]
}
],
"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": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[array([0, 1, 2, 3, 4, 5, 6]), array([0, 1, 2, 3, 4, 5, 6])]\n",
"[array([7, 8, 9, 0, 1, 2, 3]), array([7, 8, 9, 0, 1, 2, 3])]\n",
"[array([4, 5, 6, 7, 8, 9, 0]), array([4, 5, 6, 7, 8, 9, 0])]\n",
"[array([1, 2, 3, 4, 5, 6, 7]), array([1, 2, 3, 4, 5, 6, 7])]\n",
"[array([8, 9]), array([8, 9])]\n",
"Done\n"
]
}
],
"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": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0,1,7,8,4,5,2,3,9,0,6,7,4,5,1,2,8,9,6,3,0,1,2,8,9,3,4,5,6,7,Done\n"
]
}
],
"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": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1. -1.] 0.0\n",
"[4. 5.] 1.0\n",
"[7. 8.] 0.0\n",
"Done\n"
]
}
],
"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": []
}
],
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