Add data and retrain model with new keywords

README.md

@@ -84,7 +84,7 @@ For local development, you can run the server by doing `python server.py`, which
 
 ### Training the NLP Model
 
-You can train a new version of the neural network on the `data/survey.csv` data by doing `python train.py`. This will begin training of a basic 64 cell LSTM model (which is defined in `net.py`). You can configure the training parameters which are constants at the top of `train.py`.
+You can train a new version of the neural network on the `data/cumulative.csv` data by doing `python train.py`. This will begin training of a basic 64 cell LSTM model (which is defined in `net.py`). You can configure the training parameters which are constants at the top of `train.py`.
 
 ```python
 TOKENIZER_VOCAB_SIZE = 500 # Vocabulary size of the tokenizer

nlp/data/cumulative.csv

@@ -1259,4 +1259,28 @@ https://twitter.com/Adobe,taking a break,yes,2020-03-31 17:05:00 +0000 UTC
 ,procrastinate on my chinese school hw,no
 ,check the side effects of a medication,yes
 ,get to know the people im going to school with enxt year,yes
-,reach my friends new article,yes
+,reach my friends new article,yes
+,watch tutorials on knitting,yes
+,focusing on my social and emotional wellbeing,yes
+,check on my friend’s progress,yes
+,trying to fix my broken computer,yes
+,getting some inspo for decorating my house,yes
+,doing some gift shopping for the holidays,yes
+,check when the restaurant opens,yes
+,discovering ways to take care of my dog,yes
+,learning to tie a tie,yes
+,read self-help guides,yes
+,managing stress,yes
+,taking care of my finances,yes
+,find some fitness videos,yes
+,add something to my resume,yes
+,looking for some new webtoons,no
+,getting away from homework to procastinate later,no
+,idk im just bored,no
+,getting ready for my next class,yes
+,look at some fashion trends,no
+,browse cool website designs,no
+,looking at tech twitter stuff,no
+,get some nfts,no
+,buy some crypto,no
+,keep track of my stocks,no

nlp/data_proc.py

@@ -13,7 +13,7 @@
 	"havent": "have not",
 	"doesnt": "does not",
 	"he'll": "he will",
-	"she'll": "she will"	
+	"she'll": "she will"
 }
 
 # remove punctuation from string

nlp/models/acc82.49/details.yml

@@ -0,0 +1,7 @@
+BATCH_SIZE: 128
+NUM_EPOCHS: 20
+SEQUENCE_MAX_LENGTH: 35
+TOKENIZER_VOCAB_SIZE: 1000
+TRAINED_AT: 2021-10-31 18:21:05.743503
+TRAIN_TEST_SPLIT: 0.2
+VALIDATION_SPLIT: 0.2

nlp/models/acc82.49/model.json

@@ -0,0 +1 @@
+{"class_name": "Functional", "config": {"name": "model", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 35], "dtype": "float32", "sparse": false, "ragged": false, "name": "inputs"}, "name": "inputs", "inbound_nodes": []}, {"class_name": "Embedding", "config": {"name": "embedding", "trainable": true, "batch_input_shape": [null, 35], "dtype": "float32", "input_dim": 1000, "output_dim": 128, "embeddings_initializer": {"class_name": "RandomUniform", "config": {"minval": -0.05, "maxval": 0.05, "seed": null}}, "embeddings_regularizer": null, "activity_regularizer": null, "embeddings_constraint": null, "mask_zero": false, "input_length": 35}, "name": "embedding", "inbound_nodes": [[["inputs", 0, 0, {}]]]}, {"class_name": "LSTM", "config": {"name": "lstm", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 128, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 3}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}, "shared_object_id": 4}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 5}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}, "name": "lstm", "inbound_nodes": [[["embedding", 0, 0, {}]]]}, {"class_name": "Dropout", "config": {"name": "dropout", "trainable": true, "dtype": "float32", "rate": 0.5, "noise_shape": null, "seed": null}, "name": "dropout", "inbound_nodes": [[["lstm", 0, 0, {}]]]}, {"class_name": "LSTM", "config": {"name": "lstm_1", "trainable": true, "dtype": "float32", "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 128, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 9}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 11}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}, "name": "lstm_1", "inbound_nodes": [[["dropout", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "FC1", "trainable": true, "dtype": "float32", "units": 256, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "FC1", "inbound_nodes": [[["lstm_1", 0, 0, {}]]]}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "dtype": "float32", "rate": 0.5, "noise_shape": null, "seed": null}, "name": "dropout_1", "inbound_nodes": [[["FC1", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "out_layer", "trainable": true, "dtype": "float32", "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "out_layer", "inbound_nodes": [[["dropout_1", 0, 0, {}]]]}, {"class_name": "Activation", "config": {"name": "activation", "trainable": true, "dtype": "float32", "activation": "sigmoid"}, "name": "activation", "inbound_nodes": [[["out_layer", 0, 0, {}]]]}], "input_layers": [["inputs", 0, 0]], "output_layers": [["activation", 0, 0]]}, "keras_version": "2.5.0", "backend": "tensorflow"}

nlp/net.py

@@ -1,10 +1,10 @@
-from keras.layers import Activation
-from keras.layers import Dense
-from keras.layers import Dropout
-from keras.layers import Embedding
-from keras.layers import Input
-from keras.layers import LSTM
-from keras.models import Model
+from tensorflow.keras.layers import Activation
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.layers import Dropout
+from tensorflow.keras.layers import Embedding
+from tensorflow.keras.layers import Input
+from tensorflow.keras.layers import LSTM
+from tensorflow.keras.models import Model
 
 # Define RNN Architecture
 def RNN(max_seq_len, vocab_size):

nlp/train.py

@@ -8,11 +8,11 @@
 import datetime
 
 # -- Deep Learning Libraries --
-from keras.callbacks import EarlyStopping
-from keras.optimizers import RMSprop
-from keras.preprocessing import sequence
-from keras.preprocessing.text import Tokenizer
-from keras.utils import to_categorical
+from tensorflow.keras.callbacks import EarlyStopping
+from tensorflow.keras.optimizers import RMSprop
+from tensorflow.keras.preprocessing import sequence
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.utils import to_categorical
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder