1621310246

As a data scientist or a data engineer, working with *Git* is always a breeze when he/she is aware of all its core elements that are utilised under-the-hood by a *Git* operation like *git-clone or git-push. Although, core Git* operations have received sufficient hands-on attention from fellow enthusiasts, special interest is laid, here, on a core entity called

To understand *Git reference_s and their importance, let us consider the below* complex-structured_ remote repository

Illustrates a remote repository structure with five branches including the main branch. Image by author, made using diagrams.

and clone the above illustrated remote repository as below

```
$ git clone https://github.com/<git_username>/my-repo.git
```

As expected, the cloning operation results in a local repository with a default local *main* branch and a remote connection *origin*, see below

Illustrates the cloning operation of a remote repository with five branches including the main branch. Image by author, made using diagrams.

However, one thing to notice in the above cloning illustration is that, by default, the local repository *my_repo* on your local machine only contains a *main* branch. And, on closer observation in your local machine, the local directory *my_repo* will also only contain a copy of the files that are present in your remote *main* branch. Remote branches like _branch_1 _and its file contents are nowhere to be found on your local machine. Thus, leading to the question

#git #programming #data-science

1658181600

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

- 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"}
```

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 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.*

- Negative sentiment sentence:

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!

- In chronological order:
- PR #2: Renamed app to Tensorflex from TensorflEx
- PR #3: Added support for reading pretrained graph definition files
- PR #4: Merged all support functions into read_graph; returning atoms
- PR #6: Returning list of op names in get_graph_ops and extended error atoms to all TF error codes
- PR #7: Added tensor support for strings and for getting TF_DataType
- PR #8: Added matrix functions, numeral tensors, better returns & removed unnecessary POC code
- PR #9: Added freeze graph Python example
- PR #10: Tensors of TF_FLOAT and TF_DOUBLE type both supported
- PR #11: Tensor allocations and Tensorflow Session support added
- PR #12: Fixed issue regarding printing of outputs
- PR #13: Added another example for blog post
- PR #14: Added graph file check before loading
- PR #15: Added tests
- PR #16: Add linker flags for MacOS
- PR #18: Added image loading capabilities (as 3D uint8 Tensors)
- PR #19: Wrapped references around structs
- PR #20: Moved basic C error checking to Elixir
- PR #21: Added Keras example
- PR #22: Added "append" function for matrices
- PR #23: Added fast C based direct CSV-to-matrix functionality with options
- PR #24: Added TF_INT32 tensors and tensor allocators
- PR #25: Added RNN (LSTM) example
- PR #26: Added documentation
- PR #28: Added improved tests
- PR #29: Adding metadata to mix.exs
- PR #31: Update nifs.ex
- PR #32: Fixed indentation and corrected warnings
- PR #35: Added new matrix operations
- PR #36: Fixed bugs in C code
- PR #37: Added tensor_to_matrix/1 (with tests/docs)
- PR #38: Create CONTRIBUTING.md
- PR #39: Formatted C code

Author: anshuman23

Source code: https://github.com/anshuman23/tensorflex

License: Apache-2.0 license

1604109000

Git has become ubiquitous as the preferred version control system (VCS) used by developers. Using Git adds immense value especially for engineering teams where several developers work together since it becomes critical to have a system of integrating everyone’s code reliably.

But with every powerful tool, especially one that involves collaboration with others, it is better to establish conventions to follow lest we shoot ourselves in the foot.

At DeepSource, we’ve put together some guiding principles for our own team that make working with a VCS like Git easier. Here are 5 simple rules you can follow:

Oftentimes programmers working on something get sidetracked into doing *too many* things when working on one particular thing — like when you are trying to fix one particular bug and you spot another one, and you can’t resist the urge to fix that as well. And another one. Soon, it snowballs and you end up with so many changes all going together in one commit.

This is problematic, and it is better to keep commits as small and focused as possible for many reasons, including:

- It makes it easier for other people in the team to look at your change, making code reviews more efficient.
- If the commit has to be rolled back completely, it’s far easier to do so.
- It’s straightforward to track these changes with your ticketing system.

Additionally, it helps you mentally parse changes you’ve made using `git log`

.

#open source #git #git basics #git tools #git best practices #git tutorials #git commit

1597916460

There is no doubt that Git plays a significant role in software development. It allows developers to work on the same code base at the same time. Still, developers struggle for code quality. Why? They fail to follow git best practices. In this post, I will explain seven core best practices of Git and a Bonus Section.

Committing something to Git means that you have changed your code and want to save these changes as a new trusted version.

Version control systems will not limit you in how you commit your code.

- You can commit 1000 changes in one single commit.
- Commit all the dll and other dependencies
- Or you can check in broken code to your repository.

**But is it good? Not quite.**

Because you are compromising code quality, and it will take more time to review code. So overall, team productivity will be reduced. The best practice is to make an atomic commit.

When you do an atomic commit, you’re committing only one change. It might be across multiple files, but it’s one single change.

Many developers make some changes, then commit, then push. And I have seen many repositories with unwanted files like dll, pdf, etc.

You can ask two questions to yourself, before check-in your code into the repository

- Are you suppose to check-in all these files?
- Are they part of your source code?

You can simply use the .gitignore file to avoid unwanted files in the repository. If you are working on more then one repo, it’s easy to use a global .gitignore file (without adding or pushing). And .gitignore file adds clarity and helps you to keep your code clean. What you can commit, and it will automatically ignore the unwanted files like autogenerated files like .dll and .class, etc.

#git basics #git command #git ignore #git best practices #git tutorial for beginners #git tutorials

1601157360

Hello all, nowadays most of the development teams using GIT version control, some of you may have a requirement of mirroring your team’s git changes from one server to another Git server. This article will help you to achieve the Git mirroring between one server to another server.

I got one assignment wherein there will be 2 Git Servers, development will happen in one Git server and the changes should be synchronized to another Git server at regular intervals. But in my case, the complexity is both the servers are in different restricted network. So I have done the small experiment and it worked. And I am sharing the steps to you all in this article.

**Main GIT Server:** Let’s take our main git server is located in our office and can be accessed only in-office network.

**Mirror GIT Server: **The mirror server is located at the vendor/client-side, which can be accessible in a normal internet connection but not with our office network. Since the office proxy will block the outside URL’s.

#devops #git #git and github #git best practices #git cloning #git server

1617875220

In this short article, we’ll be exploring some quick git commands that can help us in digging through our repositories’ history of commits. We’ll look at

- git log
- git shortlog
- git show
- git rev-list

#git #git-log #git-commands #git-history #aws