IndexError on loss.backward()


I am trying some weird evolutionary neuron connections on a simple XOR problem.

And when I am back-propagating the losses, the loss.backward() throws a IndexError:

IndexError                                Traceback (most recent call last)
<ipython-input-2-39958196b2d6> in <module>
     86     for loss, idx, network in fittest:
     87         print(loss)
---> 88         loss['model'].backward()  # Bug in PyTorch/Numpy so network don't train now.
     89         network['optim'].step()

IndexError: too many indices for tensor of dimension 0

When I check the loss.shape it returns torch.Size([]) =(

Any idea why is the tensor for the loss in a weird shape? How should I resolve the issue?

Full code:

import copy
import random
import itertools

# Plotting candies
import seaborn as sns; sns.set()
import matplotlib.pyplot as plt

# Number stuff.
import numpy as np
import pandas as pd

import torch 
from torch import tensor
from torch.nn import Sequential, Linear, Sigmoid, L1Loss
from torch.optim import Adam

X = tensor([[0.,0.], [0.,1.], [1.,0.], [1.,1.]])
Y = tensor([[0,],[1.],[1.],[0.]])

# Use tensor.shape to get in-/output dimension size.
num_data, input_dim = X.shape
num_data, output_dim = Y.shape

init_population_size = 80
mutation_rate = 0.01
generations = 30    
hidden_dim = 10

dimensions = hidden_dim + hidden_dim + output_dim * num_data

def get_random_pairs(numbers):
    # Generate all possible non-repeating pairs
    pairs = list(itertools.combinations(numbers, 2))
    # Randomly shuffle these pairs
    return pairs

# Define the model.
def create_model(lr):
    # Each network will be initialized as such.
    model = Sequential(Linear(input_dim, hidden_dim),
                      Linear(hidden_dim, output_dim),
    return {'model': model, 
            'optim': Adam(model.parameters(), lr=lr)

# Initialize the optimizer.
lr =  0.03
# Define the optimization criterion
criterion = L1Loss()

# We create 30 networks.
population = 30
population_of_networks = [create_model(lr) for i in range(population)]
survival_rate = 0.2
##mutation_rate = 0.1

for _g in range(generations):
    for net in population_of_networks:
    # Get through every network.
    loss_of_networks = []
    for i, network in enumerate(population_of_networks):
        pred = network['model'](X) # Fowards.
        loss = criterion(pred, Y)  # Loss.
        loss_of_networks.append((loss, i, population_of_networks[i]))
    # Only the fittest (i.e. lowest loss) survive 
    # at pre-defined survival rate.
    num_survivors = int(survival_rate*population)
    fittest = sorted(loss_of_networks)[:num_survivors]
    # Populate the rest of the generation with offsprings of the fittest.
    for mating_pairs in get_random_pairs(fittest):
        if len(fittest) == population:
            (female_loss, female_id, female), (male_loss, male_id, male) = mating_pairs
            # Access female's model and optimizers 
            female_model = population_of_networks[female_id]['model']
            female_optim = population_of_networks[female_id]['optim'] 
            # Access male's model and optimizers 
            male_model = population_of_networks[male_id]['model']
            male_optim = population_of_networks[male_id]['optim']
            # Make an "empty" baby by copying first.
            baby_model = copy.deepcopy(female_model)
            baby_optim = copy.deepcopy(female_optim)
            for layer_name in zip(female_model.state_dict()):
                ## Crossover process.
                f = female_model.state_dict()[layer_name[0]]
                m = male_model.state_dict()[layer_name[0]]
                # Randomly generate a crossover point.
                crossover_pt = int(random.uniform(0, f.shape[0]))
                # Make baby with crossover tensors.
                tmp =  copy.deepcopy(m[:crossover_pt])
                m[:crossover_pt], f[:crossover_pt] = f[:crossover_pt], tmp
                baby_layer = random.choice([m, f])
                ## No mutation process.
                # Overwrite the state_dict layers of the baby
                baby_model.state_dict()[layer_name[0]] = baby_layer
            # Compute baby's loss.
            _pred = baby_model(X)
            _loss = criterion(_pred, Y)
                            "_".join([str(female_id), str(male_id)]),
                            {'model': baby_model, 'optim': baby_optim}))

    # Back propagate for all models.
    for loss, idx, network in fittest: 
        loss['model'].backward()  # Bug in PyTorch/Numpy so network don't train now.

(Alban D) #2


loss is a Tensor at this line:


So you should not try to get the 'model' attribute from it.
loss.backward() should work though


Awesome catch!! Thanks @albanD!!!