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SGD_learning_regimes

Code for reproducing the paper "Dissecting the effects of SGD Noise in Distinct Regimes of Deep Learning"

What this code does:

  1. Accepts many different parameters
  2. Perform a single training of a neural network (depending on the parameters)
  3. Compute and save observables during and at the end of the trianing (depending on the parameters)

The results are saved in a pickle format compatible with grid (grid allows to make sweeps in the paramters)

Paramters

A list of some of the paramters:

--arch architecture
--act activation function
--h width
--L depth (for mlp architecture)
--alpha alpha paramter, corresponding to initialization scale
--dataset dataset
--ptr number of training points
--pte number of test points
--loss loss function
--dynamics training dynamics
--bs batch size for sgd dynamics
--dt learning rate
--temp temperature, defined as dt/(bs * h) (it is alternative to defining the learning rate)
--ckpt_grad_stats number of train (test) points to compute the Gram matrix of the neural tangent kernel
--max_wall maximum wall time (in seconds)
--seed_init initialization seed

Tuto: execute a single training

python -m edm --dataset mnist_parity --ptr 1024 --pte 2048 --arch mlp --act gelu --h 64 --L 8 --dynamics sgd --alpha 1 --dt 0.1 --bs 64 --max_wall 120 --output test.pk

Many parameters are set by default!

Then the data can be loaded using pickle

import pickle

with open('test.pk', 'rb') as f:
    args = pickle.load(f)  # dict with the paramters
    data = pickle.load(f)  # all measurements
    
# data['sgd']['dynamics'] is a list of dict

print("Initial train loss is", data['sgd']['dynamics'][0]['train']['loss'])
print("Final test error is", data['sgd']['dynamics'][-1]['test']['err'])

Tuto: sweeping over many parameters

Install grid and the current repository (SGD_learning_regimes). Execute the following line that makes a sweep along the parameter dt, note that grid accept python code to create the list of parameters to sweep along.

python -m grid tests "python -m edm --dataset mnist_parity --ptr 1024 --pte 2048 --arch mlp --act gelu --h 64 --L 8 --dynamics sgd --alpha 1 --bs 64 --max_wall 120" --dt "[2**i for i in range(-3, 1)]"

At the end of the execution, the runs are saved in the directory name tests (in this example) and can be loaded as follow

import grid
runs = grid.load('tests')

print("values of dt for the different runs", [r['args']['dt'] for r in runs])

See more info on how to sweep and load runs using grid in the readme.

5-hidden-layer fully connected architecture

On parity MNIST

python -m grid mnist-FC_5L-lazy "python -m edm --arch mlp --act relu --L 5 --h 128 --alpha 32768 --dataset mnist_parity --pte 32768 --loss hinge --dynamics sgd --bs 16 --ckpt_grad_stats 128 --max_wall 10000" --seed_init "[i for i in range(5)]" --ptr "[1024, 2048, 4096, 8192, 16384]" --temp "[2**i for i in range(-13,3)]"

python -m grid mnist-FC_5L-feature "python -m edm --arch mlp --act relu --L 5 --h 128 --alpha 0.0009765625 --dataset mnist_parity --pte 32768 --loss hinge --dynamics sgd --bs 16 --ckpt_grad_stats 128 --max_wall 20000" --seed_init "[i for i in range(5)]" --ptr "[1024, 2048, 4096, 8192, 16384]" --temp "[2**i for i in range(-26,-5)]"

On CIFAR animal

python -m grid cifar-FC_5L-lazy "python -m edm --arch mlp --act relu --L 5 --h 128 --alpha 32768 --dataset cifar_animal --pte 32768 --loss hinge --dynamics sgd --bs 16 --ckpt_grad_stats 128 --max_wall 10000" --seed_init "[i for i in range(5)]" --ptr "[1024, 2048, 4096, 8192, 16384]" --temp "[2**i for i in range(-13,3)]"

python -m grid cifar-FC-5L-feature "python -m edm --arch mlp --act relu --L 5 --h 128 --alpha 0.0009765625 --dataset cifar_animal --pte 32768 --loss hinge --dynamics sgd --bs 16 --ckpt_grad_stats 128 --max_wall 10000" --seed_init "[i for i in range(5)]" --ptr "[1024, 2048, 4096, 8192, 16384]" --temp "[2**i for i in range(-26, -5)]"

Phase diagram (alpha, dt) on parity MNIST

python -m grid mnist-alpha_dt-FC-5L "python -m edm --arch mlp --act gelu --L 5 --h 128 --dataset mnist_parity --ptr 1024 --pte 32768 --loss hinge --dynamics sgd --bs 16 --ckpt_grad_stats 1024 --max_wall 14400" --seed_init "[i for i in range(9)]" --alpha "[2**i for i in range(-14,12)]" --dt "[2**i for i in range(-15,16)]"

MNAS

On parity MNIST

python -m grid mnist-MNAS "python -m edm --arch mnas --act relu --h 64 --alpha 32768 --dataset mnist_parity --pte 32768 --loss hinge --dynamics sgd --bs 16 --ckpt_grad_stats 128 --max_wall 10000" --seed_init "[i for i in range(5)]" --ptr "[1024, 2048, 4096, 8192, 16384]" --temp "[2**i for i in range(-20,1)]"

On CIFAR animal

python -m grid cifar-MNAS-lazy "python -m edm --arch mnas --act relu --h 64 --alpha 32768 --dataset cifar_animal --pte 32768 --loss hinge --dynamics sgd --bs 16 --ckpt_grad_stats 128 --max_wall 8000" --seed_init "[i for i in range(5)]" --ptr "[1024, 2048, 4096, 8192, 16384]" --temp "[2**i for i in range(-20,1)]"

python -m grid cifar-MNAS-feature "python -m edm --arch mnas --act relu --h 64 --alpha 1 --dataset cifar_animal --pte 32768 --loss hinge --dynamics sgd --bs 16 --ckpt_grad_stats 128 --max_wall 50000" --seed_init "[i for i in range(5)]" --ptr "[2048, 4096, 8192, 16384]" --temp "[2**i for i in range(-26,-4)]"

Phase diagram (alpha, dt) on CIFAR animal

python -m grid cifar-alpha_dt-MNAS "python -m edm --arch mnas --act relu --h 32 --dataset cifar_animal --ptr 1024 --pte 32768 --loss hinge --dynamics sgd --bs 64 --ckpt_grad_stats 1024 --max_wall 3600" --seed_init "[i for i in range(4)]" --alpha "[2**i for i in range(-14,12)]" --dt "[2**i for i in range(-13,11)]"

simpleCNN

On parity MNIST

python -m grid mnist-simpleCNN-lazy "python -m edm --arch simple_cnn --act relu --h 32 --alpha 32768 --dataset mnist_parity --pte 32768 --loss hinge --dynamics sgd --bs 16 --ckpt_grad_stats 128 --max_wall 8000" --seed_init "[i for i in range(5)]" --ptr "[1024, 2048, 4096, 8192, 16384]" --temp "[2**i for i in range(-20,1)]"

On CIFAR animal

python -m grid cifar-simpleCNN-lazy "python -m edm --arch simple_cnn --act relu --h 32 --alpha 32768 --dataset cifar_animal --pte 32768 --loss hinge --dynamics sgd --bs 16 --ckpt_grad_stats 128 --max_wall 8000" --seed_init "[i for i in range(5)]" --ptr "[1024, 2048, 4096, 8192, 16384]" --temp "[2**i for i in range(-20,1)]"

Perceptron

python -m grid depleted_perceptron "python -m edm --arch linear --alpha 32768 --dataset depleted_sign --pte 32768 --loss hinge --dynamics sgd --bs 2 --ckpt_grad_stats 512 --max_wall 10000 --ckpt_save_mult 1000000" --seed_init "[i for i in range(5)]" --data_chi "[3.0, 1.0, 0.0]" --d "[128]" --ptr "[1024, 2048, 4096, 8192, 16384]" --dt "[2**i for i in range(-15,3)]"