This example is going to be incorporated into a more detailed blog post, but her…e is a brief idea:
- 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)](https://www.oreilly.com/learning/perform-sentiment-analysis-with-lstms-using-tensorflow) and are present in a Github repository [here](https://github.com/adeshpande3/LSTM-Sentiment-Analysis).
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](https://github.com/adeshpande3/LSTM-Sentiment-Analysis/raw/master/models.tar.gz) and then store all the 4 model files in the `examples/rnn-lstm-example/model` folder
- For the dataset, download from [here](https://github.com/adeshpande3/LSTM-Sentiment-Analysis/raw/master/training_data.tar.gz). 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](https://www.dropbox.com/s/xp1bphy0k40v5r6/frozen_model_lstm.pb?dl=0)
- `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](https://github.com/adeshpande3/LSTM-Sentiment-Analysis):
```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:
```elixir
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:
```elixir
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 :D
