Stellar Core: The Reference Implementation for The Peer to Peer Agent

Stellar-core is a replicated state machine that maintains a local copy of a cryptographic ledger and processes transactions against it, in consensus with a set of peers. It implements the Stellar Consensus Protocol, a federated consensus protocol. It is written in C++14 and runs on Linux, OSX and Windows. Learn more by reading the overview document.

Documentation

Documentation of the code's layout and abstractions, as well as for the functionality available, can be found in ./docs.

Installation

Installation Instructions

These are instructions for building stellar-core from source.

For a potentially quicker set up, the following projects could be good alternatives:

stellar-core in a docker container
stellar-core and horizon in a docker container
pre-compiled packages

Picking a version to run

Best is to use the latest stable release that can be downloaded from https://github.com/stellar/stellar-core/releases

Alternatively, branches are organized in the following way:

branch name	description	quality bar
master	development branch	all unit tests passing
testnet	version deployed to testnet	acceptance tests passing
prod	version currently deployed on the live network	no recall class issue found in testnet and staging

For convenience, we also keep a record in the form of release tags of the versions that make it to production:

pre-releases are versions that get deployed to testnet
releases are versions that made it all the way to production

Containerized dev environment

We maintain a pre-configured Docker configuration ready for development with VSCode.

See the dev container's README for more detail.

Runtime dependencies

stellar-core does not have many dependencies.

If core was configured (see below) to work with Postgresql, a local Postgresql server will need to be deployed to the same host.

To install Postgresql, follow instructions from the Postgresql download page.

Build Dependencies

c++ toolchain and headers that supports c++17
- clang >= 10.0
- g++ >= 8.0
pkg-config
bison and flex
libpq-dev unless you ./configure --disable-postgres in the build step below.
64-bit system
clang-format-10 (for make format to work)
perl
libunwind-dev

Ubuntu

Ubuntu 18.04

You can install the test toolchain to build and run stellar-core with the latest version of the llvm toolchain.

Alternatively, if you want to just depend on stock Ubuntu, you will have to build with clang and have use libc++ instead of libstdc++ when compiling.

Ubuntu 18.04 has clang-10 available, that you can install with

# install clang-10 toolchain
sudo apt-get install clang-10

After installing packages, head to building with clang and libc++.

Adding the test toolchain (optional)

# NOTE: newer version of the compilers are not
#    provided by stock distributions
#    and are provided by the /test toolchain
sudo apt-get install software-properties-common
sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update

Installing packages

# common packages
sudo apt-get install git build-essential pkg-config autoconf automake libtool bison flex libpq-dev libunwind-dev parallel
# if using clang
sudo apt-get install clang-10
# clang with libstdc++
sudo apt-get install gcc-8
# if using g++ or building with libstdc++
# sudo apt-get install gcc-8 g++-8 cpp-8

In order to make changes, you'll need to install the proper version of clang-format.

In order to install the llvm (clang) toolchain, you may have to follow instructions on https://apt.llvm.org/

sudo apt-get install clang-format-10

OS X

When building on OSX, here's some dependencies you'll need:

Install xcode
Install homebrew
brew install libsodium
brew install libtool
brew install autoconf
brew install automake
brew install pkg-config
brew install libpq (required for postgres)
brew install openssl (required for postgres)
brew install parallel (required for running tests)
brew install ccache (required for enabling ccache)

You'll also need to configure pkg-config by adding the following to your shell (.zshenv or .zshrc):

export PKG_CONFIG_PATH="$PKG_CONFIG_PATH:$(brew --prefix)/opt/libpq/lib/pkgconfig" export PKG_CONFIG_PATH="$PKG_CONFIG_PATH:$(brew --prefix)/opt/openssl@3/lib/pkgconfig"

Windows

See INSTALL-Windows.md

Basic Installation

git clone https://github.com/stellar/stellar-core.git
cd stellar-core
git submodule init
git submodule update
Type ./autogen.sh.
Type ./configure (If configure complains about compiler versions, try CXX=clang-10 ./configure or CXX=g++-8 ./configure or similar, depending on your compiler.)
Type make or make -j<N> (where <N> is the number of parallel builds, a number less than the number of CPU cores available, e.g. make -j3)
Type make check to run tests.
Type make install to install.

Building with clang and libc++

On some systems, building with libc++, LLVM's version of the standard library can be done instead of libstdc++ (typically used on Linux).

NB: there are newer versions available of both clang and libc++, you will have to use the versions suited for your system.

You may need to install additional packages for this, for example, on Linux Ubuntu 18.04 LTS with clang-10:

# install libc++ headers
sudo apt-get install libc++-10-dev libc++abi-10-dev

Here are sample steps to achieve this:

export CC=clang-10
export CXX=clang++-10
export CFLAGS="-O3 -g1 -fno-omit-frame-pointer"
export CXXFLAGS="$CFLAGS -stdlib=libc++"
git clone https://github.com/stellar/stellar-core.git
cd stellar-core/
./autogen.sh && ./configure && make -j6

Building with Tracing

Configuring with --enable-tracy will build and embed the client component of the Tracy high-resolution tracing system in the stellar-core binary.

The tracing client will activate automatically when stellar-core is running, and will listen for connections from Tracy servers (a command-line capture utility, or a cross-platform GUI).

The Tracy server components can also be compiled by configuring with --enable-tracy-gui or --enable-tracy-capture.

The GUI depends on the capstone, freetype and glfw libraries and their headers, and on linux or BSD the GTK-2.0 libraries and headers. On Windows and MacOS, native toolkits are used instead.

# On Ubuntu
$ sudo apt-get install libcapstone-dev libfreetype6-dev libglfw3-dev libgtk2.0-dev

# On MacOS
$ brew install capstone freetype2 glfw

Contributing

See Contributing

Running tests

Running tests

There are two ways to run tests:

src/stellar-core test
make check

Always build before running tests, unless using make check which will build for you. See INSTALL.md for instructions for how to build.

Running tests basics with `src/stellar-core test`

run tests with: src/stellar-core test

run one test with: src/stellar-core test testName

run one test category with: src/stellar-core test '[categoryName]'

Categories (or tags) can be combined: AND-ed (by juxtaposition) or OR-ed (by comma-listing).

Tests tagged as [.] or [hide] are not part of the default test.

Tests tagged as [acceptance] are not part of make check test runs.

supported test options can be seen with src/stellar-core test --help

display tests timing information: src/stellar-core test -d yes '[categoryName]'

xml test output (includes nested section information): src/stellar-core test -r xml '[categoryName]'

Tests may also be run with make check, see [Running tests in parallel](#running tests-in-parallel-with-make-check).

Running tests against postgreSQL

There are two options. The easiest is to have the test suite just create a temporary postgreSQL database cluster in /tmp and delete it after the test. That will happen by default if you run make check.

You can also use an existing database cluster so long as it has databases named test0, test1, ..., test9, and test. To set this up, make sure your PGHOST and PGUSER environment variables are appropriately set, then run the following from bash:

for i in $(seq 0 9) ''; do
    psql -c "create database test$i;"
done

You will need to set the TEMP_POSTGRES environment variable to 0 in order to use an existing database cluster.

Running tests in parallel with `make check`

The make check command runs tests and supports parallelization. This functionality is enabled with the following environment variables:

ALL_VERSIONS: If 0, runs the latest protocol version, if 1 runs all protocol version tests.
TEST_SPEC: Used to run just a subset of the tests (default: "~[.]")
NUM_PARTITIONS: Partitions the test suite (after applying TEST_SPEC) into $NUM_PARTITIONS disjoint sets (default: 1)
BATCHSIZE: The number of tests to be batched together to reduce setup overhead. (default: 5)
RUN_PARTITIONS: Run only a subset of the partitions, indexed from 0 (default: "$(seq 0 $((NUM_PARTITIONS-1)))")
TEMP_POSTGRES: Automatically generates temporary database clusters instead of using an existing cluster (default: 1)
RND_SEED: Can be set to a specific value to affect the random test ordering. (default: 1)

For example, env TEST_SPEC="[history]" NUM_PARTITIONS=4 RUN_PARTITIONS="0 1 3" make check will partition the history tests into 4 parts then run parts 0, 1, and 3.

Running stress tests

There are a few special stress tests included in the test suite. Those are subsystem level tests, not to be confused with more advanced tests that would be done as part of performance evaluation.

We adopt the convention of tagging a stress-test for subsystem foo as [foo-stress][stress][hide].

Then, running:

stellar-core test [stress] will run all the stress tests,
stellar-core test [foo-stress] will run the stress tests for subsystem foo alone, and
neither stellar-core test nor stellar-core test [foo] will run stress tests.

Running and updating TxMeta checks

The stellar-core test unit tests can be run in two special modes that hash the TxMeta of each transaction executed. These two modes can increase confidence that a change to stellar-core does not alter the semantics of any transactions. The two modes are:

--record-test-tx-meta <dirname> which records TxMeta hashes into <dirname>
--check-test-tx-meta <dirname> which checks TxMeta hashes against <dirname>

Continuous integration tests automatically run the --check-test-tx-meta mode against a pair of captured baseline directories stored in the repository, called test-tx-meta-baseline-current (for the current protocol) and text-tx-meta-baseline-next (for the next protocol). If you make intentional changes to the semantics of any transactions, or add any new transactions that need to have their hashes recorded, you can re-record the baseline using a command like:

stellar-core test [tx] --all-versions --rng-seed 12345 --record-test-tx-meta test-tx-meta-baseline-current

for a build with only the current protocol enabled, and:

stellar-core test [tx] --all-versions --rng-seed 12345 --record-test-tx-meta test-tx-meta-baseline-next

for a build configured with --enable-next-protocol-version-unsafe-for-production.

These commands will rewrite the baseline files, which are human-readable JSON files. You should then inspect to see that only the transactions you expected to see change did so. If so, commit the changes as a new set of baselines for future tests.

Download Details:
Author: stellar
Source Code: https://github.com/stellar/stellar-core
License: View license

#blockchain #stellar #c #cpluplus

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Stellar Core: The Reference Implementation for The Peer to Peer Agent

Archie Powell

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Tensorflex: Tensorflow Bindings for The Elixir Programming Language

Tensorflex

The paper detailing Tensorflex was presented at NeurIPS/NIPS 2018 as part of the MLOSS workshop. The paper can be found here.

How to run

You need to have the Tensorflow C API installed. Look here for details.
You also need the C library libjpeg. If you are using Linux or OSX, it should already be present on your machine, otherwise be sure to install (brew install libjpeg for OSX, and sudo apt-get install libjpeg-dev for Ubuntu).
Simply add Tensorflex to your list of dependencies in mix.exs and you are good to go!:

{:tensorflex, "~> 0.1.2"}

In case you want the latest development version use this:

{:tensorflex, github: "anshuman23/tensorflex"}

Documentation

Tensorflex contains three main structs which handle different datatypes. These are %Graph, %Matrix and %Tensor. %Graph type structs handle pre-trained graph models, %Matrix handles Tensorflex 2-D matrices, and %Tensor handles Tensorflow Tensor types. The official Tensorflow documentation is present here and do note that this README only briefly discusses Tensorflex functionalities.

read_graph/1:

Used for loading a Tensorflow .pb graph model in Tensorflex.

Reads in a pre-trained Tensorflow protobuf (.pb) Graph model binary file.

Returns a tuple {:ok, %Graph}.

%Graph is an internal Tensorflex struct which holds the name of the graph file and the binary definition data that is read in via the .pb file.

get_graph_ops/1:

Used for listing all the operations in a Tensorflow .pb graph.

Reads in a Tensorflex %Graph struct obtained from read_graph/1.

Returns a list of all the operation names (as strings) that populate the graph model.

create_matrix/3:

Creates a 2-D Tensorflex matrix from custom input specifications.

Takes three input arguments: number of rows in matrix (nrows), number of columns in matrix (ncols), and a list of lists of the data that will form the matrix (datalist).

Returns a %Matrix Tensorflex struct type.

matrix_pos/3:

Used for accessing an element of a Tensorflex matrix.

Takes in three input arguments: a Tensorflex %Matrix struct matrix, and the row (row) and column (col) values of the required element in the matrix. Both row and col here are NOT zero indexed.

Returns the value as float.

size_of_matrix/1:

Used for obtaining the size of a Tensorflex matrix.

Takes a Tensorflex %Matrix struct matrix as input.

Returns a tuple {nrows, ncols} where nrows represents the number of rows of the matrix and ncols represents the number of columns of the matrix.

append_to_matrix/2:

Appends a single row to the back of a Tensorflex matrix.

Takes a Tensorflex %Matrix matrix as input and a single row of data (with the same number of columns as the original matrix) as a list of lists (datalist) to append to the original matrix.

Returns the extended and modified %Matrix struct matrix.

matrix_to_lists/1:

Converts a Tensorflex matrix (back) to a list of lists format.

Takes a Tensorflex %Matrix struct matrix as input.

Returns a list of lists representing the data stored in the matrix.

NOTE: If the matrix contains very high dimensional data, typically obtained from a function like load_csv_as_matrix/2, then it is not recommended to convert the matrix back to a list of lists format due to a possibility of memory errors.

float64_tensor/2, float32_tensor/2, int32_tensor/2:

Creates a TF_DOUBLE, TF_FLOAT, or TF_INT32 tensor from Tensorflex matrices containing the values and dimensions specified.

Takes two arguments: a %Matrix matrix (matrix1) containing the values the tensor should have and another %Matrix matrix (matrix2) containing the dimensions of the required tensor.

Returns a tuple {:ok, %Tensor} where %Tensor represents an internal Tensorflex struct type that is used for holding tensor data and type.

float64_tensor/1, float32_tensor/1, int32_tensor/1, string_tensor/1:

Creates a TF_DOUBLE, TF_FLOAT, TF_INT32, or TF_STRING constant value one-dimensional tensor from the input value specified.

Takes in a float, int or string value (depending on function) as input.

Returns a tuple {:ok, %Tensor} where %Tensor represents an internal Tensorflex struct type that is used for holding tensor data and type.

float64_tensor_alloc/1, float32_tensor_alloc/1, int32_tensor_alloc/1:

Allocates a TF_DOUBLE, TF_FLOAT, or TF_INT32 tensor of specified dimensions.

This function is generally used to allocate output tensors that do not hold any value data yet, but will after the session is run for Inference. Output tensors of the required dimensions are allocated and then passed to the run_session/5 function to hold the output values generated as predictions.

Takes a Tensorflex %Matrix struct matrix as input.

Returns a tuple {:ok, %Tensor} where %Tensor represents an internal Tensorflex struct type that is used for holding the potential tensor data and type.

tensor_datatype/1:

Used to get the datatype of a created tensor.

Takes in a %Tensor struct tensor as input.

Returns a tuple {:ok, datatype} where datatype is an atom representing the list of Tensorflow TF_DataType tensor datatypes. Click here to view a list of all possible datatypes.

load_image_as_tensor/1:

Loads JPEG images into Tensorflex directly as a TF_UINT8 tensor of dimensions image height x image width x number of color channels.

This function is very useful if you wish to do image classification using Convolutional Neural Networks, or other Deep Learning Models. One of the most widely adopted and robust image classification models is the Inception model by Google. It makes classifications on images from over a 1000 classes with highly accurate results. The load_image_as_tensor/1 function is an essential component for the prediction pipeline of the Inception model (and for other similar image classification models) to work in Tensorflex.

Reads in the path to a JPEG image file (.jpg or .jpeg).

Returns a tuple {:ok, %Tensor} where %Tensor represents an internal Tensorflex struct type that is used for holding the tensor data and type. Here the created Tensor is a uint8 tensor (TF_UINT8).

NOTE: For now, only 3 channel RGB JPEG color images can be passed as arguments. Support for grayscale images and other image formats such as PNG will be added in the future.

loads_csv_as_matrix/2:

Loads high-dimensional data from a CSV file as a Tensorflex 2-D matrix in a super-fast manner.

The load_csv_as_matrix/2 function is very fast-- when compared with the Python based pandas library for data science and analysis' function read_csv on the test.csv file from MNIST Kaggle data (source), the following execution times were obtained:

read_csv: 2.549233 seconds
load_csv_as_matrix/2: 1.711494 seconds

This function takes in 2 arguments: a path to a valid CSV file (filepath) and other optional arguments opts. These include whether or not a header needs to be discarded in the CSV, and what the delimiter type is. These are specified by passing in an atom :true or :false to the header: key, and setting a string value for the delimiter: key. By default, the header is considered to be present (:true) and the delimiter is set to ,.

Returns a %Matrix Tensorflex struct type.

run_session/5:

Runs a Tensorflow session to generate predictions for a given graph, input data, and required input/output operations.

This function is the final step of the Inference (prediction) pipeline and generates output for a given set of input data, a pre-trained graph model, and the specified input and output operations of the graph.

Takes in five arguments: a pre-trained Tensorflow graph .pb model read in from the read_graph/1 function (graph), an input tensor with the dimensions and data required for the input operation of the graph to run (tensor1), an output tensor allocated with the right dimensions (tensor2), the name of the input operation of the graph that needs where the input data is fed (input_opname), and the output operation name in the graph where the outputs are obtained (output_opname). The input tensor is generally created from the matrices manually or using the load_csv_as_matrix/2 function, and then passed through to one of the tensor creation functions. For image classification the load_image_as_tensor/1 can also be used to create the input tensor from an image. The output tensor is created using the tensor allocation functions (generally containing alloc at the end of the function name).

Returns a List of Lists (similar to the matrix_to_lists/1 function) containing the generated predictions as per the output tensor dimensions.

add_scalar_to_matrix/2:

Adds scalar value to matrix.

Takes two arguments: %Matrix matrix and scalar value (int or float)

Returns a %Matrix modified matrix.

subtract_scalar_from_matrix/2:

Subtracts scalar value from matrix.

Takes two arguments: %Matrix matrix and scalar value (int or float)

Returns a %Matrix modified matrix.

multiply_matrix_with_scalar/2:

Multiplies scalar value with matrix.

Takes two arguments: %Matrix matrix and scalar value (int or float)

Returns a %Matrix modified matrix.

divide_matrix_by_scalar/2:

Divides matrix values by scalar.

Takes two arguments: %Matrix matrix and scalar value (int or float)

Returns a %Matrix modified matrix.

add_matrices/2:

Adds two matrices of same dimensions together.

Takes in two %Matrix matrices as arguments.

Returns the resultant %Matrix matrix.

subtract_matrices/2:

Subtracts matrix2 from matrix1.

Takes in two %Matrix matrices as arguments.

Returns the resultant %Matrix matrix.

tensor_to_matrix/1:

Converts the data stored in a 2-D tensor back to a 2-D matrix.

Takes in a single argument as a %Tensor tensor (any TF_Datatype).

Returns a %Matrix 2-D matrix.

NOTE: Tensorflex doesn't currently support 3-D matrices, and therefore tensors that are 3-D (such as created using the load_image_as_tensor/1 function) cannot be converted back to a matrix, yet. Support for 3-D matrices will be added soon.

Examples

Examples are generally added in full description on my blog here. A blog post covering how to do classification on the Iris Dataset is present here.

INCEPTION CNN MODEL EXAMPLE:

Here we will briefly touch upon how to use the Google V3 Inception pre-trained graph model to do image classficiation from over a 1000 classes. First, the Inception V3 model can be downloaded here: http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz

After unzipping, see that it contains the graphdef .pb file (classify_image_graphdef.pb) which contains our graph definition, a test jpeg image that should identify/classify as a panda (cropped_panda.pb) and a few other files I will detail later.

Now for running this in Tensorflex first the graph is loaded:

iex(1)> {:ok, graph} = Tensorflex.read_graph("classify_image_graph_def.pb")
2018-07-29 00:48:19.849870: W tensorflow/core/framework/op_def_util.cc:346] Op BatchNormWithGlobalNormalization is deprecated. It will cease to work in GraphDef version 9. Use tf.nn.batch_normalization().
{:ok,
 %Tensorflex.Graph{
   def: #Reference<0.2597534446.2498625538.211058>,
   name: "classify_image_graph_def.pb"
 }}

Then the cropped_panda image is loaded using the new load_image_as_tensor function:

iex(2)> {:ok, input_tensor} = Tensorflex.load_image_as_tensor("cropped_panda.jpg")
{:ok,
 %Tensorflex.Tensor{
   datatype: :tf_uint8,
   tensor: #Reference<0.2597534446.2498625538.211093>
 }}

Then create the output tensor which will hold out output vector values. For the inception model, the output is received as a 1008x1 tensor, as there are 1008 classes in the model:

iex(3)> out_dims = Tensorflex.create_matrix(1,2,[[1008,1]])
%Tensorflex.Matrix{
  data: #Reference<0.2597534446.2498625538.211103>,
  ncols: 2,
  nrows: 1
}

iex(4)> {:ok, output_tensor} = Tensorflex.float32_tensor_alloc(out_dims)
{:ok,
 %Tensorflex.Tensor{
   datatype: :tf_float,
   tensor: #Reference<0.2597534446.2498625538.211116>
 }}

Then the output results are read into a list called results. Also, the input operation in the Inception model is DecodeJpeg and the output operation is softmax:

iex(5)> results = Tensorflex.run_session(graph, input_tensor, output_tensor, "DecodeJpeg", "softmax")
2018-07-29 00:51:13.631154: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
[
  [1.059142014128156e-4, 2.8240500250831246e-4, 8.30648496048525e-5,
   1.2982363114133477e-4, 7.32232874725014e-5, 8.014426566660404e-5,
   6.63459359202534e-5, 0.003170756157487631, 7.931600703159347e-5,
   3.707312498590909e-5, 3.0997329304227605e-5, 1.4232713147066534e-4,
   1.0381334868725389e-4, 1.1057958181481808e-4, 1.4321311027742922e-4,
   1.203602587338537e-4, 1.3130248407833278e-4, 5.850398520124145e-5,
   2.641105093061924e-4, 3.1629020668333396e-5, 3.906813799403608e-5,
   2.8646905775531195e-5, 2.2863158665131778e-4, 1.2222197256051004e-4,
   5.956588938715868e-5, 5.421260357252322e-5, 5.996063555357978e-5,
   4.867801326327026e-4, 1.1005574924638495e-4, 2.3433618480339646e-4,
   1.3062104699201882e-4, 1.317620772169903e-4, 9.388553007738665e-5,
   7.076268957462162e-5, 4.281177825760096e-5, 1.6863139171618968e-4,
   9.093972039408982e-5, 2.611844101920724e-4, 2.7584232157096267e-4,
   5.157176201464608e-5, 2.144951868103817e-4, 1.3628098531626165e-4,
   8.007588621694595e-5, 1.7929042223840952e-4, 2.2831936075817794e-4,
   6.216531619429588e-5, 3.736453436431475e-5, 6.782123091397807e-5,
   1.1538144462974742e-4, ...]
]

Finally, we need to find which class has the maximum probability and identify it's label. Since results is a List of Lists, it's better to read in the nested list. Then we need to find the index of the element in the new list which as the maximum value. Therefore:

iex(6)> max_prob = List.flatten(results) |> Enum.max
0.8849328756332397

iex(7)> Enum.find_index(results |> List.flatten, fn(x) -> x == max_prob end)
169

We can thus see that the class with the maximum probability predicted (0.8849328756332397) for the image is 169. We will now find what the 169 label corresponds to. For this we can look back into the unzipped Inception folder, where there is a file called imagenet_2012_challenge_label_map_proto.pbtxt. On opening this file, we can find the string class identifier for the 169 class index. This is n02510455 and is present on Line 1556 in the file. Finally, we need to match this string identifier to a set of identification labels by referring to the file imagenet_synset_to_human_label_map.txt file. Here we can see that corresponding to the string class n02510455 the human labels are giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca (Line 3691 in the file).

Thus, we have correctly identified the animal in the image as a panda using Tensorflex!

RNN LSTM SENTIMENT ANALYSIS MODEL EXAMPLE:

A brief idea of what this example entails:

The Recurrent Neural Network utilizes Long-Short-Term-Memory (LSTM) cells for holding the state for the data flowing in through the network
In this example, we utilize the LSTM network for sentiment analysis on movie reviews data in Tensorflex. The trained models are originally created as part of an online tutorial (source) and are present in a Github repository here.

To do sentiment analysis in Tensorflex however, we first need to do some preprocessing and prepare the graph model (.pb) as done multiple times before in other examples. For that, in the examples/rnn-lstm-example directory there are two scripts: freeze.py and create_input_data.py. Prior to explaining the working of these scripts you first need to download the original saved models as well as the datasets:

For the model, download from here and then store all the 4 model files in the examples/rnn-lstm-example/model folder
For the dataset, download from here. After decompressing, we do not need all the files, just the 2 numpy binaries wordsList.npy and wordVectors.npy. These will be used to encode our text data into UTF-8 encoding for feeding our RNN as input.

Now, for the Python two scripts: freeze.py and create_input_data.py:

freeze.py: This is used to create our pb model from the Python saved checkpoints. Here we will use the downloaded Python checkpoints' model to create the .pb graph. Just running python freeze.py after putting the model files in the correct directory will do the trick. In the same ./model/ folder, you will now see a file called frozen_model_lstm.pb. This is the file which we will load into Tensorflex. In case for some reason you want to skip this step and just get the loaded graph here is a Dropbox link
create_input_data.py: Even if we can load our model into Tensorflex, we also need some data to do inference on. For that, we will write our own example sentences and convert them (read encode) to a numeral (int32) format that can be used by the network as input. For that, you can inspect the code in the script to get an understanding of what is happening. Basically, the neural network takes in an input of a 24x250 int32 (matrix) tensor created from text which has been encoded as UTF-8. Again, running python create_input_data.py will give you two csv files (one indicating positive sentiment and the other a negative sentiment) which we will later load into Tensorflex. The two sentences converted are:
- Negative sentiment sentence: That movie was terrible.
- Positive sentiment sentence: That movie was the best one I have ever seen.

Both of these get converted to two files inputMatrixPositive.csv and inputMatrixNegative.csv (by create_input_data.py) which we load into Tensorflex next.

Inference in Tensorflex: Now we do sentiment analysis in Tensorflex. A few things to note:

The input graph operation is named Placeholder_1
The output graph operation is named add and is the eventual result of a matrix multiplication. Of this obtained result we only need the first row
Here the input is going to be a integer valued matrix tensor of dimensions 24x250 representing our sentence/review
The output will have 2 columns, as there are 2 classes-- for positive and negative sentiment respectively. Since we will only be needing only the first row we will get our result in a 1x2 vector. If the value of the first column is higher than the second column, then the network indicates a positive sentiment otherwise a negative sentiment. All this can be observed in the original repository in a Jupyter notebook here: ```elixir iex(1)> {:ok, graph} = Tensorflex.read_graph "examples/rnn-lstm-example/model/frozen_model_lstm.pb" {:ok, %Tensorflex.Graph{ def: #Reference<0.713975820.1050542081.11558>, name: "examples/rnn-lstm-example/model/frozen_model_lstm.pb" }}

iex(2)> Tensorflex.get_graph_ops graph ["Placeholder_1", "embedding_lookup/params_0", "embedding_lookup", "transpose/perm", "transpose", "rnn/Shape", "rnn/strided_slice/stack", "rnn/strided_slice/stack_1", "rnn/strided_slice/stack_2", "rnn/strided_slice", "rnn/stack/1", "rnn/stack", "rnn/zeros/Const", "rnn/zeros", "rnn/stack_1/1", "rnn/stack_1", "rnn/zeros_1/Const", "rnn/zeros_1", "rnn/Shape_1", "rnn/strided_slice_2/stack", "rnn/strided_slice_2/stack_1", "rnn/strided_slice_2/stack_2", "rnn/strided_slice_2", "rnn/time", "rnn/TensorArray", "rnn/TensorArray_1", "rnn/TensorArrayUnstack/Shape", "rnn/TensorArrayUnstack/strided_slice/stack", "rnn/TensorArrayUnstack/strided_slice/stack_1", "rnn/TensorArrayUnstack/strided_slice/stack_2", "rnn/TensorArrayUnstack/strided_slice", "rnn/TensorArrayUnstack/range/start", "rnn/TensorArrayUnstack/range/delta", "rnn/TensorArrayUnstack/range", "rnn/TensorArrayUnstack/TensorArrayScatter/TensorArrayScatterV3", "rnn/while/Enter", "rnn/while/Enter_1", "rnn/while/Enter_2", "rnn/while/Enter_3", "rnn/while/Merge", "rnn/while/Merge_1", "rnn/while/Merge_2", "rnn/while/Merge_3", "rnn/while/Less/Enter", "rnn/while/Less", "rnn/while/LoopCond", "rnn/while/Switch", "rnn/while/Switch_1", "rnn/while/Switch_2", "rnn/while/Switch_3", ...]

First we will try for positive sentiment:
```elixir
iex(3)> input_vals = Tensorflex.load_csv_as_matrix("examples/rnn-lstm-example/inputMatrixPositive.csv", header: :false)
%Tensorflex.Matrix{
  data: #Reference<0.713975820.1050542081.13138>,
  ncols: 250,
  nrows: 24
}

iex(4)> input_dims = Tensorflex.create_matrix(1,2,[[24,250]])
%Tensorflex.Matrix{
  data: #Reference<0.713975820.1050542081.13575>,
  ncols: 2,
  nrows: 1
}

iex(5)> {:ok, input_tensor} = Tensorflex.int32_tensor(input_vals, input_dims)
{:ok,
 %Tensorflex.Tensor{
   datatype: :tf_int32,
   tensor: #Reference<0.713975820.1050542081.14434>
 }}

iex(6)> output_dims = Tensorflex.create_matrix(1,2,[[24,2]])
%Tensorflex.Matrix{
  data: #Reference<0.713975820.1050542081.14870>,
  ncols: 2,
  nrows: 1
}

iex(7)> {:ok, output_tensor} = Tensorflex.float32_tensor_alloc(output_dims)
{:ok,
 %Tensorflex.Tensor{
   datatype: :tf_float,
   tensor: #Reference<0.713975820.1050542081.15363>
 }}

We only need the first row, the rest do not indicate anything:

iex(8)> [result_pos | _ ] = Tensorflex.run_session(graph, input_tensor,output_tensor, "Placeholder_1", "add")
[
  [4.483788013458252, -1.273943305015564],
  [-0.17151066660881042, -2.165886402130127],
  [0.9569928646087646, -1.131581425666809],
  [0.5669126510620117, -1.3842089176177979],
  [-1.4346938133239746, -4.0750861167907715],
  [0.4680981934070587, -1.3494354486465454],
  [1.068990707397461, -2.0195648670196533],
  [3.427264451980591, 0.48857203125953674],
  [0.6307879686355591, -2.069119691848755],
  [0.35061028599739075, -1.700657844543457],
  [3.7612719535827637, 2.421398878097534],
  [2.7635951042175293, -0.7214710116386414],
  [1.146680235862732, -0.8688814640045166],
  [0.8996094465255737, -1.0183486938476563],
  [0.23605018854141235, -1.893072247505188],
  [2.8790698051452637, -0.37355837225914],
  [-1.7325369119644165, -3.6470277309417725],
  [-1.687785029411316, -4.903762340545654],
  [3.6726789474487305, 0.14170047640800476],
  [0.982108473777771, -1.554244875907898],
  [2.248904228210449, 1.0617655515670776],
  [0.3663095533847809, -3.5266385078430176],
  [-1.009346604347229, -2.901120901107788],
  [3.0659966468811035, -1.7605335712432861]
]

iex(9)> result_pos
[4.483788013458252, -1.273943305015564]

Thus we can clearly see that the RNN predicts a positive sentiment. For a negative sentiment, next:

iex(10)> input_vals = Tensorflex.load_csv_as_matrix("examples/rnn-lstm-example/inputMatrixNegative.csv", header: :false)
%Tensorflex.Matrix{
  data: #Reference<0.713975820.1050542081.16780>,
  ncols: 250,
  nrows: 24
}

iex(11)> {:ok, input_tensor} = Tensorflex.int32_tensor(input_vals,input_dims)
{:ok,              
 %Tensorflex.Tensor{
   datatype: :tf_int32,
   tensor: #Reference<0.713975820.1050542081.16788>
 }}

iex(12)> [result_neg|_] = Tensorflex.run_session(graph, input_tensor,output_tensor, "Placeholder_1", "add")
[
  [0.7635725736618042, 10.895986557006836],
  [2.205151319503784, -0.6267685294151306],
  [3.5995595455169678, -0.1240251287817955],
  [-1.6063352823257446, -3.586883068084717],
  [1.9608432054519653, -3.084211826324463],
  [3.772461414337158, -0.19421455264091492],
  [3.9185996055603027, 0.4442034661769867],
  [3.010765552520752, -1.4757057428359985],
  [3.23650860786438, -0.008513949811458588],
  [2.263028144836426, -0.7358709573745728],
  [0.206748828291893, -2.1945853233337402],
  [2.913491725921631, 0.8632720708847046],
  [0.15935257077217102, -2.9757845401763916],
  [-0.7757357358932495, -2.360766649246216],
  [3.7359719276428223, -0.7668198347091675],
  [2.2896337509155273, -0.45704856514930725],
  [-1.5497230291366577, -4.42919921875],
  [-2.8478822708129883, -5.541027545928955],
  [1.894787073135376, -0.8441318273544312],
  [0.15720489621162415, -2.699129819869995],
  [-0.18114641308784485, -2.988100051879883],
  [3.342879056930542, 2.1714375019073486],
  [2.906526565551758, 0.18969044089317322],
  [0.8568912744522095, -1.7559258937835693]
]
iex(13)> result_neg
[0.7635725736618042, 10.895986557006836]

Thus we can clearly see that in this case the RNN indicates negative sentiment! Our model works!

Pull Requests Made

In chronological order:

Author: anshuman23
Source code: https://github.com/anshuman23/tensorflex
License: Apache-2.0 license

#elixir #tensorflow #machine-learning

Einar Hintz

1602560783

jQuery Ajax CRUD in ASP.NET Core MVC with Modal Popup

In this article, we’ll discuss how to use jQuery Ajax for ASP.NET Core MVC CRUD Operations using Bootstrap Modal. With jQuery Ajax, we can make HTTP request to controller action methods without reloading the entire page, like a single page application.

To demonstrate CRUD operations – insert, update, delete and retrieve, the project will be dealing with details of a normal bank transaction. GitHub repository for this demo project : https://bit.ly/33KTJAu.

Sub-topics discussed :

Form design for insert and update operation.
Display forms in modal popup dialog.
Form post using jQuery Ajax.
Implement MVC CRUD operations with jQuery Ajax.
Loading spinner in .NET Core MVC.
Prevent direct access to MVC action method.

Create ASP.NET Core MVC Project

In Visual Studio 2019, Go to File > New > Project (Ctrl + Shift + N).

From new project window, Select Asp.Net Core Web Application_._

Image showing how to create ASP.NET Core Web API project in Visual Studio.

Once you provide the project name and location. Select Web Application(Model-View-Controller) and uncheck HTTPS Configuration. Above steps will create a brand new ASP.NET Core MVC project.

Showing project template selection for .NET Core MVC.

Setup a Database

Let’s create a database for this application using Entity Framework Core. For that we’ve to install corresponding NuGet Packages. Right click on project from solution explorer, select Manage NuGet Packages_,_ From browse tab, install following 3 packages.

Showing list of NuGet Packages for Entity Framework Core

Now let’s define DB model class file – /Models/TransactionModel.cs.

public class TransactionModel
{
    [Key]
    public int TransactionId { get; set; }

    [Column(TypeName ="nvarchar(12)")]
    [DisplayName("Account Number")]
    [Required(ErrorMessage ="This Field is required.")]
    [MaxLength(12,ErrorMessage ="Maximum 12 characters only")]
    public string AccountNumber { get; set; }

    [Column(TypeName ="nvarchar(100)")]
    [DisplayName("Beneficiary Name")]
    [Required(ErrorMessage = "This Field is required.")]
    public string BeneficiaryName { get; set; }

    [Column(TypeName ="nvarchar(100)")]
    [DisplayName("Bank Name")]
    [Required(ErrorMessage = "This Field is required.")]
    public string BankName { get; set; }

    [Column(TypeName ="nvarchar(11)")]
    [DisplayName("SWIFT Code")]
    [Required(ErrorMessage = "This Field is required.")]
    [MaxLength(11)]
    public string SWIFTCode { get; set; }

    [DisplayName("Amount")]
    [Required(ErrorMessage = "This Field is required.")]
    public int Amount { get; set; }

    [DisplayFormat(DataFormatString = "{0:MM/dd/yyyy}")]
    public DateTime Date { get; set; }
}

C#Copy

Here we’ve defined model properties for the transaction with proper validation. Now let’s define DbContextclass for EF Core.

#asp.net core article #asp.net core #add loading spinner in asp.net core #asp.net core crud without reloading #asp.net core jquery ajax form #asp.net core modal dialog #asp.net core mvc crud using jquery ajax #asp.net core mvc with jquery and ajax #asp.net core popup window #bootstrap modal popup in asp.net core mvc. bootstrap modal popup in asp.net core #delete and viewall in asp.net core #jquery ajax - insert #jquery ajax form post #modal popup dialog in asp.net core #no direct access action method #update #validation in modal popup

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