Datasets:
HenryAI
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KerasCodeExamples.txt

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Epoch 16/30
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0038 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0068
Epoch 17/30
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0039 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0066
Epoch 18/30
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0038 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0064
Epoch 19/30
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0035 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0071
Epoch 20/30
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0038 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0066
Epoch 21/30
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0038 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0068
Epoch 22/30
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0034 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0073
Epoch 23/30
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0035 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0078
Epoch 24/30
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0037 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0087
Epoch 25/30
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0031 - val_sparse_categorical_accuracy: 0.0108 - val_distillation_loss: 0.0078
Epoch 26/30
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0033 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0072
Epoch 27/30
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0036 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0071
Epoch 28/30
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0036 - val_sparse_categorical_accuracy: 0.0275 - val_distillation_loss: 0.0078
Epoch 29/30
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0032 - val_sparse_categorical_accuracy: 0.0196 - val_distillation_loss: 0.0068
Epoch 30/30
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0034 - val_sparse_categorical_accuracy: 0.0147 - val_distillation_loss: 0.0071
97/97 [==============================] - 7s 64ms/step - loss: 0.0000e+00 - accuracy: 0.0107
Top-1 accuracy on the test set: 1.07%
Results
With just 30 epochs of training, the results are nowhere near expected. This is where the benefits of patience aka a longer training schedule will come into play. Let's investigate what the model trained for 1000 epochs can do.
# Download the pre-trained weights.
!wget https://git.io/JBO3Y -O S-r50x1-128-1000.tar.gz
!tar xf S-r50x1-128-1000.tar.gz
pretrained_student = keras.models.load_model(\"S-r50x1-128-1000\")
pretrained_student.summary()
Model: \"resnet\"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
root_block (Sequential) (None, 32, 32, 64) 9408
_________________________________________________________________
block1 (Sequential) (None, 32, 32, 256) 214912
_________________________________________________________________
block2 (Sequential) (None, 16, 16, 512) 1218048
_________________________________________________________________
block3 (Sequential) (None, 8, 8, 1024) 7095296
_________________________________________________________________
block4 (Sequential) (None, 4, 4, 2048) 14958592
_________________________________________________________________
group_norm (GroupNormalizati multiple 4096
_________________________________________________________________
re_lu_97 (ReLU) multiple 0
_________________________________________________________________
global_average_pooling2d_1 ( multiple 0
_________________________________________________________________
head/dense (Dense) multiple 208998
=================================================================
Total params: 23,709,350
Trainable params: 23,709,350
Non-trainable params: 0
_________________________________________________________________
This model exactly follows what the authors have used in their student models. This is why the model summary is a bit different.
_, top1_accuracy = pretrained_student.evaluate(test_ds)
print(f\"Top-1 accuracy on the test set: {round(top1_accuracy * 100, 2)}%\")
97/97 [==============================] - 14s 131ms/step - loss: 0.0000e+00 - accuracy: 0.8102
Top-1 accuracy on the test set: 81.02%
With 100000 epochs of training, this same model leads to a top-1 accuracy of 95.54%.
There are a number of important ablations studies presented in the paper that show the effectiveness of these recipes compared to the prior art. So if you are skeptical about these recipes, definitely consult the paper.
Note on training for longer
With TPU-based hardware infrastructure, we can train the model for 1000 epochs faster. This does not even require adding a lot of changes to this codebase. You are encouraged to check this repository as it presents TPU-compatible training workflows for these recipes and can be run on Kaggle Kernel leveraging their free TPU v3-8 hardware.
Using compositional and mixed-dimension embeddings for memory-efficient recommendation models.
Introduction
This example demonstrates two techniques for building memory-efficient recommendation models by reducing the size of the embedding tables, without sacrificing model effectiveness:
Quotient-remainder trick, by Hao-Jun Michael Shi et al., which reduces the number of embedding vectors to store, yet produces unique embedding vector for each item without explicit definition.
Mixed Dimension embeddings, by Antonio Ginart et al., which stores embedding vectors with mixed dimensions, where less popular items have reduced dimension embeddings.
We use the 1M version of the Movielens dataset. The dataset includes around 1 million ratings from 6,000 users on 4,000 movies.
Setup
import os
import math
from zipfile import ZipFile
from urllib.request import urlretrieve
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers import StringLookup
import matplotlib.pyplot as plt