1618024574
Tensorflow Object Detection in 5 Hours with Python | Full Course with 3 Projects
In this course, you’ll learn everything you need to know to go from beginner to practitioner when it comes to deep learning object detection with Tensorflow. This course mainly revolves around Python but there’s a little Javascript thrown in as well when it comes to building a web app in Project 2. But don’t fret we’ll take it step by step so you can take your time and work through it. All the code it made available through GitHub, links below.
As part of this course you’ll build four different object detection models:
A. Gesture Detection - this is the first project where you’ll be able to build a model that detects four different gestures
B. Microscope Based Defect Detection - here we’ll leverage a USB microscope to detect defects in LEDs and PCBs using TFOD and Python
C. Web Direction Detection - in this model you’ll learn how to detect hand directions for integration in a React Js Web App with Tensorflow Js
D. Face Sentiment Detection - here you’ll learn how to estimate facial sentiment using Tensorflow Object Detection on a Raspberry Pi with TFLite
You’ll learn how to:
- Install Tensorflow Object Detection on a Local Machine and on Colab
- Collect and Label images for Object Detection using LabelImg
- Train Deep Learning powered Object Detection Models using Python and TFOD
- Detect objects in real time using a webcam and using Images
- Tune Object Detection models to improve Precision and Recall
- Export your model to Tensorflow JS for integration in React JS web apps
- Export your model to TFLite for use on a Raspberry Pi
Chapters:
0:00 - Start
12:13 - SECTION 1: Installation and Setup
26:34 - Cloning the Baseline Code from GitHub
27:59 - Creating a Virtual Environment
39:57 - SECTION 2: Collecting Images and Labelling
44:48 - Collecting Images Using Your Webcam
1:04:11 - Labelling Images for Object Detection using LabelImg
1:29:08 - SECTION 3: Training Tensorflow Object Detection Models
1:34:04 - Tensorflow Model Zoo
1:39:04 - Installing Tensorflow Object Detection for Python
1:56:41 - Installing CUDA and cuDNN
2:06:42 - Using Tensorflow Model Zoo models
2:09:21 - Creating and Updating a Label Map
2:10:09 - Creating TF Records
2:17:23 - Training Tensorflow Object Detection Models for Python
2:27:48 - Evaluating OD Models (Precision and Recall)
2:29:08 - Evaluating OD Models using Tensorboard
2:34:07 - SECTION 4: Detecting Objects from Images and Webcams
2:34:52 - Detecting Objects in Images
2:38:57 - Detecting Objects in Real Time using a Webcam
2:41:56 - SECTION 5: Freezing TFOD and Converting to TFJS and TFLite
2:42:25 - Freezing the Tensorflow Graph
2:44:17 - Converting Object Detection Models to Tensorflow Js
2:45:27 - Converting Object Detection Models to TFLite
2:47:45 - SECTION 6: Performance Tuning to Improve Precision and Recall
3:12:34 - SECTION 7: Training Object Detection Models on Colab
3:24:05 - SECTION 8: Object Detection Projects with Python
3:25:25 - Project 1: Detecting Object Defects with a Microscope
3:57:34 - Project 2: Web Direction Detection using Tensorflow JS
4:47:40 - Project 3: Sentiment Detection on a Raspberry Pi Using TFLite
Get the code
Tensorflow Object Detection Python Course Code: https://github.com/nicknochnack/TFODCourse
Tensorflow Object Detection React App: https://github.com/nicknochnack/TFODApp
Tensorflow Object Detection for Raspberry Pi: https://github.com/nicknochnack/TFODRPi
Subscribe: https://www.youtube.com/channel/UCHXa4OpASJEwrHrLeIzw7Yg
#tensorflow #python
What is GEEK
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1648030144
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1653475560
A Pure PHP Implementation Of The MessagePack Serialization Format
msgpack.php
A pure PHP implementation of the MessagePack serialization format.
Features
- Fully compliant with the latest MessagePack specification
- Supports streaming unpacking
- Supports unsigned 64-bit integers handling
- Supports object serialization
- Fully tested
- Relatively fast
Installation
The recommended way to install the library is through Composer:
composer require rybakit/msgpack
Usage
Packing
To pack values you can either use an instance of a Packer:
$packer = new Packer();
$packed = $packer->pack($value);
or call a static method on the MessagePack class:
$packed = MessagePack::pack($value);
In the examples above, the method pack automatically packs a value depending on its type. However, not all PHP types can be uniquely translated to MessagePack types. For example, the MessagePack format defines map and array types, which are represented by a single array type in PHP. By default, the packer will pack a PHP array as a MessagePack array if it has sequential numeric keys, starting from 0 and as a MessagePack map otherwise:
$mpArr1 = $packer->pack([1, 2]); // MP array [1, 2]
$mpArr2 = $packer->pack([0 => 1, 1 => 2]); // MP array [1, 2]
$mpMap1 = $packer->pack([0 => 1, 2 => 3]); // MP map {0: 1, 2: 3}
$mpMap2 = $packer->pack([1 => 2, 2 => 3]); // MP map {1: 2, 2: 3}
$mpMap3 = $packer->pack(['a' => 1, 'b' => 2]); // MP map {a: 1, b: 2}
However, sometimes you need to pack a sequential array as a MessagePack map. To do this, use the packMap method:
$mpMap = $packer->packMap([1, 2]); // {0: 1, 1: 2}
Here is a list of type-specific packing methods:
$packer->packNil(); // MP nil
$packer->packBool(true); // MP bool
$packer->packInt(42); // MP int
$packer->packFloat(M_PI); // MP float (32 or 64)
$packer->packFloat32(M_PI); // MP float 32
$packer->packFloat64(M_PI); // MP float 64
$packer->packStr('foo'); // MP str
$packer->packBin("\x80"); // MP bin
$packer->packArray([1, 2]); // MP array
$packer->packMap(['a' => 1]); // MP map
$packer->packExt(1, "\xaa"); // MP ext
Check the "Custom types" section below on how to pack custom types.
Packing options
The Packer object supports a number of bitmask-based options for fine-tuning the packing process (defaults are in bold):
| Name | Description |
|---|---|
FORCE_STR | Forces PHP strings to be packed as MessagePack UTF-8 strings |
FORCE_BIN | Forces PHP strings to be packed as MessagePack binary data |
DETECT_STR_BIN | Detects MessagePack str/bin type automatically |
FORCE_ARR | Forces PHP arrays to be packed as MessagePack arrays |
FORCE_MAP | Forces PHP arrays to be packed as MessagePack maps |
DETECT_ARR_MAP | Detects MessagePack array/map type automatically |
FORCE_FLOAT32 | Forces PHP floats to be packed as 32-bits MessagePack floats |
FORCE_FLOAT64 | Forces PHP floats to be packed as 64-bits MessagePack floats |
The type detection mode (
DETECT_STR_BIN/DETECT_ARR_MAP) adds some overhead which can be noticed when you pack large (16- and 32-bit) arrays or strings. However, if you know the value type in advance (for example, you only work with UTF-8 strings or/and associative arrays), you can eliminate this overhead by forcing the packer to use the appropriate type, which will save it from running the auto-detection routine. Another option is to explicitly specify the value type. The library provides 2 auxiliary classes for this,MapandBin. Check the "Custom types" section below for details.
Examples:
// detect str/bin type and pack PHP 64-bit floats (doubles) to MP 32-bit floats
$packer = new Packer(PackOptions::DETECT_STR_BIN | PackOptions::FORCE_FLOAT32);
// these will throw MessagePack\Exception\InvalidOptionException
$packer = new Packer(PackOptions::FORCE_STR | PackOptions::FORCE_BIN);
$packer = new Packer(PackOptions::FORCE_FLOAT32 | PackOptions::FORCE_FLOAT64);
Unpacking
To unpack data you can either use an instance of a BufferUnpacker:
$unpacker = new BufferUnpacker();
$unpacker->reset($packed);
$value = $unpacker->unpack();
or call a static method on the MessagePack class:
$value = MessagePack::unpack($packed);
If the packed data is received in chunks (e.g. when reading from a stream), use the tryUnpack method, which attempts to unpack data and returns an array of unpacked messages (if any) instead of throwing an InsufficientDataException:
while ($chunk = ...) {
$unpacker->append($chunk);
if ($messages = $unpacker->tryUnpack()) {
return $messages;
}
}
If you want to unpack from a specific position in a buffer, use seek:
$unpacker->seek(42); // set position equal to 42 bytes
$unpacker->seek(-8); // set position to 8 bytes before the end of the buffer
To skip bytes from the current position, use skip:
$unpacker->skip(10); // set position to 10 bytes ahead of the current position
To get the number of remaining (unread) bytes in the buffer:
$unreadBytesCount = $unpacker->getRemainingCount();
To check whether the buffer has unread data:
$hasUnreadBytes = $unpacker->hasRemaining();
If needed, you can remove already read data from the buffer by calling:
$releasedBytesCount = $unpacker->release();
With the read method you can read raw (packed) data:
$packedData = $unpacker->read(2); // read 2 bytes
Besides the above methods BufferUnpacker provides type-specific unpacking methods, namely:
$unpacker->unpackNil(); // PHP null
$unpacker->unpackBool(); // PHP bool
$unpacker->unpackInt(); // PHP int
$unpacker->unpackFloat(); // PHP float
$unpacker->unpackStr(); // PHP UTF-8 string
$unpacker->unpackBin(); // PHP binary string
$unpacker->unpackArray(); // PHP sequential array
$unpacker->unpackMap(); // PHP associative array
$unpacker->unpackExt(); // PHP MessagePack\Type\Ext object
Unpacking options
The BufferUnpacker object supports a number of bitmask-based options for fine-tuning the unpacking process (defaults are in bold):
| Name | Description |
|---|---|
BIGINT_AS_STR | Converts overflowed integers to strings [1] |
BIGINT_AS_GMP | Converts overflowed integers to GMP objects [2] |
BIGINT_AS_DEC | Converts overflowed integers to Decimal\Decimal objects [3] |
1. The binary MessagePack format has unsigned 64-bit as its largest integer data type, but PHP does not support such integers, which means that an overflow can occur during unpacking.
2. Make sure the GMP extension is enabled.
3. Make sure the Decimal extension is enabled.
Examples:
$packedUint64 = "\xcf"."\xff\xff\xff\xff"."\xff\xff\xff\xff";
$unpacker = new BufferUnpacker($packedUint64);
var_dump($unpacker->unpack()); // string(20) "18446744073709551615"
$unpacker = new BufferUnpacker($packedUint64, UnpackOptions::BIGINT_AS_GMP);
var_dump($unpacker->unpack()); // object(GMP) {...}
$unpacker = new BufferUnpacker($packedUint64, UnpackOptions::BIGINT_AS_DEC);
var_dump($unpacker->unpack()); // object(Decimal\Decimal) {...}
Custom types
In addition to the basic types, the library provides functionality to serialize and deserialize arbitrary types. This can be done in several ways, depending on your use case. Let's take a look at them.
Type objects
If you need to serialize an instance of one of your classes into one of the basic MessagePack types, the best way to do this is to implement the CanBePacked interface in the class. A good example of such a class is the Map type class that comes with the library. This type is useful when you want to explicitly specify that a given PHP array should be packed as a MessagePack map without triggering an automatic type detection routine:
$packer = new Packer();
$packedMap = $packer->pack(new Map([1, 2, 3]));
$packedArray = $packer->pack([1, 2, 3]);
More type examples can be found in the src/Type directory.
Type transformers
As with type objects, type transformers are only responsible for serializing values. They should be used when you need to serialize a value that does not implement the CanBePacked interface. Examples of such values could be instances of built-in or third-party classes that you don't own, or non-objects such as resources.
A transformer class must implement the CanPack interface. To use a transformer, it must first be registered in the packer. Here is an example of how to serialize PHP streams into the MessagePack bin format type using one of the supplied transformers, StreamTransformer:
$packer = new Packer(null, [new StreamTransformer()]);
$packedBin = $packer->pack(fopen('/path/to/file', 'r+'));
More type transformer examples can be found in the src/TypeTransformer directory.
Extensions
In contrast to the cases described above, extensions are intended to handle extension types and are responsible for both serialization and deserialization of values (types).
An extension class must implement the Extension interface. To use an extension, it must first be registered in the packer and the unpacker.
The MessagePack specification divides extension types into two groups: predefined and application-specific. Currently, there is only one predefined type in the specification, Timestamp.
Timestamp
The Timestamp extension type is a predefined type. Support for this type in the library is done through the TimestampExtension class. This class is responsible for handling Timestamp objects, which represent the number of seconds and optional adjustment in nanoseconds:
$timestampExtension = new TimestampExtension();
$packer = new Packer();
$packer = $packer->extendWith($timestampExtension);
$unpacker = new BufferUnpacker();
$unpacker = $unpacker->extendWith($timestampExtension);
$packedTimestamp = $packer->pack(Timestamp::now());
$timestamp = $unpacker->reset($packedTimestamp)->unpack();
$seconds = $timestamp->getSeconds();
$nanoseconds = $timestamp->getNanoseconds();
When using the MessagePack class, the Timestamp extension is already registered:
$packedTimestamp = MessagePack::pack(Timestamp::now());
$timestamp = MessagePack::unpack($packedTimestamp);
Application-specific extensions
In addition, the format can be extended with your own types. For example, to make the built-in PHP DateTime objects first-class citizens in your code, you can create a corresponding extension, as shown in the example. Please note, that custom extensions have to be registered with a unique extension ID (an integer from 0 to 127).
More extension examples can be found in the examples/MessagePack directory.
To learn more about how extension types can be useful, check out this article.
Exceptions
If an error occurs during packing/unpacking, a PackingFailedException or an UnpackingFailedException will be thrown, respectively. In addition, an InsufficientDataException can be thrown during unpacking.
An InvalidOptionException will be thrown in case an invalid option (or a combination of mutually exclusive options) is used.
Tests
Run tests as follows:
vendor/bin/phpunit
Also, if you already have Docker installed, you can run the tests in a docker container. First, create a container:
./dockerfile.sh | docker build -t msgpack -
The command above will create a container named msgpack with PHP 8.1 runtime. You may change the default runtime by defining the PHP_IMAGE environment variable:
PHP_IMAGE='php:8.0-cli' ./dockerfile.sh | docker build -t msgpack -
See a list of various images here.
Then run the unit tests:
docker run --rm -v $PWD:/msgpack -w /msgpack msgpack
Fuzzing
To ensure that the unpacking works correctly with malformed/semi-malformed data, you can use a testing technique called Fuzzing. The library ships with a help file (target) for PHP-Fuzzer and can be used as follows:
php-fuzzer fuzz tests/fuzz_buffer_unpacker.php
Performance
To check performance, run:
php -n -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php
Example output
Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000
=============================================
Test/Target Packer BufferUnpacker
---------------------------------------------
nil .................. 0.0030 ........ 0.0139
false ................ 0.0037 ........ 0.0144
true ................. 0.0040 ........ 0.0137
7-bit uint #1 ........ 0.0052 ........ 0.0120
7-bit uint #2 ........ 0.0059 ........ 0.0114
7-bit uint #3 ........ 0.0061 ........ 0.0119
5-bit sint #1 ........ 0.0067 ........ 0.0126
5-bit sint #2 ........ 0.0064 ........ 0.0132
5-bit sint #3 ........ 0.0066 ........ 0.0135
8-bit uint #1 ........ 0.0078 ........ 0.0200
8-bit uint #2 ........ 0.0077 ........ 0.0212
8-bit uint #3 ........ 0.0086 ........ 0.0203
16-bit uint #1 ....... 0.0111 ........ 0.0271
16-bit uint #2 ....... 0.0115 ........ 0.0260
16-bit uint #3 ....... 0.0103 ........ 0.0273
32-bit uint #1 ....... 0.0116 ........ 0.0326
32-bit uint #2 ....... 0.0118 ........ 0.0332
32-bit uint #3 ....... 0.0127 ........ 0.0325
64-bit uint #1 ....... 0.0140 ........ 0.0277
64-bit uint #2 ....... 0.0134 ........ 0.0294
64-bit uint #3 ....... 0.0134 ........ 0.0281
8-bit int #1 ......... 0.0086 ........ 0.0241
8-bit int #2 ......... 0.0089 ........ 0.0225
8-bit int #3 ......... 0.0085 ........ 0.0229
16-bit int #1 ........ 0.0118 ........ 0.0280
16-bit int #2 ........ 0.0121 ........ 0.0270
16-bit int #3 ........ 0.0109 ........ 0.0274
32-bit int #1 ........ 0.0128 ........ 0.0346
32-bit int #2 ........ 0.0118 ........ 0.0339
32-bit int #3 ........ 0.0135 ........ 0.0368
64-bit int #1 ........ 0.0138 ........ 0.0276
64-bit int #2 ........ 0.0132 ........ 0.0286
64-bit int #3 ........ 0.0137 ........ 0.0274
64-bit int #4 ........ 0.0180 ........ 0.0285
64-bit float #1 ...... 0.0134 ........ 0.0284
64-bit float #2 ...... 0.0125 ........ 0.0275
64-bit float #3 ...... 0.0126 ........ 0.0283
fix string #1 ........ 0.0035 ........ 0.0133
fix string #2 ........ 0.0094 ........ 0.0216
fix string #3 ........ 0.0094 ........ 0.0222
fix string #4 ........ 0.0091 ........ 0.0241
8-bit string #1 ...... 0.0122 ........ 0.0301
8-bit string #2 ...... 0.0118 ........ 0.0304
8-bit string #3 ...... 0.0119 ........ 0.0315
16-bit string #1 ..... 0.0150 ........ 0.0388
16-bit string #2 ..... 0.1545 ........ 0.1665
32-bit string ........ 0.1570 ........ 0.1756
wide char string #1 .. 0.0091 ........ 0.0236
wide char string #2 .. 0.0122 ........ 0.0313
8-bit binary #1 ...... 0.0100 ........ 0.0302
8-bit binary #2 ...... 0.0123 ........ 0.0324
8-bit binary #3 ...... 0.0126 ........ 0.0327
16-bit binary ........ 0.0168 ........ 0.0372
32-bit binary ........ 0.1588 ........ 0.1754
fix array #1 ......... 0.0042 ........ 0.0131
fix array #2 ......... 0.0294 ........ 0.0367
fix array #3 ......... 0.0412 ........ 0.0472
16-bit array #1 ...... 0.1378 ........ 0.1596
16-bit array #2 ........... S ............. S
32-bit array .............. S ............. S
complex array ........ 0.1865 ........ 0.2283
fix map #1 ........... 0.0725 ........ 0.1048
fix map #2 ........... 0.0319 ........ 0.0405
fix map #3 ........... 0.0356 ........ 0.0665
fix map #4 ........... 0.0465 ........ 0.0497
16-bit map #1 ........ 0.2540 ........ 0.3028
16-bit map #2 ............. S ............. S
32-bit map ................ S ............. S
complex map .......... 0.2372 ........ 0.2710
fixext 1 ............. 0.0283 ........ 0.0358
fixext 2 ............. 0.0291 ........ 0.0371
fixext 4 ............. 0.0302 ........ 0.0355
fixext 8 ............. 0.0288 ........ 0.0384
fixext 16 ............ 0.0293 ........ 0.0359
8-bit ext ............ 0.0302 ........ 0.0439
16-bit ext ........... 0.0334 ........ 0.0499
32-bit ext ........... 0.1845 ........ 0.1888
32-bit timestamp #1 .. 0.0337 ........ 0.0547
32-bit timestamp #2 .. 0.0335 ........ 0.0560
64-bit timestamp #1 .. 0.0371 ........ 0.0575
64-bit timestamp #2 .. 0.0374 ........ 0.0542
64-bit timestamp #3 .. 0.0356 ........ 0.0533
96-bit timestamp #1 .. 0.0362 ........ 0.0699
96-bit timestamp #2 .. 0.0381 ........ 0.0701
96-bit timestamp #3 .. 0.0367 ........ 0.0687
=============================================
Total 2.7618 4.0820
Skipped 4 4
Failed 0 0
Ignored 0 0
With JIT:
php -n -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.jit_buffer_size=64M -dopcache.jit=tracing -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php
Example output
Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000
=============================================
Test/Target Packer BufferUnpacker
---------------------------------------------
nil .................. 0.0005 ........ 0.0054
false ................ 0.0004 ........ 0.0059
true ................. 0.0004 ........ 0.0059
7-bit uint #1 ........ 0.0010 ........ 0.0047
7-bit uint #2 ........ 0.0010 ........ 0.0046
7-bit uint #3 ........ 0.0010 ........ 0.0046
5-bit sint #1 ........ 0.0025 ........ 0.0046
5-bit sint #2 ........ 0.0023 ........ 0.0046
5-bit sint #3 ........ 0.0024 ........ 0.0045
8-bit uint #1 ........ 0.0043 ........ 0.0081
8-bit uint #2 ........ 0.0043 ........ 0.0079
8-bit uint #3 ........ 0.0041 ........ 0.0080
16-bit uint #1 ....... 0.0064 ........ 0.0095
16-bit uint #2 ....... 0.0064 ........ 0.0091
16-bit uint #3 ....... 0.0064 ........ 0.0094
32-bit uint #1 ....... 0.0085 ........ 0.0114
32-bit uint #2 ....... 0.0077 ........ 0.0122
32-bit uint #3 ....... 0.0077 ........ 0.0120
64-bit uint #1 ....... 0.0085 ........ 0.0159
64-bit uint #2 ....... 0.0086 ........ 0.0157
64-bit uint #3 ....... 0.0086 ........ 0.0158
8-bit int #1 ......... 0.0042 ........ 0.0080
8-bit int #2 ......... 0.0042 ........ 0.0080
8-bit int #3 ......... 0.0042 ........ 0.0081
16-bit int #1 ........ 0.0065 ........ 0.0095
16-bit int #2 ........ 0.0065 ........ 0.0090
16-bit int #3 ........ 0.0056 ........ 0.0085
32-bit int #1 ........ 0.0067 ........ 0.0107
32-bit int #2 ........ 0.0066 ........ 0.0106
32-bit int #3 ........ 0.0063 ........ 0.0104
64-bit int #1 ........ 0.0072 ........ 0.0162
64-bit int #2 ........ 0.0073 ........ 0.0174
64-bit int #3 ........ 0.0072 ........ 0.0164
64-bit int #4 ........ 0.0077 ........ 0.0161
64-bit float #1 ...... 0.0053 ........ 0.0135
64-bit float #2 ...... 0.0053 ........ 0.0135
64-bit float #3 ...... 0.0052 ........ 0.0135
fix string #1 ....... -0.0002 ........ 0.0044
fix string #2 ........ 0.0035 ........ 0.0067
fix string #3 ........ 0.0035 ........ 0.0077
fix string #4 ........ 0.0033 ........ 0.0078
8-bit string #1 ...... 0.0059 ........ 0.0110
8-bit string #2 ...... 0.0063 ........ 0.0121
8-bit string #3 ...... 0.0064 ........ 0.0124
16-bit string #1 ..... 0.0099 ........ 0.0146
16-bit string #2 ..... 0.1522 ........ 0.1474
32-bit string ........ 0.1511 ........ 0.1483
wide char string #1 .. 0.0039 ........ 0.0084
wide char string #2 .. 0.0073 ........ 0.0123
8-bit binary #1 ...... 0.0040 ........ 0.0112
8-bit binary #2 ...... 0.0075 ........ 0.0123
8-bit binary #3 ...... 0.0077 ........ 0.0129
16-bit binary ........ 0.0096 ........ 0.0145
32-bit binary ........ 0.1535 ........ 0.1479
fix array #1 ......... 0.0008 ........ 0.0061
fix array #2 ......... 0.0121 ........ 0.0165
fix array #3 ......... 0.0193 ........ 0.0222
16-bit array #1 ...... 0.0607 ........ 0.0479
16-bit array #2 ........... S ............. S
32-bit array .............. S ............. S
complex array ........ 0.0749 ........ 0.0824
fix map #1 ........... 0.0329 ........ 0.0431
fix map #2 ........... 0.0161 ........ 0.0189
fix map #3 ........... 0.0205 ........ 0.0262
fix map #4 ........... 0.0252 ........ 0.0205
16-bit map #1 ........ 0.1016 ........ 0.0927
16-bit map #2 ............. S ............. S
32-bit map ................ S ............. S
complex map .......... 0.1096 ........ 0.1030
fixext 1 ............. 0.0157 ........ 0.0161
fixext 2 ............. 0.0175 ........ 0.0183
fixext 4 ............. 0.0156 ........ 0.0185
fixext 8 ............. 0.0163 ........ 0.0184
fixext 16 ............ 0.0164 ........ 0.0182
8-bit ext ............ 0.0158 ........ 0.0207
16-bit ext ........... 0.0203 ........ 0.0219
32-bit ext ........... 0.1614 ........ 0.1539
32-bit timestamp #1 .. 0.0195 ........ 0.0249
32-bit timestamp #2 .. 0.0188 ........ 0.0260
64-bit timestamp #1 .. 0.0207 ........ 0.0281
64-bit timestamp #2 .. 0.0212 ........ 0.0291
64-bit timestamp #3 .. 0.0207 ........ 0.0295
96-bit timestamp #1 .. 0.0222 ........ 0.0358
96-bit timestamp #2 .. 0.0228 ........ 0.0353
96-bit timestamp #3 .. 0.0210 ........ 0.0319
=============================================
Total 1.6432 1.9674
Skipped 4 4
Failed 0 0
Ignored 0 0
You may change default benchmark settings by defining the following environment variables:
| Name | Default |
|---|---|
| MP_BENCH_TARGETS | pure_p,pure_u, see a list of available targets |
| MP_BENCH_ITERATIONS | 100_000 |
| MP_BENCH_DURATION | not set |
| MP_BENCH_ROUNDS | 3 |
| MP_BENCH_TESTS | -@slow, see a list of available tests |
For example:
export MP_BENCH_TARGETS=pure_p
export MP_BENCH_ITERATIONS=1000000
export MP_BENCH_ROUNDS=5
# a comma separated list of test names
export MP_BENCH_TESTS='complex array, complex map'
# or a group name
# export MP_BENCH_TESTS='-@slow' // @pecl_comp
# or a regexp
# export MP_BENCH_TESTS='/complex (array|map)/'
Another example, benchmarking both the library and the PECL extension:
MP_BENCH_TARGETS=pure_p,pure_u,pecl_p,pecl_u \
php -n -dextension=msgpack.so -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php
Example output
Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000
===========================================================================
Test/Target Packer BufferUnpacker msgpack_pack msgpack_unpack
---------------------------------------------------------------------------
nil .................. 0.0031 ........ 0.0141 ...... 0.0055 ........ 0.0064
false ................ 0.0039 ........ 0.0154 ...... 0.0056 ........ 0.0053
true ................. 0.0038 ........ 0.0139 ...... 0.0056 ........ 0.0044
7-bit uint #1 ........ 0.0061 ........ 0.0110 ...... 0.0059 ........ 0.0046
7-bit uint #2 ........ 0.0065 ........ 0.0119 ...... 0.0042 ........ 0.0029
7-bit uint #3 ........ 0.0054 ........ 0.0117 ...... 0.0045 ........ 0.0025
5-bit sint #1 ........ 0.0047 ........ 0.0103 ...... 0.0038 ........ 0.0022
5-bit sint #2 ........ 0.0048 ........ 0.0117 ...... 0.0038 ........ 0.0022
5-bit sint #3 ........ 0.0046 ........ 0.0102 ...... 0.0038 ........ 0.0023
8-bit uint #1 ........ 0.0063 ........ 0.0174 ...... 0.0039 ........ 0.0031
8-bit uint #2 ........ 0.0063 ........ 0.0167 ...... 0.0040 ........ 0.0029
8-bit uint #3 ........ 0.0063 ........ 0.0168 ...... 0.0039 ........ 0.0030
16-bit uint #1 ....... 0.0092 ........ 0.0222 ...... 0.0049 ........ 0.0030
16-bit uint #2 ....... 0.0096 ........ 0.0227 ...... 0.0042 ........ 0.0046
16-bit uint #3 ....... 0.0123 ........ 0.0274 ...... 0.0059 ........ 0.0051
32-bit uint #1 ....... 0.0136 ........ 0.0331 ...... 0.0060 ........ 0.0048
32-bit uint #2 ....... 0.0130 ........ 0.0336 ...... 0.0070 ........ 0.0048
32-bit uint #3 ....... 0.0127 ........ 0.0329 ...... 0.0051 ........ 0.0048
64-bit uint #1 ....... 0.0126 ........ 0.0268 ...... 0.0055 ........ 0.0049
64-bit uint #2 ....... 0.0135 ........ 0.0281 ...... 0.0052 ........ 0.0046
64-bit uint #3 ....... 0.0131 ........ 0.0274 ...... 0.0069 ........ 0.0044
8-bit int #1 ......... 0.0077 ........ 0.0236 ...... 0.0058 ........ 0.0044
8-bit int #2 ......... 0.0087 ........ 0.0244 ...... 0.0058 ........ 0.0048
8-bit int #3 ......... 0.0084 ........ 0.0241 ...... 0.0055 ........ 0.0049
16-bit int #1 ........ 0.0112 ........ 0.0271 ...... 0.0048 ........ 0.0045
16-bit int #2 ........ 0.0124 ........ 0.0292 ...... 0.0057 ........ 0.0049
16-bit int #3 ........ 0.0118 ........ 0.0270 ...... 0.0058 ........ 0.0050
32-bit int #1 ........ 0.0137 ........ 0.0366 ...... 0.0058 ........ 0.0051
32-bit int #2 ........ 0.0133 ........ 0.0366 ...... 0.0056 ........ 0.0049
32-bit int #3 ........ 0.0129 ........ 0.0350 ...... 0.0052 ........ 0.0048
64-bit int #1 ........ 0.0145 ........ 0.0254 ...... 0.0034 ........ 0.0025
64-bit int #2 ........ 0.0097 ........ 0.0214 ...... 0.0034 ........ 0.0025
64-bit int #3 ........ 0.0096 ........ 0.0287 ...... 0.0059 ........ 0.0050
64-bit int #4 ........ 0.0143 ........ 0.0277 ...... 0.0059 ........ 0.0046
64-bit float #1 ...... 0.0134 ........ 0.0281 ...... 0.0057 ........ 0.0052
64-bit float #2 ...... 0.0141 ........ 0.0281 ...... 0.0057 ........ 0.0050
64-bit float #3 ...... 0.0144 ........ 0.0282 ...... 0.0057 ........ 0.0050
fix string #1 ........ 0.0036 ........ 0.0143 ...... 0.0066 ........ 0.0053
fix string #2 ........ 0.0107 ........ 0.0222 ...... 0.0065 ........ 0.0068
fix string #3 ........ 0.0116 ........ 0.0245 ...... 0.0063 ........ 0.0069
fix string #4 ........ 0.0105 ........ 0.0253 ...... 0.0083 ........ 0.0077
8-bit string #1 ...... 0.0126 ........ 0.0318 ...... 0.0075 ........ 0.0088
8-bit string #2 ...... 0.0121 ........ 0.0295 ...... 0.0076 ........ 0.0086
8-bit string #3 ...... 0.0125 ........ 0.0293 ...... 0.0130 ........ 0.0093
16-bit string #1 ..... 0.0159 ........ 0.0368 ...... 0.0117 ........ 0.0086
16-bit string #2 ..... 0.1547 ........ 0.1686 ...... 0.1516 ........ 0.1373
32-bit string ........ 0.1558 ........ 0.1729 ...... 0.1511 ........ 0.1396
wide char string #1 .. 0.0098 ........ 0.0237 ...... 0.0066 ........ 0.0065
wide char string #2 .. 0.0128 ........ 0.0291 ...... 0.0061 ........ 0.0082
8-bit binary #1 ........... I ............. I ........... F ............. I
8-bit binary #2 ........... I ............. I ........... F ............. I
8-bit binary #3 ........... I ............. I ........... F ............. I
16-bit binary ............. I ............. I ........... F ............. I
32-bit binary ............. I ............. I ........... F ............. I
fix array #1 ......... 0.0040 ........ 0.0129 ...... 0.0120 ........ 0.0058
fix array #2 ......... 0.0279 ........ 0.0390 ...... 0.0143 ........ 0.0165
fix array #3 ......... 0.0415 ........ 0.0463 ...... 0.0162 ........ 0.0187
16-bit array #1 ...... 0.1349 ........ 0.1628 ...... 0.0334 ........ 0.0341
16-bit array #2 ........... S ............. S ........... S ............. S
32-bit array .............. S ............. S ........... S ............. S
complex array ............. I ............. I ........... F ............. F
fix map #1 ................ I ............. I ........... F ............. I
fix map #2 ........... 0.0345 ........ 0.0391 ...... 0.0143 ........ 0.0168
fix map #3 ................ I ............. I ........... F ............. I
fix map #4 ........... 0.0459 ........ 0.0473 ...... 0.0151 ........ 0.0163
16-bit map #1 ........ 0.2518 ........ 0.2962 ...... 0.0400 ........ 0.0490
16-bit map #2 ............. S ............. S ........... S ............. S
32-bit map ................ S ............. S ........... S ............. S
complex map .......... 0.2380 ........ 0.2682 ...... 0.0545 ........ 0.0579
fixext 1 .................. I ............. I ........... F ............. F
fixext 2 .................. I ............. I ........... F ............. F
fixext 4 .................. I ............. I ........... F ............. F
fixext 8 .................. I ............. I ........... F ............. F
fixext 16 ................. I ............. I ........... F ............. F
8-bit ext ................. I ............. I ........... F ............. F
16-bit ext ................ I ............. I ........... F ............. F
32-bit ext ................ I ............. I ........... F ............. F
32-bit timestamp #1 ....... I ............. I ........... F ............. F
32-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #1 ....... I ............. I ........... F ............. F
64-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #3 ....... I ............. I ........... F ............. F
96-bit timestamp #1 ....... I ............. I ........... F ............. F
96-bit timestamp #2 ....... I ............. I ........... F ............. F
96-bit timestamp #3 ....... I ............. I ........... F ............. F
===========================================================================
Total 1.5625 2.3866 0.7735 0.7243
Skipped 4 4 4 4
Failed 0 0 24 17
Ignored 24 24 0 7
With JIT:
MP_BENCH_TARGETS=pure_p,pure_u,pecl_p,pecl_u \
php -n -dextension=msgpack.so -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.jit_buffer_size=64M -dopcache.jit=tracing -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php
Example output
Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000
===========================================================================
Test/Target Packer BufferUnpacker msgpack_pack msgpack_unpack
---------------------------------------------------------------------------
nil .................. 0.0001 ........ 0.0052 ...... 0.0053 ........ 0.0042
false ................ 0.0007 ........ 0.0060 ...... 0.0057 ........ 0.0043
true ................. 0.0008 ........ 0.0060 ...... 0.0056 ........ 0.0041
7-bit uint #1 ........ 0.0031 ........ 0.0046 ...... 0.0062 ........ 0.0041
7-bit uint #2 ........ 0.0021 ........ 0.0043 ...... 0.0062 ........ 0.0041
7-bit uint #3 ........ 0.0022 ........ 0.0044 ...... 0.0061 ........ 0.0040
5-bit sint #1 ........ 0.0030 ........ 0.0048 ...... 0.0062 ........ 0.0040
5-bit sint #2 ........ 0.0032 ........ 0.0046 ...... 0.0062 ........ 0.0040
5-bit sint #3 ........ 0.0031 ........ 0.0046 ...... 0.0062 ........ 0.0040
8-bit uint #1 ........ 0.0054 ........ 0.0079 ...... 0.0062 ........ 0.0050
8-bit uint #2 ........ 0.0051 ........ 0.0079 ...... 0.0064 ........ 0.0044
8-bit uint #3 ........ 0.0051 ........ 0.0082 ...... 0.0062 ........ 0.0044
16-bit uint #1 ....... 0.0077 ........ 0.0094 ...... 0.0065 ........ 0.0045
16-bit uint #2 ....... 0.0077 ........ 0.0094 ...... 0.0063 ........ 0.0045
16-bit uint #3 ....... 0.0077 ........ 0.0095 ...... 0.0064 ........ 0.0047
32-bit uint #1 ....... 0.0088 ........ 0.0119 ...... 0.0063 ........ 0.0043
32-bit uint #2 ....... 0.0089 ........ 0.0117 ...... 0.0062 ........ 0.0039
32-bit uint #3 ....... 0.0089 ........ 0.0118 ...... 0.0063 ........ 0.0044
64-bit uint #1 ....... 0.0097 ........ 0.0155 ...... 0.0063 ........ 0.0045
64-bit uint #2 ....... 0.0095 ........ 0.0153 ...... 0.0061 ........ 0.0045
64-bit uint #3 ....... 0.0096 ........ 0.0156 ...... 0.0063 ........ 0.0047
8-bit int #1 ......... 0.0053 ........ 0.0083 ...... 0.0062 ........ 0.0044
8-bit int #2 ......... 0.0052 ........ 0.0080 ...... 0.0062 ........ 0.0044
8-bit int #3 ......... 0.0052 ........ 0.0080 ...... 0.0062 ........ 0.0043
16-bit int #1 ........ 0.0089 ........ 0.0097 ...... 0.0069 ........ 0.0046
16-bit int #2 ........ 0.0075 ........ 0.0093 ...... 0.0063 ........ 0.0043
16-bit int #3 ........ 0.0075 ........ 0.0094 ...... 0.0062 ........ 0.0046
32-bit int #1 ........ 0.0086 ........ 0.0122 ...... 0.0063 ........ 0.0044
32-bit int #2 ........ 0.0087 ........ 0.0120 ...... 0.0066 ........ 0.0046
32-bit int #3 ........ 0.0086 ........ 0.0121 ...... 0.0060 ........ 0.0044
64-bit int #1 ........ 0.0096 ........ 0.0149 ...... 0.0060 ........ 0.0045
64-bit int #2 ........ 0.0096 ........ 0.0157 ...... 0.0062 ........ 0.0044
64-bit int #3 ........ 0.0096 ........ 0.0160 ...... 0.0063 ........ 0.0046
64-bit int #4 ........ 0.0097 ........ 0.0157 ...... 0.0061 ........ 0.0044
64-bit float #1 ...... 0.0079 ........ 0.0153 ...... 0.0056 ........ 0.0044
64-bit float #2 ...... 0.0079 ........ 0.0152 ...... 0.0057 ........ 0.0045
64-bit float #3 ...... 0.0079 ........ 0.0155 ...... 0.0057 ........ 0.0044
fix string #1 ........ 0.0010 ........ 0.0045 ...... 0.0071 ........ 0.0044
fix string #2 ........ 0.0048 ........ 0.0075 ...... 0.0070 ........ 0.0060
fix string #3 ........ 0.0048 ........ 0.0086 ...... 0.0068 ........ 0.0060
fix string #4 ........ 0.0050 ........ 0.0088 ...... 0.0070 ........ 0.0059
8-bit string #1 ...... 0.0081 ........ 0.0129 ...... 0.0069 ........ 0.0062
8-bit string #2 ...... 0.0086 ........ 0.0128 ...... 0.0069 ........ 0.0065
8-bit string #3 ...... 0.0086 ........ 0.0126 ...... 0.0115 ........ 0.0065
16-bit string #1 ..... 0.0105 ........ 0.0137 ...... 0.0128 ........ 0.0068
16-bit string #2 ..... 0.1510 ........ 0.1486 ...... 0.1526 ........ 0.1391
32-bit string ........ 0.1517 ........ 0.1475 ...... 0.1504 ........ 0.1370
wide char string #1 .. 0.0044 ........ 0.0085 ...... 0.0067 ........ 0.0057
wide char string #2 .. 0.0081 ........ 0.0125 ...... 0.0069 ........ 0.0063
8-bit binary #1 ........... I ............. I ........... F ............. I
8-bit binary #2 ........... I ............. I ........... F ............. I
8-bit binary #3 ........... I ............. I ........... F ............. I
16-bit binary ............. I ............. I ........... F ............. I
32-bit binary ............. I ............. I ........... F ............. I
fix array #1 ......... 0.0014 ........ 0.0059 ...... 0.0132 ........ 0.0055
fix array #2 ......... 0.0146 ........ 0.0156 ...... 0.0155 ........ 0.0148
fix array #3 ......... 0.0211 ........ 0.0229 ...... 0.0179 ........ 0.0180
16-bit array #1 ...... 0.0673 ........ 0.0498 ...... 0.0343 ........ 0.0388
16-bit array #2 ........... S ............. S ........... S ............. S
32-bit array .............. S ............. S ........... S ............. S
complex array ............. I ............. I ........... F ............. F
fix map #1 ................ I ............. I ........... F ............. I
fix map #2 ........... 0.0148 ........ 0.0180 ...... 0.0156 ........ 0.0179
fix map #3 ................ I ............. I ........... F ............. I
fix map #4 ........... 0.0252 ........ 0.0201 ...... 0.0214 ........ 0.0167
16-bit map #1 ........ 0.1027 ........ 0.0836 ...... 0.0388 ........ 0.0510
16-bit map #2 ............. S ............. S ........... S ............. S
32-bit map ................ S ............. S ........... S ............. S
complex map .......... 0.1104 ........ 0.1010 ...... 0.0556 ........ 0.0602
fixext 1 .................. I ............. I ........... F ............. F
fixext 2 .................. I ............. I ........... F ............. F
fixext 4 .................. I ............. I ........... F ............. F
fixext 8 .................. I ............. I ........... F ............. F
fixext 16 ................. I ............. I ........... F ............. F
8-bit ext ................. I ............. I ........... F ............. F
16-bit ext ................ I ............. I ........... F ............. F
32-bit ext ................ I ............. I ........... F ............. F
32-bit timestamp #1 ....... I ............. I ........... F ............. F
32-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #1 ....... I ............. I ........... F ............. F
64-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #3 ....... I ............. I ........... F ............. F
96-bit timestamp #1 ....... I ............. I ........... F ............. F
96-bit timestamp #2 ....... I ............. I ........... F ............. F
96-bit timestamp #3 ....... I ............. I ........... F ............. F
===========================================================================
Total 0.9642 1.0909 0.8224 0.7213
Skipped 4 4 4 4
Failed 0 0 24 17
Ignored 24 24 0 7
Note that the msgpack extension (v2.1.2) doesn't support ext, bin and UTF-8 str types.
License
The library is released under the MIT License. See the bundled LICENSE file for details.
Author: rybakit
Source Code: https://github.com/rybakit/msgpack.php
License: MIT License
1650636000
Dl4clj: Clojure Wrapper for Deeplearning4j.
dl4clj
Port of deeplearning4j to clojure
Contact info
If you have any questions,
- my email is will@yetanalytics.com
- I'm will_hoyt in the clojurians slack
- twitter is @FeLungz (don't check very often)
TODO
- update examples dir
- finish README
- add in examples using Transfer Learning
- finish tests
- eval is missing regression tests, roc tests
- nn-test is missing regression tests
- spark tests need to be redone
- need dl4clj.core tests
- revist spark for updates
- write specs for user facing functions
- this is very important, match isnt strict for maps
- provides 100% certianty of the input -> output flow
- check the args as they come in, dispatch once I know its safe, test the pure output
- collapse overlapping api namespaces
- add to core use case flows
Features
Stable Features with tests
- Neural Networks DSL
- Early Stopping Training
- Transfer Learning
- Evaluation
- Data import
Features being worked on for 0.1.0
- Clustering (testing in progress)
- Spark (currently being refactored)
- Front End (maybe current release, maybe future release. Not sure yet)
- Version of dl4j is 0.0.8 in this project. Current dl4j version is 0.0.9
- Parallelism
- Kafka support
- Other items mentioned in TODO
Features being worked on for future releases
- NLP
- Computational Graphs
- Reinforement Learning
- Arbiter
Artifacts
NOT YET RELEASED TO CLOJARS
- fork or clone to try it out
If using Maven add the following repository definition to your pom.xml:
<repository>
<id>clojars.org</id>
<url>http://clojars.org/repo</url>
</repository>
Latest release
With Leiningen:
n/a
With Maven:
n/a
<dependency>
<groupId>_</groupId>
<artifactId>_</artifactId>
<version>_</version>
</dependency>
Usage
Things you need to know
All functions for creating dl4j objects return code by default
- All of these functions have an option to return the dl4j object
- :as-code? = false
- This because all builders require the code representation of dl4j objects
- this requirement is not going to change
- INDarray creation fns default to objects, this is for convenience
- :as-code? is still respected
API functions return code when all args are provided as code
API functions return the value of calling the wrapped method when args are provided as a mixture of objects and code or just objects
The tests are there to help clarify behavior, if you are unsure of how to use a fn, search the tests
- for questions about spark, refer to the spark section bellow
Example of obj/code duality
(ns my.ns
(:require [dl4clj.nn.conf.builders.layers :as l]))
;; as code (the default)
(l/dense-layer-builder
:activation-fn :relu
:learning-rate 0.006
:weight-init :xavier
:layer-name "example layer"
:n-in 10
:n-out 1)
;; =>
(doto
(org.deeplearning4j.nn.conf.layers.DenseLayer$Builder.)
(.nOut 1)
(.activation (dl4clj.constants/value-of {:activation-fn :relu}))
(.weightInit (dl4clj.constants/value-of {:weight-init :xavier}))
(.nIn 10)
(.name "example layer")
(.learningRate 0.006))
;; as an object
(l/dense-layer-builder
:activation-fn :relu
:learning-rate 0.006
:weight-init :xavier
:layer-name "example layer"
:n-in 10
:n-out 1
:as-code? false)
;; =>
#object[org.deeplearning4j.nn.conf.layers.DenseLayer 0x69d7d160 "DenseLayer(super=FeedForwardLayer(super=Layer(layerName=example layer, activationFn=relu, weightInit=XAVIER, biasInit=NaN, dist=null, learningRate=0.006, biasLearningRate=NaN, learningRateSchedule=null, momentum=NaN, momentumSchedule=null, l1=NaN, l2=NaN, l1Bias=NaN, l2Bias=NaN, dropOut=NaN, updater=null, rho=NaN, epsilon=NaN, rmsDecay=NaN, adamMeanDecay=NaN, adamVarDecay=NaN, gradientNormalization=null, gradientNormalizationThreshold=NaN), nIn=10, nOut=1))"]
General usage examples
Importing data
Loading data from a file (here its a csv)
(ns my.ns
(:require [dl4clj.datasets.input-splits :as s]
[dl4clj.datasets.record-readers :as rr]
[dl4clj.datasets.api.record-readers :refer :all]
[dl4clj.datasets.iterators :as ds-iter]
[dl4clj.datasets.api.iterators :refer :all]
[dl4clj.helpers :refer [data-from-iter]]))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; file splits (convert the data to records)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def poker-path "resources/poker-hand-training.csv")
;; this is not a complete dataset, it is just here to sever as an example
(def file-split (s/new-filesplit :path poker-path))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; record readers, (read the records created by the file split)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def csv-rr (initialize-rr! :rr (rr/new-csv-record-reader :skip-n-lines 0 :delimiter ",")
:input-split file-split))
;; lets look at some data
(println (next-record! :rr csv-rr :as-code? false))
;; => #object[java.util.ArrayList 0x2473e02d [1, 10, 1, 11, 1, 13, 1, 12, 1, 1, 9]]
;; this is our first line from the csv
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; record readers dataset iterators (turn our writables into a dataset)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def rr-ds-iter (ds-iter/new-record-reader-dataset-iterator
:record-reader csv-rr
:batch-size 1
:label-idx 10
:n-possible-labels 10))
;; we use our record reader created above
;; we want to see one example per dataset obj returned (:batch-size = 1)
;; we know our label is at the last index, so :label-idx = 10
;; there are 10 possible types of poker hands so :n-possible-labels = 10
;; you can also set :label-idx to -1 to use the last index no matter the size of the seq
(def other-rr-ds-iter (ds-iter/new-record-reader-dataset-iterator
:record-reader csv-rr
:batch-size 1
:label-idx -1
:n-possible-labels 10))
(str (next-example! :iter rr-ds-iter :as-code? false))
;; =>
;;===========INPUT===================
;;[1.00, 10.00, 1.00, 11.00, 1.00, 13.00, 1.00, 12.00, 1.00, 1.00]
;;=================OUTPUT==================
;;[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00]
;; and to show that :label-idx = -1 gives us the same output
(= (next-example! :iter rr-ds-iter :as-code? false)
(next-example! :iter other-rr-ds-iter :as-code? false)) ;; => true
INDArrays and Datasets from clojure data structures
(ns my.ns
(:require [nd4clj.linalg.factory.nd4j :refer [vec->indarray matrix->indarray
indarray-of-zeros indarray-of-ones
indarray-of-rand vec-or-matrix->indarray]]
[dl4clj.datasets.new-datasets :refer [new-ds]]
[dl4clj.datasets.api.datasets :refer [as-list]]
[dl4clj.datasets.iterators :refer [new-existing-dataset-iterator]]
[dl4clj.datasets.api.iterators :refer :all]
[dl4clj.datasets.pre-processors :as ds-pp]
[dl4clj.datasets.api.pre-processors :refer :all]
[dl4clj.core :as c]))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; INDArray creation
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;TODO: consider defaulting to code
;; can create from a vector
(vec->indarray [1 2 3 4])
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x269df212 [1.00, 2.00, 3.00, 4.00]]
;; or from a matrix
(matrix->indarray [[1 2 3 4] [2 4 6 8]])
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x20aa7fe1
;; [[1.00, 2.00, 3.00, 4.00], [2.00, 4.00, 6.00, 8.00]]]
;; will fill in spareness with zeros
(matrix->indarray [[1 2 3 4] [2 4 6 8] [10 12]])
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x8b7796c
;;[[1.00, 2.00, 3.00, 4.00],
;; [2.00, 4.00, 6.00, 8.00],
;; [10.00, 12.00, 0.00, 0.00]]]
;; can create an indarray of all zeros with specified shape
;; defaults to :rows = 1 :columns = 1
(indarray-of-zeros :rows 3 :columns 2)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x6f586a7e
;;[[0.00, 0.00],
;; [0.00, 0.00],
;; [0.00, 0.00]]]
(indarray-of-zeros) ;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0xe59ffec 0.00]
;; and if only one is supplied, will get a vector of specified length
(indarray-of-zeros :rows 2)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x2899d974 [0.00, 0.00]]
(indarray-of-zeros :columns 2)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0xa5b9782 [0.00, 0.00]]
;; same considerations/defaults for indarray-of-ones and indarray-of-rand
(indarray-of-ones :rows 2 :columns 3)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x54f08662 [[1.00, 1.00, 1.00], [1.00, 1.00, 1.00]]]
(indarray-of-rand :rows 2 :columns 3)
;; all values are greater than 0 but less than 1
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x2f20293b [[0.85, 0.86, 0.13], [0.94, 0.04, 0.36]]]
;; vec-or-matrix->indarray is built into all functions which require INDArrays
;; so that you can use clojure data structures
;; but you still have the option of passing existing INDArrays
(def example-array (vec-or-matrix->indarray [1 2 3 4]))
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x5c44c71f [1.00, 2.00, 3.00, 4.00]]
(vec-or-matrix->indarray example-array)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x607b03b0 [1.00, 2.00, 3.00, 4.00]]
(vec-or-matrix->indarray (indarray-of-rand :rows 2))
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x49143b08 [0.76, 0.92]]
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; data-set creation
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def ds-with-single-example (new-ds :input [1 2 3 4]
:output [0.0 1.0 0.0]))
(as-list :ds ds-with-single-example :as-code? false)
;; =>
;; #object[java.util.ArrayList 0x5d703d12
;;[===========INPUT===================
;;[1.00, 2.00, 3.00, 4.00]
;;=================OUTPUT==================
;;[0.00, 1.00, 0.00]]]
(def ds-with-multiple-examples (new-ds
:input [[1 2 3 4] [2 4 6 8]]
:output [[0.0 1.0 0.0] [0.0 0.0 1.0]]))
(as-list :ds ds-with-multiple-examples :as-code? false)
;; =>
;;#object[java.util.ArrayList 0x29c7a9e2
;;[===========INPUT===================
;;[1.00, 2.00, 3.00, 4.00]
;;=================OUTPUT==================
;;[0.00, 1.00, 0.00],
;;===========INPUT===================
;;[2.00, 4.00, 6.00, 8.00]
;;=================OUTPUT==================
;;[0.00, 0.00, 1.00]]]
;; we can create a dataset iterator from the code which creates datasets
;; and set the labels for our outputs (optional)
(def ds-with-multiple-examples
(new-ds
:input [[1 2 3 4] [2 4 6 8]]
:output [[0.0 1.0 0.0] [0.0 0.0 1.0]]))
;; iterator
(def training-rr-ds-iter
(new-existing-dataset-iterator
:dataset ds-with-multiple-examples
:labels ["foo" "baz" "foobaz"]))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; data-set normalization
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; this gathers statistics on the dataset and normalizes the data
;; and applies the transformation to all dataset objects in the iterator
(def train-iter-normalized
(c/normalize-iter! :iter training-rr-ds-iter
:normalizer (ds-pp/new-standardize-normalization-ds-preprocessor)
:as-code? false))
;; above returns the normalized iterator
;; to get fit normalizer
(def the-normalizer
(get-pre-processor train-iter-normalized))
Model configuration
Creating a neural network configuration with singe and multiple layers
(ns my.ns
(:require [dl4clj.nn.conf.builders.layers :as l]
[dl4clj.nn.conf.builders.nn :as nn]
[dl4clj.nn.conf.distributions :as dist]
[dl4clj.nn.conf.input-pre-processor :as pp]
[dl4clj.nn.conf.step-fns :as s-fn]))
;; nn/builder has 3 types of args
;; 1) args which set network configuration params
;; 2) args which set default values for layers
;; 3) args which set multi layer network configuration params
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; single layer nn configuration
;; here we are setting network configuration
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(nn/builder :optimization-algo :stochastic-gradient-descent
:seed 123
:iterations 1
:minimize? true
:use-drop-connect? false
:lr-score-based-decay-rate 0.002
:regularization? false
:step-fn :default-step-fn
:layers {:dense-layer {:activation-fn :relu
:updater :adam
:adam-mean-decay 0.2
:adam-var-decay 0.1
:learning-rate 0.006
:weight-init :xavier
:layer-name "single layer model example"
:n-in 10
:n-out 20}})
;; there are several options within a nn-conf map which can be configuration maps
;; or calls to fns
;; It doesn't matter which option you choose and you don't have to stay consistent
;; the list of params which can be passed as config maps or fn calls will
;; be enumerated at a later date
(nn/builder :optimization-algo :stochastic-gradient-descent
:seed 123
:iterations 1
:minimize? true
:use-drop-connect? false
:lr-score-based-decay-rate 0.002
:regularization? false
:step-fn (s-fn/new-default-step-fn)
:build? true
;; dont need to specify layer order, theres only one
:layers (l/dense-layer-builder
:activation-fn :relu
:updater :adam
:adam-mean-decay 0.2
:adam-var-decay 0.1
:dist (dist/new-normal-distribution :mean 0 :std 1)
:learning-rate 0.006
:weight-init :xavier
:layer-name "single layer model example"
:n-in 10
:n-out 20))
;; these configurations are the same
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; multi-layer configuration
;; here we are also setting layer defaults
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; defaults will apply to layers which do not specify those value in their config
(nn/builder
:optimization-algo :stochastic-gradient-descent
:seed 123
:iterations 1
:minimize? true
:use-drop-connect? false
:lr-score-based-decay-rate 0.002
:regularization? false
:default-activation-fn :sigmoid
:default-weight-init :uniform
;; we need to specify the layer order
:layers {0 (l/activation-layer-builder
:activation-fn :relu
:updater :adam
:adam-mean-decay 0.2
:adam-var-decay 0.1
:learning-rate 0.006
:weight-init :xavier
:layer-name "example first layer"
:n-in 10
:n-out 20)
1 {:output-layer {:n-in 20
:n-out 2
:loss-fn :mse
:layer-name "example output layer"}}})
;; specifying multi-layer config params
(nn/builder
;; network args
:optimization-algo :stochastic-gradient-descent
:seed 123
:iterations 1
:minimize? true
:use-drop-connect? false
:lr-score-based-decay-rate 0.002
:regularization? false
;; layer defaults
:default-activation-fn :sigmoid
:default-weight-init :uniform
;; the layers
:layers {0 (l/activation-layer-builder
:activation-fn :relu
:updater :adam
:adam-mean-decay 0.2
:adam-var-decay 0.1
:learning-rate 0.006
:weight-init :xavier
:layer-name "example first layer"
:n-in 10
:n-out 20)
1 {:output-layer {:n-in 20
:n-out 2
:loss-fn :mse
:layer-name "example output layer"}}}
;; multi layer network args
:backprop? true
:backprop-type :standard
:pretrain? false
:input-pre-processors {0 (pp/new-zero-mean-pre-pre-processor)
1 {:unit-variance-processor {}}})
Configuration to Trained models
Multi Layer models
- an implementation of the dl4j mnist classification example
(ns my.ns
(:require [dl4clj.datasets.iterators :as iter]
[dl4clj.datasets.input-splits :as split]
[dl4clj.datasets.record-readers :as rr]
[dl4clj.optimize.listeners :as listener]
[dl4clj.nn.conf.builders.nn :as nn]
[dl4clj.nn.multilayer.multi-layer-network :as mln]
[dl4clj.nn.api.model :refer [init! set-listeners!]]
[dl4clj.nn.api.multi-layer-network :refer [evaluate-classification]]
[dl4clj.datasets.api.record-readers :refer [initialize-rr!]]
[dl4clj.eval.api.eval :refer [get-stats get-accuracy]]
[dl4clj.core :as c]))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; nn-conf -> multi-layer-network
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def nn-conf
(nn/builder
;; network args
:optimization-algo :stochastic-gradient-descent
:seed 123 :iterations 1 :regularization? true
;; setting layer defaults
:default-activation-fn :relu :default-l2 7.5e-6
:default-weight-init :xavier :default-learning-rate 0.0015
:default-updater :nesterovs :default-momentum 0.98
;; setting layer configuration
:layers {0 {:dense-layer
{:layer-name "example first layer"
:n-in 784 :n-out 500}}
1 {:dense-layer
{:layer-name "example second layer"
:n-in 500 :n-out 100}}
2 {:output-layer
{:n-in 100 :n-out 10
;; layer specific params
:loss-fn :negativeloglikelihood
:activation-fn :softmax
:layer-name "example output layer"}}}
;; multi layer args
:backprop? true
:pretrain? false))
(def multi-layer-network (c/model-from-conf nn-conf))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; local cpu training with dl4j pre-built iterators
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; lets use the pre-built Mnist data set iterator
(def train-mnist-iter
(iter/new-mnist-data-set-iterator
:batch-size 64
:train? true
:seed 123))
(def test-mnist-iter
(iter/new-mnist-data-set-iterator
:batch-size 64
:train? false
:seed 123))
;; and lets set a listener so we can know how training is going
(def score-listener (listener/new-score-iteration-listener :print-every-n 5))
;; and attach it to our model
;; TODO: listeners are broken, look into log4j warnning
(def mln-with-listener (set-listeners! :model multi-layer-network
:listeners [score-listener]))
(def trained-mln (mln/train-mln-with-ds-iter! :mln mln-with-listener
:iter train-mnist-iter
:n-epochs 15
:as-code? false))
;; training happens because :as-code? = false
;; if it was true, we would still just have a data structure
;; we now have a trained model that has seen the training dataset 15 times
;; time to evaluate our model
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;Create an evaluation object
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def eval-obj (evaluate-classification :mln trained-mln
:iter test-mnist-iter))
;; always remember that these objects are stateful, dont use the same eval-obj
;; to eval two different networks
;; we trained the model on a training dataset. We evaluate on a test set
(println (get-stats :evaler eval-obj))
;; this will print the stats to standard out for each feature/label pair
;;Examples labeled as 0 classified by model as 0: 968 times
;;Examples labeled as 0 classified by model as 1: 1 times
;;Examples labeled as 0 classified by model as 2: 1 times
;;Examples labeled as 0 classified by model as 3: 1 times
;;Examples labeled as 0 classified by model as 5: 1 times
;;Examples labeled as 0 classified by model as 6: 3 times
;;Examples labeled as 0 classified by model as 7: 1 times
;;Examples labeled as 0 classified by model as 8: 2 times
;;Examples labeled as 0 classified by model as 9: 2 times
;;Examples labeled as 1 classified by model as 1: 1126 times
;;Examples labeled as 1 classified by model as 2: 2 times
;;Examples labeled as 1 classified by model as 3: 1 times
;;Examples labeled as 1 classified by model as 5: 1 times
;;Examples labeled as 1 classified by model as 6: 2 times
;;Examples labeled as 1 classified by model as 7: 1 times
;;Examples labeled as 1 classified by model as 8: 2 times
;;Examples labeled as 2 classified by model as 0: 3 times
;;Examples labeled as 2 classified by model as 1: 2 times
;;Examples labeled as 2 classified by model as 2: 1006 times
;;Examples labeled as 2 classified by model as 3: 2 times
;;Examples labeled as 2 classified by model as 4: 3 times
;;Examples labeled as 2 classified by model as 6: 3 times
;;Examples labeled as 2 classified by model as 7: 7 times
;;Examples labeled as 2 classified by model as 8: 6 times
;;Examples labeled as 3 classified by model as 2: 4 times
;;Examples labeled as 3 classified by model as 3: 990 times
;;Examples labeled as 3 classified by model as 5: 3 times
;;Examples labeled as 3 classified by model as 7: 3 times
;;Examples labeled as 3 classified by model as 8: 3 times
;;Examples labeled as 3 classified by model as 9: 7 times
;;Examples labeled as 4 classified by model as 2: 2 times
;;Examples labeled as 4 classified by model as 3: 1 times
;;Examples labeled as 4 classified by model as 4: 967 times
;;Examples labeled as 4 classified by model as 6: 4 times
;;Examples labeled as 4 classified by model as 7: 1 times
;;Examples labeled as 4 classified by model as 9: 7 times
;;Examples labeled as 5 classified by model as 0: 2 times
;;Examples labeled as 5 classified by model as 3: 6 times
;;Examples labeled as 5 classified by model as 4: 1 times
;;Examples labeled as 5 classified by model as 5: 874 times
;;Examples labeled as 5 classified by model as 6: 3 times
;;Examples labeled as 5 classified by model as 7: 1 times
;;Examples labeled as 5 classified by model as 8: 3 times
;;Examples labeled as 5 classified by model as 9: 2 times
;;Examples labeled as 6 classified by model as 0: 4 times
;;Examples labeled as 6 classified by model as 1: 3 times
;;Examples labeled as 6 classified by model as 3: 2 times
;;Examples labeled as 6 classified by model as 4: 4 times
;;Examples labeled as 6 classified by model as 5: 4 times
;;Examples labeled as 6 classified by model as 6: 939 times
;;Examples labeled as 6 classified by model as 7: 1 times
;;Examples labeled as 6 classified by model as 8: 1 times
;;Examples labeled as 7 classified by model as 1: 7 times
;;Examples labeled as 7 classified by model as 2: 4 times
;;Examples labeled as 7 classified by model as 3: 3 times
;;Examples labeled as 7 classified by model as 7: 1005 times
;;Examples labeled as 7 classified by model as 8: 2 times
;;Examples labeled as 7 classified by model as 9: 7 times
;;Examples labeled as 8 classified by model as 0: 3 times
;;Examples labeled as 8 classified by model as 2: 3 times
;;Examples labeled as 8 classified by model as 3: 2 times
;;Examples labeled as 8 classified by model as 4: 4 times
;;Examples labeled as 8 classified by model as 5: 3 times
;;Examples labeled as 8 classified by model as 6: 2 times
;;Examples labeled as 8 classified by model as 7: 4 times
;;Examples labeled as 8 classified by model as 8: 947 times
;;Examples labeled as 8 classified by model as 9: 6 times
;;Examples labeled as 9 classified by model as 0: 2 times
;;Examples labeled as 9 classified by model as 1: 2 times
;;Examples labeled as 9 classified by model as 3: 4 times
;;Examples labeled as 9 classified by model as 4: 8 times
;;Examples labeled as 9 classified by model as 6: 1 times
;;Examples labeled as 9 classified by model as 7: 4 times
;;Examples labeled as 9 classified by model as 8: 2 times
;;Examples labeled as 9 classified by model as 9: 986 times
;;==========================Scores========================================
;; Accuracy: 0.9808
;; Precision: 0.9808
;; Recall: 0.9807
;; F1 Score: 0.9807
;;========================================================================
;; can get the stats that are printed via fns in the evaluation namespace
;; after running eval-model-whole-ds
(get-accuracy :evaler evaler-with-stats) ;; => 0.9808
Model Tuning
Early Stopping (controlling training)
it is recommened you start here when designing models
using dl4clj.core
(ns my.ns
(:require [dl4clj.earlystopping.termination-conditions :refer :all]
[dl4clj.earlystopping.model-saver :refer [new-in-memory-saver]]
[dl4clj.nn.api.multi-layer-network :refer [evaluate-classification]]
[dl4clj.eval.api.eval :refer [get-stats]]
[dl4clj.nn.conf.builders.nn :as nn]
[dl4clj.datasets.iterators :as iter]
[dl4clj.core :as c]))
(def nn-conf
(nn/builder
;; network args
:optimization-algo :stochastic-gradient-descent
:seed 123
:iterations 1
:regularization? true
;; setting layer defaults
:default-activation-fn :relu
:default-l2 7.5e-6
:default-weight-init :xavier
:default-learning-rate 0.0015
:default-updater :nesterovs
:default-momentum 0.98
;; setting layer configuration
:layers {0 {:dense-layer
{:layer-name "example first layer"
:n-in 784 :n-out 500}}
1 {:dense-layer
{:layer-name "example second layer"
:n-in 500 :n-out 100}}
2 {:output-layer
{:n-in 100 :n-out 10
;; layer specific params
:loss-fn :negativeloglikelihood
:activation-fn :softmax
:layer-name "example output layer"}}}
;; multi layer args
:backprop? true
:pretrain? false))
(def train-iter
(iter/new-mnist-data-set-iterator
:batch-size 64
:train? true
:seed 123))
(def test-iter
(iter/new-mnist-data-set-iterator
:batch-size 64
:train? false
:seed 123))
(def invalid-score-condition (new-invalid-score-iteration-termination-condition))
(def max-score-condition (new-max-score-iteration-termination-condition
:max-score 20.0))
(def max-time-condition (new-max-time-iteration-termination-condition
:max-time-val 10
:max-time-unit :minutes))
(def score-doesnt-improve-condition (new-score-improvement-epoch-termination-condition
:max-n-epoch-no-improve 5))
(def target-score-condition (new-best-score-epoch-termination-condition
:best-expected-score 0.009))
(def max-number-epochs-condition (new-max-epochs-termination-condition :max-n 20))
(def in-mem-saver (new-in-memory-saver))
(def trained-mln
;; defaults to returning the model
(c/train-with-early-stopping
:nn-conf nn-conf
:training-iter train-mnist-iter
:testing-iter test-mnist-iter
:eval-every-n-epochs 1
:iteration-termination-conditions [invalid-score-condition
max-score-condition
max-time-condition]
:epoch-termination-conditions [score-doesnt-improve-condition
target-score-condition
max-number-epochs-condition]
:save-last-model? true
:model-saver in-mem-saver
:as-code? false))
(def model-evaler
(evaluate-classification :mln trained-mln :iter test-mnist-iter))
(println (get-stats :evaler model-evaler))
- explicit, step by step way of doing this
(ns my.ns
(:require [dl4clj.earlystopping.early-stopping-config :refer [new-early-stopping-config]]
[dl4clj.earlystopping.termination-conditions :refer :all]
[dl4clj.earlystopping.model-saver :refer [new-in-memory-saver new-local-file-model-saver]]
[dl4clj.earlystopping.score-calc :refer [new-ds-loss-calculator]]
[dl4clj.earlystopping.early-stopping-trainer :refer [new-early-stopping-trainer]]
[dl4clj.earlystopping.api.early-stopping-trainer :refer [fit-trainer!]]
[dl4clj.nn.conf.builders.nn :as nn]
[dl4clj.nn.multilayer.multi-layer-network :as mln]
[dl4clj.utils :refer [load-model!]]
[dl4clj.datasets.iterators :as iter]
[dl4clj.core :as c]))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; start with our network config
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def nn-conf
(nn/builder
;; network args
:optimization-algo :stochastic-gradient-descent
:seed 123 :iterations 1 :regularization? true
;; setting layer defaults
:default-activation-fn :relu :default-l2 7.5e-6
:default-weight-init :xavier :default-learning-rate 0.0015
:default-updater :nesterovs :default-momentum 0.98
;; setting layer configuration
:layers {0 {:dense-layer
{:layer-name "example first layer"
:n-in 784 :n-out 500}}
1 {:dense-layer
{:layer-name "example second layer"
:n-in 500 :n-out 100}}
2 {:output-layer
{:n-in 100 :n-out 10
;; layer specific params
:loss-fn :negativeloglikelihood
:activation-fn :softmax
:layer-name "example output layer"}}}
;; multi layer args
:backprop? true
:pretrain? false))
(def mln (c/model-from-conf nn-conf))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; the training/testing data
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def train-iter
(iter/new-mnist-data-set-iterator
:batch-size 64
:train? true
:seed 123))
(def test-iter
(iter/new-mnist-data-set-iterator
:batch-size 64
:train? false
:seed 123))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; we are going to need termination conditions
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; these allow us to control when we exit training
;; this can be based off of iterations or epochs
;; iteration termination conditions
(def invalid-score-condition (new-invalid-score-iteration-termination-condition))
(def max-score-condition (new-max-score-iteration-termination-condition
:max-score 20.0))
(def max-time-condition (new-max-time-iteration-termination-condition
:max-time-val 10
:max-time-unit :minutes))
;; epoch termination conditions
(def score-doesnt-improve-condition (new-score-improvement-epoch-termination-condition
:max-n-epoch-no-improve 5))
(def target-score-condition (new-best-score-epoch-termination-condition :best-expected-score 0.009))
(def max-number-epochs-condition (new-max-epochs-termination-condition :max-n 20))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; we also need a way to save our model
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; can be in memory or to a local directory
(def in-mem-saver (new-in-memory-saver))
(def local-file-saver (new-local-file-model-saver :directory "resources/tmp/readme/"))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; set up your score calculator
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def score-calcer (new-ds-loss-calculator :iter test-iter
:average? true))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; create an early stopping configuration
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; termination conditions
;; a way to save our model
;; a way to calculate the score of our model on the dataset
(def early-stopping-conf
(new-early-stopping-config
:epoch-termination-conditions [score-doesnt-improve-condition
target-score-condition
max-number-epochs-condition]
:iteration-termination-conditions [invalid-score-condition
max-score-condition
max-time-condition]
:eval-every-n-epochs 5
:model-saver local-file-saver
:save-last-model? true
:score-calculator score-calcer))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; create an early stopping trainer from our data, model and early stopping conf
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def es-trainer (new-early-stopping-trainer :early-stopping-conf early-stopping-conf
:mln mln
:iter train-iter))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; fit and use our early stopping trainer
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def es-trainer-fitted (fit-trainer! es-trainer :as-code? false))
;; when the trainer terminates, you will see something like this
;;[nREPL-worker-24] BaseEarlyStoppingTrainer INFO Completed training epoch 14
;;[nREPL-worker-24] BaseEarlyStoppingTrainer INFO New best model: score = 0.005225599372851298,
;; epoch = 14 (previous: score = 0.018243224899038346, epoch = 7)
;;[nREPL-worker-24] BaseEarlyStoppingTrainer INFO Hit epoch termination condition at epoch 14.
;; Details: BestScoreEpochTerminationCondition(0.009)
;; and if we look at the es-trainer-fitted object we see
;;#object[org.deeplearning4j.earlystopping.EarlyStoppingResult 0x5ab74f27 EarlyStoppingResult
;;(terminationReason=EpochTerminationCondition,details=BestScoreEpochTerminationCondition(0.009),
;; bestModelEpoch=14,bestModelScore=0.005225599372851298,totalEpochs=15)]
;; and our model has been saved to /resources/tmp/readme/bestModel.bin
;; there we have our model config, model params and our updater state
;; we can then load this model to use it or continue refining it
(def loaded-model (load-model! :path "resources/tmp/readme/bestModel.bin"
:load-updater? true))
Transfer Learning (freezing layers)
;; TODO: need to write up examples
Spark Training
dl4j Spark usage
How it is done in dl4clj
- Uses dl4clj.core
- This example uses a fn which takes care of most steps for you
- allows you to pass args as code bc the fn accounts for the multiple spark contexts issue encountered when everything is just a data structure
- This example uses a fn which takes care of most steps for you
(ns my.ns
(:require [dl4clj.nn.conf.builders.layers :as l]
[dl4clj.nn.conf.builders.nn :as nn]
[dl4clj.datasets.iterators :refer [new-iris-data-set-iterator]]
[dl4clj.eval.api.eval :refer [get-stats]]
[dl4clj.spark.masters.param-avg :as master]
[dl4clj.spark.data.java-rdd :refer [new-java-spark-context
java-rdd-from-iter]]
[dl4clj.spark.api.dl4j-multi-layer :refer [eval-classification-spark-mln
get-spark-context]]
[dl4clj.core :as c]))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 1, create your model config
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def mln-conf
(nn/builder
:optimization-algo :stochastic-gradient-descent
:default-learning-rate 0.006
:layers {0 (l/dense-layer-builder :n-in 4 :n-out 2 :activation-fn :relu)
1 {:output-layer
{:loss-fn :negativeloglikelihood
:n-in 2 :n-out 3
:activation-fn :soft-max
:weight-init :xavier}}}
:backprop? true
:backprop-type :standard))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 2, training master
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def training-master
(master/new-parameter-averaging-training-master
:build? true
:rdd-n-examples 10
:n-workers 4
:averaging-freq 10
:batch-size-per-worker 2
:export-dir "resources/spark/master/"
:rdd-training-approach :direct
:repartition-data :always
:repartition-strategy :balanced
:seed 1234
:save-updater? true
:storage-level :none))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 3, spark context
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def your-spark-context
(new-java-spark-context :app-name "example app"))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 4, training data
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def iris-iter
(new-iris-data-set-iterator
:batch-size 1
:n-examples 5))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 5, spark mln
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def fitted-spark-mln
(c/train-with-spark :spark-context your-spark-context
:mln-conf mln-conf
:training-master training-master
:iter iris-iter
:n-epochs 1
:as-code? false))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 5, use spark context from spark-mln to create rdd
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; TODO: eliminate this step
(def our-rdd
(let [sc (get-spark-context fitted-spark-mln :as-code? false)]
(java-rdd-from-iter :spark-context sc
:iter iris-iter)))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 6, evaluation model and print stats (poor performance of model expected)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def eval-obj
(eval-classification-spark-mln
:spark-mln fitted-spark-mln
:rdd our-rdd))
(println (get-stats :evaler eval-obj))
- this example demonstrates the dl4j workflow
- NOTE: unlike the previous example, this one requires dl4j objects to be used
- this is becaues spark only wants you to have one spark context at a time
- NOTE: unlike the previous example, this one requires dl4j objects to be used
(ns my.ns
(:require [dl4clj.nn.conf.builders.layers :as l]
[dl4clj.nn.conf.builders.nn :as nn]
[dl4clj.datasets.iterators :refer [new-iris-data-set-iterator]]
[dl4clj.eval.api.eval :refer [get-stats]]
[dl4clj.spark.masters.param-avg :as master]
[dl4clj.spark.data.java-rdd :refer [new-java-spark-context java-rdd-from-iter]]
[dl4clj.spark.dl4j-multi-layer :as spark-mln]
[dl4clj.spark.api.dl4j-multi-layer :refer [fit-spark-mln!
eval-classification-spark-mln]]))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 1, create your model
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def mln-conf
(nn/builder
:optimization-algo :stochastic-gradient-descent
:default-learning-rate 0.006
:layers {0 (l/dense-layer-builder :n-in 4 :n-out 2 :activation-fn :relu)
1 {:output-layer
{:loss-fn :negativeloglikelihood
:n-in 2 :n-out 3
:activation-fn :soft-max
:weight-init :xavier}}}
:backprop? true
:as-code? false
:backprop-type :standard))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 2, create a training master
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; not all options specified, but most are
(def training-master
(master/new-parameter-averaging-training-master
:build? true
:rdd-n-examples 10
:n-workers 4
:averaging-freq 10
:batch-size-per-worker 2
:export-dir "resources/spark/master/"
:rdd-training-approach :direct
:repartition-data :always
:repartition-strategy :balanced
:seed 1234
:as-code? false
:save-updater? true
:storage-level :none))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 3, create a Spark Multi Layer Network
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def your-spark-context
(new-java-spark-context :app-name "example app" :as-code? false))
;; new-java-spark-context will turn an existing spark-configuration into a java spark context
;; or create a new java spark context with master set to "local[*]" and the app name
;; set to :app-name
(def spark-mln
(spark-mln/new-spark-multi-layer-network
:spark-context your-spark-context
:mln mln-conf
:training-master training-master
:as-code? false))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 4, load your data
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; one way is via a dataset-iterator
;; can make one directly from a dataset (iterator data-set)
;; see: nd4clj.linalg.dataset.api.data-set and nd4clj.linalg.dataset.data-set
;; we are going to use a pre-built one
(def iris-iter
(new-iris-data-set-iterator
:batch-size 1
:n-examples 5
:as-code? false))
;; now lets convert the data into a javaRDD
(def our-rdd
(java-rdd-from-iter :spark-context your-spark-context
:iter iris-iter))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 5, fit and evaluate the model
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def fitted-spark-mln
(fit-spark-mln!
:spark-mln spark-mln
:rdd our-rdd
:n-epochs 1))
;; this fn also has the option to supply :path-to-data instead of :rdd
;; that path should point to a directory containing a number of dataset objects
(def eval-obj
(eval-classification-spark-mln
:spark-mln fitted-spark-mln
:rdd our-rdd))
;; we would want to have different testing and training rdd's but here we are using
;; the data we trained on
;; lets get the stats for how our model performed
(println (get-stats :evaler eval-obj))
Terminology
Coming soon
Packages to come back to:
Implement ComputationGraphs and the classes which use them
- https://deeplearning4j.org/doc/org/deeplearning4j/nn/graph/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/nn/conf/ComputationGraphConfiguration.GraphBuilder.html
- https://deeplearning4j.org/doc/org/deeplearning4j/nn/conf/ComputationGraphConfiguration.html
- https://deeplearning4j.org/doc/org/deeplearning4j/nn/conf/graph/package-frame.html
- https://deeplearning4j.org/doc/org/deeplearning4j/nn/conf/graph/rnn/package-frame.html
NLP
- https://deeplearning4j.org/doc/org/deeplearning4j/bagofwords/vectorizer/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/datasets/vectorizer/Vectorizer.html
- https://deeplearning4j.org/doc/org/deeplearning4j/iterator/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/iterator/provider/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/WeightLookupTable.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/inmemory/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/learning/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/learning/impl/elements/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/learning/impl/sequence/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/loader/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/reader/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/reader/impl/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/wordvectors/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/glove/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/glove/count/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/node2vec/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/paragraphvectors/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/enums/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/interfaces/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/iterators/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/listeners/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/sequence/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/serialization/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/transformers/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/transformers/impl/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/transformers/impl/iterables/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/word2vec/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/word2vec/iterator/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/word2vec/wordstore/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/word2vec/wordstore/inmemory/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/annotator/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/corpora/sentiwordnet/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/corpora/treeparser/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/corpora/treeparser/transformer/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/documentiterator/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/documentiterator/interoperability/DocumentIteratorConverter.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/inputsanitation/InputHomogenization.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/invertedindex/InvertedIndex.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/labels/LabelsProvider.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/movingwindow/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/sentenceiterator/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/sentenceiterator/interoperability/SentenceIteratorConverter.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/sentenceiterator/labelaware/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/stopwords/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/tokenization/tokenizer/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/tokenization/tokenizer/preprocessor/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/tokenization/tokenizer/tokenprepreprocessor/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/tokenization/tokenizerfactory/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/uima/UimaResource.html
Parallelism
- https://deeplearning4j.org/doc/org/deeplearning4j/parallelism/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/parallelism/factory/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/parallelism/main/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/parallelism/parameterserver/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/parallelism/trainer/package-summary.html
TSNE
UI
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/activation/PathUpdate.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/api/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/chart/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/chart/style/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/component/ComponentDiv.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/component/style/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/decorator/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/decorator/style/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/table/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/table/style/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/text/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/text/style/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/flow/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/flow/beans/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/flow/data/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/i18n/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/convolutional/ConvolutionalListenerModule.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/defaultModule/DefaultModule.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/flow/FlowListenerModule.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/histogram/HistogramModule.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/remote/RemoteReceiverModule.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/train/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/tsne/TsneModule.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/nearestneighbors/word2vec/NearestNeighborsQuery.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/play/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/play/misc/FunctionUtil.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/play/staticroutes/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/providers/ObjectMapperProvider.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/renders/PathUpdate.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/standalone/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/stats/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/stats/api/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/stats/impl/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/stats/impl/java/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/stats/sbe/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/storage/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/storage/impl/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/storage/mapdb/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/storage/sqlite/J7FileStatsStorage.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/weights/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/weights/beans/CompactModelAndGradient.html
Author: yetanalytics
Source Code: https://github.com/yetanalytics/dl4clj
License: BSD-2-Clause License
1591611780
How to Find Ulimit For user on Linux
How can I find the correct ulimit values for a user account or process on Linux systems?
For proper operation, we must ensure that the correct ulimit values set after installing various software. The Linux system provides means of restricting the number of resources that can be used. Limits set for each Linux user account. However, system limits are applied separately to each process that is running for that user too. For example, if certain thresholds are too low, the system might not be able to server web pages using Nginx/Apache or PHP/Python app. System resource limits viewed or set with the NA command. Let us see how to use the ulimit that provides control over the resources available to the shell and processes.
#[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object]
1591993440
MEAN Stack Tutorial MongoDB ExpressJS AngularJS NodeJS
We are going to build a full stack Todo App using the MEAN (MongoDB, ExpressJS, AngularJS and NodeJS). This is the last part of three-post series tutorial.
MEAN Stack tutorial series:
AngularJS tutorial for beginners (Part I)
Creating RESTful APIs with NodeJS and MongoDB Tutorial (Part II)
MEAN Stack Tutorial: MongoDB, ExpressJS, AngularJS and NodeJS (Part III) 👈 you are here
Before completing the app, let’s cover some background about the this stack. If you rather jump to the hands-on part click here to get started.
#[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object]
1619799996
Deep Learning Project : Real Time Object Detection in Python & Opencv
Real Time Object Detection in Python And OpenCV
Github Link: https://github.com/Chando0185/Object_Detection
Blog Link: https://knowledgedoctor37.blogspot.com/#
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#python project #object detection #python opencv #opencv object detection #object detection in python #python opencv for object detection