R Notebook

Sys.setenv(TF_KERAS=1) 
reticulate::py_config()

python:         /home/ron/.local/share/r-miniconda/envs/r-reticulate/bin/python
libpython:      /home/ron/.local/share/r-miniconda/envs/r-reticulate/lib/libpython3.6m.so
pythonhome:     /home/ron/.local/share/r-miniconda/envs/r-reticulate:/home/ron/.local/share/r-miniconda/envs/r-reticulate
version:        3.6.13 | packaged by conda-forge | (default, Feb 19 2021, 05:36:01)  [GCC 9.3.0]
numpy:          /home/ron/.local/lib/python3.6/site-packages/numpy
numpy_version:  1.19.5

reticulate::py_module_available('keras_bert')

[1] TRUE

tensorflow::tf_version()

[1] ‘2.6’

pretrained_path = './bert_models/uncased_L-12_H-768_A-12'
config_path = file.path(pretrained_path, 'bert_config.json')
checkpoint_path = file.path(pretrained_path, 
                            'bert_model.ckpt')
vocab_path = file.path(pretrained_path, 'vocab.txt')

library(reticulate)
library(keras)
k_bert = import('keras_bert')
token_dict = k_bert$load_vocabulary(vocab_path)
tokenizer = k_bert$Tokenizer(token_dict)

seq_length = 128L 
bch_size = 24
epochs = 8
learning_rate = 1e-4

DATA_COLUMN = 'text'
LABEL_COLUMN = 'declined'

model = k_bert$load_trained_model_from_checkpoint(
  config_path,
  checkpoint_path,
  training=T,
  trainable=T,
  seq_len=seq_length)

2022-03-12 04:25:56.421131: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-03-12 04:25:56.776775: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 11329 MB memory:  -> device: 0, name: NVIDIA TITAN X (Pascal), pci bus id: 0000:41:00.0, compute capability: 6.1

# tokenize text
tokenize_fun = function(dataset) {
  c(indices, target, segments) %<-% list(list(),list(),list())
  for ( i in 1:nrow(dataset)) {
    c(indices_tok, segments_tok) %<-% tokenizer$encode(dataset[[DATA_COLUMN]][i], 
                                                       max_len=seq_length)
    indices = indices %>% append(list(as.matrix(indices_tok)))
    target = target %>% append(dataset[[LABEL_COLUMN]][i])
    segments = segments %>% append(list(as.matrix(segments_tok)))
  }
  return(list(indices,segments, target))
}

# read data
dt_data = function(dir, rows_to_read){
  data = data.table::fread(dir, nrows=rows_to_read)
  c(x_train, x_segment, y_train) %<-% tokenize_fun(data)
  return(list(x_train, x_segment, y_train))
}

# write_csv(sub_text %>%
#             mutate(declined = if_else(Decision == "Declined", 1, 0)) %>%
#             select(text, declined),
#           "sub_text.csv")
c(x_train, x_segment, y_train) %<-% dt_data("sub_text.csv", 1000)

Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     

train = do.call(cbind,x_train) %>% t()
segments = do.call(cbind,x_segment) %>% t()
targets = do.call(cbind,y_train) %>% t()

concat = c(list(train ),list(segments))

c(decay_steps, warmup_steps) %<-% k_bert$calc_train_steps(
  targets %>% length(),
  batch_size=bch_size,
  epochs=epochs
)

library(keras)

input_1 = get_layer(model,name = 'Input-Token')$input
input_2 = get_layer(model,name = 'Input-Segment')$input
inputs = list(input_1,input_2)

dense = get_layer(model,name = 'NSP-Dense')$output

outputs = dense %>% layer_dense(units=1L, activation='sigmoid',
                         kernel_initializer=initializer_truncated_normal(stddev = 0.02),
                         name = 'output')

model = keras_model(inputs = inputs,outputs = outputs)

model %>% compile(
  k_bert$AdamWarmup(decay_steps=decay_steps, 
                    warmup_steps=warmup_steps, learning_rate=learning_rate),
  loss = 'binary_crossentropy',
  metrics = list('accuracy', 'AUC')
)

history <- model %>% fit(
  concat,
  targets,
  epochs=epochs,
  batch_size=bch_size, validation_split=0.2)

2022-03-12 04:26:20.520348: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)

Epoch 1/8

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23/23 [==============================] - ETA: 0s - loss: 0.5018 - accuracy: 0.8120 - auc: 0.5063
23/23 [==============================] - 44s 576ms/step - loss: 0.5018 - accuracy: 0.8120 - auc: 0.5063 - val_loss: 0.4496 - val_accuracy: 0.8175 - val_auc: 0.7036
Epoch 2/8

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23/23 [==============================] - ETA: 0s - loss: 0.4361 - accuracy: 0.8157 - auc: 0.7308
23/23 [==============================] - 10s 437ms/step - loss: 0.4361 - accuracy: 0.8157 - auc: 0.7308 - val_loss: 0.4420 - val_accuracy: 0.8102 - val_auc: 0.7729
Epoch 3/8

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23/23 [==============================] - ETA: 0s - loss: 0.3404 - accuracy: 0.8339 - auc: 0.8651
23/23 [==============================] - 10s 439ms/step - loss: 0.3404 - accuracy: 0.8339 - auc: 0.8651 - val_loss: 0.4564 - val_accuracy: 0.8248 - val_auc: 0.7952
Epoch 4/8

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23/23 [==============================] - 10s 443ms/step - loss: 0.2163 - accuracy: 0.9234 - auc: 0.9466 - val_loss: 0.5839 - val_accuracy: 0.8029 - val_auc: 0.7954
Epoch 5/8

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23/23 [==============================] - ETA: 0s - loss: 0.0988 - accuracy: 0.9708 - auc: 0.9907
23/23 [==============================] - 10s 448ms/step - loss: 0.0988 - accuracy: 0.9708 - auc: 0.9907 - val_loss: 0.7148 - val_accuracy: 0.8321 - val_auc: 0.7509
Epoch 6/8

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23/23 [==============================] - 10s 452ms/step - loss: 0.0592 - accuracy: 0.9818 - auc: 0.9977 - val_loss: 0.8358 - val_accuracy: 0.7518 - val_auc: 0.7514
Epoch 7/8

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23/23 [==============================] - ETA: 0s - loss: 0.0353 - accuracy: 0.9836 - auc: 0.9993
23/23 [==============================] - 10s 441ms/step - loss: 0.0353 - accuracy: 0.9836 - auc: 0.9993 - val_loss: 1.0636 - val_accuracy: 0.6861 - val_auc: 0.7159
Epoch 8/8

 1/23 [>.............................] - ETA: 9s - loss: 0.1284 - accuracy: 0.9167 - auc: 1.0000
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 7/23 [========>.....................] - ETA: 6s - loss: 0.0294 - accuracy: 0.9881 - auc: 1.0000
 8/23 [=========>....................] - ETA: 6s - loss: 0.0272 - accuracy: 0.9896 - auc: 1.0000
 9/23 [==========>...................] - ETA: 6s - loss: 0.0250 - accuracy: 0.9907 - auc: 1.0000
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18/23 [======================>.......] - ETA: 2s - loss: 0.0208 - accuracy: 0.9931 - auc: 1.0000
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20/23 [=========================>....] - ETA: 1s - loss: 0.0190 - accuracy: 0.9937 - auc: 1.0000
21/23 [==========================>...] - ETA: 0s - loss: 0.0188 - accuracy: 0.9940 - auc: 1.0000
22/23 [===========================>..] - ETA: 0s - loss: 0.0181 - accuracy: 0.9943 - auc: 1.0000
23/23 [==============================] - ETA: 0s - loss: 0.0175 - accuracy: 0.9945 - auc: 1.0000
23/23 [==============================] - 10s 451ms/step - loss: 0.0175 - accuracy: 0.9945 - auc: 1.0000 - val_loss: 0.7724 - val_accuracy: 0.8102 - val_auc: 0.6995

plot(history)