#StackBounty: #python #performance #neural-network Deep Neural Network in Python

Bounty: 100

I have written a neural network in Python and focused on adaptability and performance. I want to use it to dive deeper into that field. I am far from being an expert in neural networks and the same goes for Python. I do not want to use Tensorflow since I really want to understand how a neural network works.

My questions are:

  • How can I increase the performance? At the moment it takes days to train the network

The code runs on a single core. But since every loop over the batches run independently it can be parallelized.

  • How can I parallelize the loop over the batches?

I found some tutorials on parallel loops in Python but I could not apply it to my problem.

Here is my tested code with some pseudo training data:

from numpy import random, zeros, array, dot
from scipy.special import expit
import time 

def sigma(x):
    return expit(x)

def sigma_prime(x):
    u = expit(x)
    return  u-u*u 

def SGD(I, L, batch_size, eta):

    images = len(L)

    # Pre-activation
    z = [zeros((layer_size[l],1)) for l in range(1,nn_size)]

    # Activations
    a = [zeros((layer_size[l],1)) for l in range(nn_size)]

    # Ground truth      
    y = zeros((images, layer_size[-1]))
    for i in range(images):
        y[i,L[i]] = 1.0

    while (1):

        t0 = time.time()

        # Create random batch
        batch = random.randint(0,images,batch_size)

        dW = [zeros((layer_size[l+1], layer_size[l])) for l in range(nn_size-1)]
        db = [zeros((layer_size[l],1)) for l in range(1, nn_size)]

        for i in batch:        
            # Feedforward
            a[0] = array([I[i]]).T
            for l in range(nn_size-1):
                z[l] = dot(W[l], a[l]) + b[l]
                a[l+1] = sigma(z[l])

            # Backpropagation
            delta = (a[nn_size-1]-array([y[i]]).T) * sigma_prime(z[nn_size-2])
            dW[nn_size-2] += dot(delta, a[nn_size-2].T)
            dW[nn_size-2] += delta.dot(a[nn_size-2].T)
            db[nn_size-2] += delta
            for l in reversed(range(nn_size-2)):
                delta = dot(W[l+1].T, delta) * sigma_prime(z[l])
                dW[l] += dot(delta, a[l].T)
                db[l] += delta

        # Update Weights and Biases
        for l in range(nn_size-1):
            W[l] += - eta * dW[l] / batch_size
            b[l] += - eta * db[l] / batch_size

        print(time.time() - t0)

input_size = 1000
output_size = 10

layer_size = [input_size, 30**2, 30**2, 30**2, output_size]

nn_size = len(layer_size)
layer_size = layer_size

# Weights
W = [random.randn(layer_size[l+1],layer_size[l]) for l in range(nn_size-1)]

# Bias
b = [random.randn(layer_size[l],1) for l in range(1,nn_size)]

# Some random training data with label
size_training_data = 1000
I = random.rand(size_training_data, input_size)
L = random.randint(0,10, input_size)

batch_size = 100
eta = 0.1
SGD(I, L, batch_size, eta)


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