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Add two experiments showing how to train imdb classification to 94% accuracy
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Piotr Czapla committed
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@@ -0,0 +1,736 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Test of classifcation Fastai tokenization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%reload_ext autoreload\n",
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"%autoreload 2\n",
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"%matplotlib inline\n",
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"%cd .."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from fastai import *\n",
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"from fastai.text import *"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from ulmfit.train_clas import *"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Batch size: 70\n",
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"Max vocab: 60000\n",
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"Cache dir: data/imdb/models/f60k\n",
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"Model dir: data/imdb/models/f60k/lstm_None.m\n"
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]
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}
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],
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"source": [
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"exp = CLSHyperParams('data/imdb', qrnn=False,tokenizer='f', lang='en', cuda_id=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"exp.pretrained_model = URLs.WT103_1\n",
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"exp.drop_mult=0.3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Saving tokenized: cls.trn 25000, cls.val 25000\n",
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"Size of vocabulary: 60002\n",
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"First 20 words in vocab: ['xxunk', 'xxpad', 'xxmaj', 'the', '.', ',', 'and', 'a', 'of', 'to', 'is', 'it', 'in', 'i', 'this', 'that', '\"', \"'s\", '-', '\\n\\n']\n"
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]
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}
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],
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"source": [
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"data_clas, data_lm = exp.load_cls_data(bs=40,force=False, use_test_for_validation=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"40"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data_lm.train_dl.batch_size"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"true_wd: False\n"
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]
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}
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],
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"source": [
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"learn = exp.create_lm_learner(data_lm)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"learn.true_wd=True\n",
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"learn.opt=None"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
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]
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}
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],
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"source": [
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"learn.lr_find()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"learn.recorder.plot(skip_end=15)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Total time: 23:05\n",
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"epoch train_loss valid_loss accuracy\n",
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"1 4.216088 4.007746 0.300710 (23:05)\n",
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"\n"
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]
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}
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],
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"source": [
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"learn.fit_one_cycle(1, 1e-02, moms=(0.8,0.7))"
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]
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 14,
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"metadata": {},
|
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"outputs": [],
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"source": [
|
||||
"learn.save('fit_head')"
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]
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},
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{
|
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"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.load('fit_head');"
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||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
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"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
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"Total time: 8:42:10\n",
|
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"epoch train_loss valid_loss accuracy\n",
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"1 3.969496 3.845486 0.315829 (26:05)\n",
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"2 3.918823 3.763834 0.326209 (26:03)\n",
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"3 3.839340 3.699592 0.335176 (26:08)\n",
|
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"4 3.806484 3.647501 0.342129 (26:06)\n",
|
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"5 3.774351 3.600486 0.347226 (26:12)\n",
|
||||
"6 3.743252 3.558260 0.352164 (26:09)\n",
|
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"7 3.699686 3.523352 0.356172 (26:09)\n",
|
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"8 3.713620 3.493051 0.360080 (26:11)\n",
|
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"9 3.655959 3.463499 0.363502 (26:11)\n",
|
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"10 3.639268 3.436016 0.366489 (26:08)\n",
|
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"11 3.612611 3.406506 0.370597 (26:11)\n",
|
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"12 3.583289 3.374530 0.374582 (26:07)\n",
|
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"13 3.538116 3.351603 0.378152 (26:08)\n",
|
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"14 3.525463 3.321655 0.382012 (26:06)\n",
|
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"15 3.490068 3.292532 0.385994 (26:08)\n",
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"16 3.455298 3.272834 0.388984 (26:11)\n",
|
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"17 3.408465 3.253918 0.391583 (26:05)\n",
|
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"18 3.420946 3.241025 0.393331 (25:56)\n",
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"19 3.382529 3.235213 0.394229 (25:57)\n",
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"20 3.367180 3.233525 0.394359 (25:49)\n",
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"\n"
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]
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||||
}
|
||||
],
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||||
"source": [
|
||||
"learn.unfreeze()\n",
|
||||
"learn.fit_one_cycle(20, 1e-3, moms=(0.8,0.7))"
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||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.save(\"afteroom\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.load(\"afteroom\");"
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||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
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"text/plain": [
|
||||
"[3.2334335, tensor(0.3944)]"
|
||||
]
|
||||
},
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.validate()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# odl: [3.6938994, tensor(0.3338)]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.save_encoder(\"enc_best\")\n",
|
||||
"learn.save(\"lm_best\", with_opt=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Classification"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Validation = 100% of test"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"exp.drop_mult=0.5\n",
|
||||
"learn=exp.create_cls_learner(data_clas)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.true_wd=True"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.load_encoder('enc_best')\n",
|
||||
"learn.freeze()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Total time: 04:03\n",
|
||||
"epoch train_loss valid_loss accuracy\n",
|
||||
"1 0.286372 0.176776 0.933840 (04:03)\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.fit_one_cycle(1, 2e-2, moms=(0.8,0.7))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Total time: 04:21\n",
|
||||
"epoch train_loss valid_loss accuracy\n",
|
||||
"1 0.235155 0.160959 0.940560 (04:21)\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.freeze_to(-2)\n",
|
||||
"learn.fit_one_cycle(1, slice(1e-2/(2.6**4),1e-2), moms=(0.8,0.7))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.save(\"2\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Total time: 05:38\n",
|
||||
"epoch train_loss valid_loss accuracy\n",
|
||||
"1 0.213980 0.148502 0.946840 (05:38)\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.freeze_to(-3)\n",
|
||||
"learn.fit_one_cycle(1, slice(5e-3/(2.6**4),5e-3), moms=(0.8,0.7))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.save(\"3\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Total time: 13:45\n",
|
||||
"epoch train_loss valid_loss accuracy\n",
|
||||
"1 0.198522 0.153674 0.947800 (06:52)\n",
|
||||
"2 0.169954 0.157253 0.947320 (06:52)\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.unfreeze()\n",
|
||||
"learn.fit_one_cycle(2, slice(1e-3/(2.6**4),1e-3), moms=(0.8,0.7))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.save(\"all\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Validation = 10% of train"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Tokenized data loaded, lm.trn 90000, lm.val 10000\n",
|
||||
"Tokenized data loaded, cls.trn 22500, cls.val 2500\n",
|
||||
"Size of vocabulary: 60002\n",
|
||||
"First 20 words in vocab: ['xxunk', 'xxpad', 'xxmaj', 'the', '.', ',', 'and', 'a', 'of', 'to', 'is', 'it', 'in', 'i', 'this', 'that', '\"', \"'s\", '-', '\\n\\n']\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data_clas, data_lm = exp.load_cls_data(bs=40,force=False, use_test_for_validation=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"exp.drop_mult=0.5\n",
|
||||
"learn=exp.create_cls_learner(data_clas)\n",
|
||||
"learn.true_wd=True"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.load_encoder('enc_best')\n",
|
||||
"learn.freeze()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Total time: 02:43\n",
|
||||
"epoch train_loss valid_loss accuracy\n",
|
||||
"1 0.288307 1.597378 0.575200 (02:43)\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.fit_one_cycle(1, 2e-2, moms=(0.8,0.7))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Total time: 03:08\n",
|
||||
"epoch train_loss valid_loss accuracy\n",
|
||||
"1 0.246644 0.435174 0.779200 (03:08)\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.freeze_to(-2)\n",
|
||||
"learn.fit_one_cycle(1, slice(1e-2/(2.6**4),1e-2), moms=(0.8,0.7))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Total time: 04:19\n",
|
||||
"epoch train_loss valid_loss accuracy\n",
|
||||
"1 0.202743 0.212411 0.923200 (04:19)\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.freeze_to(-3)\n",
|
||||
"learn.fit_one_cycle(1, slice(5e-3/(2.6**4),5e-3), moms=(0.8,0.7))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Total time: 10:51\n",
|
||||
"epoch train_loss valid_loss accuracy\n",
|
||||
"1 0.189055 0.162822 0.944000 (05:25)\n",
|
||||
"2 0.168473 0.165473 0.941600 (05:25)\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.unfreeze()\n",
|
||||
"learn.fit_one_cycle(2, slice(1e-3/(2.6**4),1e-3), moms=(0.8,0.7))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.save(\"valid\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Running models on testset\n",
|
||||
"There is a small bug that does not let us use test_ds so we load the test set in to valid_ds and run validate() on that data set"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Tokenized data loaded, lm.trn 90000, lm.val 10000\n",
|
||||
"Tokenized data loaded, cls.trn 25000, cls.val 25000\n",
|
||||
"Size of vocabulary: 60002\n",
|
||||
"First 20 words in vocab: ['xxunk', 'xxpad', 'xxmaj', 'the', '.', ',', 'and', 'a', 'of', 'to', 'is', 'it', 'in', 'i', 'this', 'that', '\"', \"'s\", '-', '\\n\\n']\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data_clas, data_lm = exp.load_cls_data(bs=40,force=False, use_test_for_validation=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn=exp.create_cls_learner(data_clas)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.load('valid');"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[0.15947564, tensor(0.9483)]"
|
||||
]
|
||||
},
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.validate()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.load(\"all\");"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[0.15725298, tensor(0.9473)]"
|
||||
]
|
||||
},
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.validate()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python [conda env:fastaiv1]",
|
||||
"language": "python",
|
||||
"name": "conda-env-fastaiv1-py"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -0,0 +1,467 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Test of classifcation Moses + Fastai pre/post processing"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%reload_ext autoreload\n",
|
||||
"%autoreload 2\n",
|
||||
"%matplotlib inline\n",
|
||||
"%cd .."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from fastai import *\n",
|
||||
"from fastai.text import *"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from ulmfit.train_clas import *"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Batch size: 70\n",
|
||||
"Max vocab: 60000\n",
|
||||
"Cache dir: data/imdb/models/vf60k\n",
|
||||
"Model dir: data/imdb/models/vf60k/lstm_None.m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"exp = CLSHyperParams('data/imdb', qrnn=False,tokenizer='vf', lang='en', cuda_id=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"exp.pretrained_model = URLs.WT103_1\n",
|
||||
"exp.drop_mult=0.3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"exp.load_cls_data??"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Saving tokenized: cls.trn 25000, cls.val 25000\n",
|
||||
"Size of vocabulary: 60002\n",
|
||||
"First 20 words in vocab: ['xxunk', 'xxpad', 'xxmaj', 'the', ',', '.', 'and', 'a', 'of', 'to', 'is', 'it', 'in', 'i', 'this', 'that', '\"', \"'s\", 'was', 'as']\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data_clas, data_lm = exp.load_cls_data(bs=40,force=True, use_test_for_validation=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Training args: {'drop_mult': 0.3, 'tie_weights': True, 'clip': 0.12, 'bptt': 70, 'pretrained_fnames': None, 'pretrained_model': 'https://s3.amazonaws.com/fast-ai-modelzoo/wt103-1'} dps: None\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn = exp.create_lm_learner(data_lm)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.true_wd=True\n",
|
||||
"learn.opt=None\n",
|
||||
"learn.lr_find()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.recorder.plot(skip_end=15)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Total time: 23:37\n",
|
||||
"epoch train_loss valid_loss accuracy\n",
|
||||
"1 4.225404 4.029806 0.302210 (23:37)\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.fit_one_cycle(1, 1e-02, moms=(0.8,0.7))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.save('fit_head')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.load('fit_head');"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Total time: 8:58:03\n",
|
||||
"epoch train_loss valid_loss accuracy\n",
|
||||
"1 3.970405 3.867857 0.316788 (26:53)\n",
|
||||
"2 3.920167 3.786381 0.327380 (26:54)\n",
|
||||
"3 3.851934 3.726164 0.336203 (26:53)\n",
|
||||
"4 3.814150 3.671940 0.343449 (26:54)\n",
|
||||
"5 3.798842 3.626528 0.348548 (26:53)\n",
|
||||
"6 3.753443 3.585267 0.353221 (26:57)\n",
|
||||
"7 3.723497 3.546739 0.357538 (26:55)\n",
|
||||
"8 3.676399 3.515404 0.361083 (26:53)\n",
|
||||
"9 3.668247 3.487297 0.364337 (26:52)\n",
|
||||
"10 3.633528 3.454587 0.368336 (26:51)\n",
|
||||
"11 3.611737 3.425386 0.372090 (26:49)\n",
|
||||
"12 3.586109 3.396924 0.375549 (26:54)\n",
|
||||
"13 3.568666 3.371741 0.379174 (26:53)\n",
|
||||
"14 3.536366 3.337895 0.383549 (26:59)\n",
|
||||
"15 3.507154 3.313276 0.387211 (26:56)\n",
|
||||
"16 3.465269 3.290863 0.390346 (26:56)\n",
|
||||
"17 3.479237 3.274435 0.392694 (26:54)\n",
|
||||
"18 3.407071 3.261680 0.394350 (26:55)\n",
|
||||
"19 3.425704 3.255773 0.395224 (26:52)\n",
|
||||
"20 3.390900 3.253836 0.395450 (26:52)\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.unfreeze()\n",
|
||||
"learn.fit_one_cycle(20, 1e-3, moms=(0.8,0.7))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.save(\"afteroom\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.load(\"afteroom\");"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[3.2538004, tensor(0.3954)]"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.validate()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.save_encoder(\"enc_best\")\n",
|
||||
"learn.save(\"lm_best\", with_opt=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.recorder.plot_losses()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Classification"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"exp.drop_mult=0.5\n",
|
||||
"learn=exp.create_cls_learner(data_clas)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.true_wd=True"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.load_encoder('enc_best')\n",
|
||||
"learn.freeze()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Total time: 03:53\n",
|
||||
"epoch train_loss valid_loss accuracy\n",
|
||||
"1 0.264060 0.178016 0.934240 (03:53)\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.fit_one_cycle(1, 2e-2, moms=(0.8,0.7))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Total time: 04:21\n",
|
||||
"epoch train_loss valid_loss accuracy\n",
|
||||
"1 0.261168 0.164097 0.940720 (04:21)\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.freeze_to(-2)\n",
|
||||
"learn.fit_one_cycle(1, slice(1e-2/(2.6**4),1e-2), moms=(0.8,0.7))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Total time: 05:39\n",
|
||||
"epoch train_loss valid_loss accuracy\n",
|
||||
"1 0.222833 0.151485 0.944720 (05:39)\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.freeze_to(-3)\n",
|
||||
"learn.fit_one_cycle(1, slice(5e-3/(2.6**4),5e-3), moms=(0.8,0.7))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Total time: 13:48\n",
|
||||
"epoch train_loss valid_loss accuracy\n",
|
||||
"1 0.210751 0.151522 0.948720 (06:54)\n",
|
||||
"2 0.181168 0.161364 0.948640 (06:53)\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"learn.unfreeze()\n",
|
||||
"learn.fit_one_cycle(2, slice(1e-3/(2.6**4),1e-3), moms=(0.8,0.7))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.save('best_cls')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.load(\"best_cls\");"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python [conda env:fastaiv1]",
|
||||
"language": "python",
|
||||
"name": "conda-env-fastaiv1-py"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Reference in new issue
Block a user