1595395680

Structured Query Language, or SQL, is a widely used language the allows users to query and manage data in a database. Databases such as MySQL, MariaDB, SQLite, PostgreSQL, Oracle, and Microsoft SQL Server are all based on the SQL standard, with some slight variations. This resource uses the MySQL flavor of SQL.

I’ve created an overview resource to quickly be able to reference the appropriate syntax for the most popular SQL commands, and code to use the PDO class in PHP to securely connect to and work with a database.

To see PHP and MySQL in action, view the Creating a Simple Database Application from Scratch tutorial - Part One: Create and Read and Part Two: Update and Delete.

You can view the commands alone without explanations on GitHub through the below link.

View on GitHub

The logo in this article is of Sequel Pro, an awesome free MySQL GUI for Mac.

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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

1620677280

This article will focus on writing SQL queries against the database reference table with a fairly simple structure to understand and implement.

Additionally, we are going to clarify the concepts behind writing effective SQL queries along with some professional life tips.

As this article is about querying database tables with the help of SQL scripts, readers should have a certain background to fully understand the concepts and examples. Also, the necessary equipment must be present:

You need:

- The basic knowledge of relational databases and SQL.
- An SQL database server installed locally or remotely.
- Database management tools such as SQL Server Management Studio or dbForge Studio for SQL Server.

You should be able to create a sample database (with the help of provided scripts), connect to the SQL Server, and run against that sample database.

#sql server #reference table #sql query #t-sql reference #sql

1596441660

When you develop large chunks of T-SQL code with the help of the SQL Server Management Studio tool, it is essential to test the “Live” behavior of your code by making sure that each small piece of code works fine and being able to allocate any error message that may cause a failure within that code.

The easiest way to perform that would be to use the T-SQL debugger feature, which used to be built-in over the SQL Server Management Studio tool. But since the T-SQL debugger feature was removed completely from SQL Server Management Studio 18 and later editions, we need a replacement for that feature. This is because we cannot keep using the old versions of SSMS just to support the T-SQL Debugger feature without “enjoying” the new features and bug fixes that are released in the new SSMS versions.

If you plan to wait for SSMS to bring back the T-SQL Debugger feature, vote in the Put Debugger back into SSMS 18 to ask Microsoft to reintroduce it.

As for me, I searched for an alternative tool for a T-SQL Debugger SSMS built-in feature and found that Devart company rolled out a new T-SQL Debugger feature to version 6.4 of SQL – Complete tool. SQL Complete is an add-in for Visual Studio and SSMS that offers scripts autocompletion capabilities, which help develop and debug your SQL database project.

The SQL Debugger feature of SQL Complete allows you to check the execution of your scripts, procedures, functions, and triggers step by step by adding breakpoints to the lines where you plan to start, suspend, evaluate, step through, and then to continue the execution of your script.

You can download SQL Complete from the dbForge Download page and install it on your machine using a straight-forward installation wizard. The wizard will ask you to specify the installation path for the SQL Complete tool and the versions of SSMS and Visual Studio that you plan to install the SQL Complete on, as an add-in, from the versions that are installed on your machine, as shown below:

Once SQL Complete is fully installed on your machine, the dbForge SQL Complete installation wizard will notify you of whether the installation was completed successfully or the wizard faced any specific issue that you can troubleshoot and fix easily. If there are no issues, the wizard will provide you with an option to open the SSMS tool and start using the SQL Complete tool, as displayed below:

When you open SSMS, you will see a new “Debug” tools menu, under which you can navigate the SQL Debugger feature options. Besides, you will see a list of icons that will be used to control the debug mode of the T-SQL query at the leftmost side of the SSMS tool. If you cannot see the list, you can go to View -> Toolbars -> Debugger to make these icons visible.

During the debugging session, the SQL Debugger icons will be as follows:

The functionality of these icons within the SQL Debugger can be summarized as:

- Adding
**Breakpoints**to control the execution pause of the T-SQL script at a specific statement allows you to check the debugging information of the T-SQL statements such as the values for the parameters and the variables. **Step Into**is “navigate” through the script statements one by one, allowing you to check how each statement behaves.**Step Over**is “execute” a specific stored procedure if you are sure that it contains no error.**Step Out**is “return” from the stored procedure, function, or trigger to the main debugging window.**Continue**executing the script until reaching the next breakpoint.**Stop Debugging**is “terminate” the debugging session.**Restart**“stop and start” the current debugging session.

#sql server #sql #sql debugger #sql server #sql server stored procedure #ssms #t-sql queries

1596448980

Let’s say the chief credit and collections officer asks you to list down the names of people, their unpaid balances per month, and the current running balance and wants you to import this data array into Excel. The purpose is to analyze the data and come up with an offer making payments lighter to mitigate the effects of the COVID19 pandemic.

Do you opt to use a query and a nested subquery or a join? What decision will you make?

Before we do a deep dive into syntax, performance impact, and caveats, why not define a subquery first?

In the simplest terms, a subquery is a query within a query. While a query that embodies a subquery is the outer query, we refer to a subquery as the inner query or inner select. And parentheses enclose a subquery similar to the structure below:

```
SELECT
col1
,col2
,(subquery) as col3
FROM table1
[JOIN table2 ON table1.col1 = table2.col2]
WHERE col1 <operator> (subquery)
```

We are going to look upon the following points in this post:

- SQL subquery syntax depending on different subquery types and operators.
- When and in what sort of statements one can use a subquery.
- Performance implications vs.
**JOINs**. - Common caveats when using SQL subqueries.

As is customary, we provide examples and illustrations to enhance understanding. But bear in mind that the main focus of this post is on subqueries in SQL Server.

Now, let’s get started.

For one thing, subqueries are categorized based on their dependency on the outer query.

Let me describe what a self-contained subquery is.

Self-contained subqueries (or sometimes referred to as non-correlated or simple subqueries) are independent of the tables in the outer query. Let me illustrate this:

```
-- Get sales orders of customers from Southwest United States
-- (TerritoryID = 4)
USE [AdventureWorks]
GO
SELECT CustomerID, SalesOrderID
FROM Sales.SalesOrderHeader
WHERE CustomerID IN (SELECT [CustomerID]
FROM [AdventureWorks].[Sales].[Customer]
WHERE TerritoryID = 4)
```

As demonstrated in the above code, the subquery (enclosed in parentheses below) has no references to any column in the outer query. Additionally, you can highlight the subquery in SQL Server Management Studio and execute it without getting any runtime errors.

Which, in turn, leads to easier debugging of self-contained subqueries.

The next thing to consider is correlated subqueries. Compared to its self-contained counterpart, this one has at least one column being referenced from the outer query. To clarify, I will provide an example:

```
USE [AdventureWorks]
GO
SELECT DISTINCT a.LastName, a.FirstName, b.BusinessEntityID
FROM Person.Person AS p
JOIN HumanResources.Employee AS e ON p.BusinessEntityID = e.BusinessEntityID
WHERE 1262000.00 IN
(SELECT [SalesQuota]
FROM Sales.SalesPersonQuotaHistory spq
WHERE p.BusinessEntityID = spq.BusinessEntityID)
```

Were you attentive enough to notice the reference to ** BusinessEntityID** from the

Once a column from the outer query is referenced in the subquery, it becomes a correlated subquery. One more point to consider: if you highlight a subquery and execute it, an error will occur.

And yes, you are absolutely right: this makes correlated subqueries pretty harder to debug.

To make debugging possible, follow these steps:

- isolate the subquery.
- replace the reference to the outer query with a constant value.

Isolating the subquery for debugging will make it look like this:

```
SELECT [SalesQuota]
FROM Sales.SalesPersonQuotaHistory spq
WHERE spq.BusinessEntityID = <constant value>
```

Now, let’s dig a little deeper into the output of subqueries.

Well, first, let’s think of what returned values can we expect from SQL subqueries.

In fact, there are 3 possible outcomes:

- A single value
- Multiple values
- Whole tables

Let’s start with single-valued output. This type of subquery can appear anywhere in the outer query where an expression is expected, like the **WHERE** clause.

```
-- Output a single value which is the maximum or last TransactionID
USE [AdventureWorks]
GO
SELECT TransactionID, ProductID, TransactionDate, Quantity
FROM Production.TransactionHistory
WHERE TransactionID = (SELECT MAX(t.TransactionID)
FROM Production.TransactionHistory t)
```

When you use a **MAX**() function, you retrieve a single value. That’s exactly what happened to our subquery above. Using the equal (**=**) operator tells SQL Server that you expect a single value. Another thing: if the subquery returns multiple values using the equals (**=**) operator, you get an **error,** similar to the one below:

```
Msg 512, Level 16, State 1, Line 20
Subquery returned more than 1 value. This is not permitted when the subquery follows =, !=, <, <= , >, >= or when the subquery is used as an expression.
```

Next, we examine the multi-valued output. This kind of subquery returns a list of values with a single column. Additionally, operators like **IN** and **NOT IN** will expect one or more values.

```
-- Output multiple values which is a list of customers with lastnames that --- start with 'I'
USE [AdventureWorks]
GO
SELECT [SalesOrderID], [OrderDate], [ShipDate], [CustomerID]
FROM Sales.SalesOrderHeader
WHERE [CustomerID] IN (SELECT c.[CustomerID] FROM Sales.Customer c
INNER JOIN Person.Person p ON c.PersonID = p.BusinessEntityID
WHERE p.lastname LIKE N'I%' AND p.PersonType='SC')
```

And last but not least, why not delve into whole table outputs.

```
-- Output a table of values based on sales orders
USE [AdventureWorks]
GO
SELECT [ShipYear],
COUNT(DISTINCT [CustomerID]) AS CustomerCount
FROM (SELECT YEAR([ShipDate]) AS [ShipYear], [CustomerID]
FROM Sales.SalesOrderHeader) AS Shipments
GROUP BY [ShipYear]
ORDER BY [ShipYear]
```

Have you noticed the **FROM** clause?

Instead of using a table, it used a subquery. This is called a derived table or a table subquery.

And now, let me present you some ground rules when using this sort of query:

- All columns in the subquery should have unique names. Much like a physical table, a derived table should have unique column names.
**ORDER BY**is not allowed unless**TOP**is also specified. That’s because the derived table represents a relational table where rows have no defined order.

In this case, a derived table has the benefits of a physical table. That’s why in our example, we can use **COUNT**() in one of the columns of the derived table.

That’s about all regarding subquery outputs. But before we get any further, you may have noticed that the logic behind the example for multiple values and others as well can also be done using a **JOIN**.

```
-- Output multiple values which is a list of customers with lastnames that start with 'I'
USE [AdventureWorks]
GO
SELECT o.[SalesOrderID], o.[OrderDate], o.[ShipDate], o.[CustomerID]
FROM Sales.SalesOrderHeader o
INNER JOIN Sales.Customer c on o.CustomerID = c.CustomerID
INNER JOIN Person.Person p ON c.PersonID = p.BusinessEntityID
WHERE p.LastName LIKE N'I%' AND p.PersonType = 'SC'
```

In fact, the output will be the same. But which one performs better?

Before we get into that, let me tell you that I have dedicated a section to this hot topic. We’ll examine it with complete execution plans and have a look at illustrations.

So, bear with me for a moment. Let’s discuss another way to place your subqueries.

#sql server #sql query #sql server #sql subqueries #t-sql statements #sql