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@swati210994
Created October 14, 2020 06:53
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{
"cells": [
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"tf_bert_for_sequence_classification\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"bert (TFBertMainLayer) multiple 109482240 \n",
"_________________________________________________________________\n",
"dropout_37 (Dropout) multiple 0 \n",
"_________________________________________________________________\n",
"classifier (Dense) multiple 1538 \n",
"=================================================================\n",
"Total params: 109,483,778\n",
"Trainable params: 109,483,778\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"\n",
"Bert Model None\n"
]
}
],
"source": [
"log_dir='tensorboard_data/tb_bert'\n",
"model_save_path='./models/bert_model.h5'\n",
"\n",
"callbacks = [tf.keras.callbacks.ModelCheckpoint(filepath=model_save_path,save_weights_only=True,monitor='val_loss',mode='min',save_best_only=True),keras.callbacks.TensorBoard(log_dir=log_dir)]\n",
"\n",
"print('\\nBert Model',bert_model.summary())\n",
"\n",
"loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
"metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')\n",
"optimizer = tf.keras.optimizers.Adam(learning_rate=2e-5,epsilon=1e-08)\n",
"\n",
"bert_model.compile(loss=loss,optimizer=optimizer,metrics=[metric])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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