How to increase accuracy of neural networks

I'm trying to build a simple neural network to classify product images to different labels (product types). i.e, given a new product image tell which product category type (books, toys, electronics etc.) it belongs to.

I have a couple of product images under each product number and each product number has a label (i.e., product type) in a excel sheet.

Below is my code:

from sklearn.preprocessing import LabelEncoder
from sklearn.cross_validation import train_test_split
from keras.models import Sequential
from keras.layers import Activation
from keras.optimizers import SGD
from keras.layers import Dense
from keras.utils import np_utils
from imutils import paths
import numpy as np
import argparse
import cv2
import os
import xlwt
import xlrd
import glob2
import pickle

def image_to_feature_vector(image, size=(32,32)):
    return cv2.resize(image, size).flatten()

def read_data(xls = "/Desktop/num_to_product_type.xlsx"):
    book = xlrd.open_workbook(xls)
    sheet = book.sheet_by_index(0)
    d = {}
    for row_index in xrange(1, sheet.nrows): # skip heading row
        prod_type, prod_num = sheet.row_values(row_index, end_colx=2)
        prod_type = unicode(prod_type).encode('UTF8')
        produ_num = unicode(prod_num).encode('UTF8')

        d[prod_num] = prod_type
    return d

def main():

    try:
        imagePaths=[]
        print("[INFO] describing images...")
        for path, subdirs, files in os.walk(r'/Desktop/data'):
            for filename in files:
                imagePaths.append(os.path.join(path, filename))

        files = glob2.glob('/Desktop/data/**/.DS_Store')
        for i in files:
            imagePaths.remove(i)  
    except:
        pass

    dd = read_data() 
    # initialize the data matrix and labels list
    data = []
    labels1 = []

    for (i, imagePath) in enumerate(imagePaths):
        image = cv2.imread(imagePath)
        #print(image.shape)
        subdir = imagePath.split('/')[-2]
        for k, v in dd.items():
            if k == subdir:
                label = v
                break

        features = image_to_feature_vector(image)
        data.append(features)
        labels1.append(label)


        # show an update every 1,000 images
        if i > 0 and i % 1000 == 0:
            print("[INFO] processed {}/{}".format(i, len(imagePaths)))
    print("String Labels")
    print(labels1)

    # encode the labels, converting them from strings to integers
    le = LabelEncoder()
    labels = le.fit_transform(labels1)
    print(labels) 

    d={}
    d[labels[0]] = labels1[0]

    for i in range(1,len(labels)-1):
        if labels[i-1] != labels[i] and labels[i] == labels[i+1]:
            d[labels[i]]  = labels1[i]

    data = np.array(data) / 255.0
    labels = np_utils.to_categorical(labels, 51)
    print("To_Categorical")
    print(labels) 

    print("[INFO] constructing training/testing split...")
    (trainData, testData, trainLabels, testLabels) = train_test_split(
        data, labels, test_size=0.25, random_state=42)

    model = Sequential()
    model.add(Dense(768, input_dim=3072, init="uniform",
        activation="relu"))
    model.add(Dense(384, init="uniform", activation="relu"))
    model.add(Dense(51))
    model.add(Activation("softmax"))

    print("[INFO] compiling model...")

    sgd = SGD(lr=0.125
              )
    model.compile(loss="categorical_crossentropy", optimizer=sgd,
        metrics=["accuracy"])
    model.fit(trainData, trainLabels, nb_epoch=50, batch_size=750)


#     #Test the model
    #show the accuracy on the testing set
     print("[INFO] evaluating on testing set...")
     (loss, accuracy) = model.evaluate(testData, testLabels,
         batch_size=128, verbose=1)
     print("[INFO] loss={:.4f}, accuracy: {:.4f}%".format(loss,
         accuracy * 100))

if __name__ == '__main__':

    main()

The neural network is a 3-2-3-51 feedforward neural network. Layer 0 contains 3 inputs. Layers 1 & 2 are hidden layers containing 2 & 3 nodes resp. Layer 3 is the output layer which has 51 nodes (i.e., for 51 product category type). However, with this I'm getting very low accuracy, only about 45-50%.

Is there something wrong that I'm doing? How do you increase the accuracy of the neural network? I read somewhere that it can be done by "crossvalidation and hyperparameter tuning" but how is it done? Sorry, I'm very new at neural network, just trying something new. Thanks.