I need a python programmer to create a simple neural network using tensorflow and tensorboard

For a smaal research project I need a python script written what uses tensorflow to create a small neural network which can is trained on this dataset: [login to view URL] and can recognise handwriting. It should look somewhat like this: import tensorflow as tf from [login to view URL] import input_data mnist = input_data.read_data_sets(one_hot = True) n_nodes_hl1 = 500 n_nodes_hl2 = 500 n_nodes_hl3 = 500 n_classes = 10 batch_size = 100 x = [login to view URL]('float', [None, 784]) y = [login to view URL]('float') def neural_network_model(data): hidden_1_layer = {'weights':[login to view URL](tf.random_normal([784, n_nodes_hl1])), 'biases':[login to view URL](tf.random_normal([n_nodes_hl1]))} hidden_2_layer = {'weights':[login to view URL](tf.random_normal([n_nodes_hl1, n_nodes_hl2])), 'biases':[login to view URL](tf.random_normal([n_nodes_hl2]))} hidden_3_layer = {'weights':[login to view URL](tf.random_normal([n_nodes_hl2, n_nodes_hl3])), 'biases':[login to view URL](tf.random_normal([n_nodes_hl3]))} output_layer = {'weights':[login to view URL](tf.random_normal([n_nodes_hl3, n_classes])), 'biases':[login to view URL](tf.random_normal([n_classes])),} l1 = [login to view URL]([login to view URL](data,hidden_1_layer['weights']), hidden_1_layer['biases']) l1 = [login to view URL](l1) l2 = [login to view URL]([login to view URL](l1,hidden_2_layer['weights']), hidden_2_layer['biases']) l2 = [login to view URL](l2) l3 = [login to view URL]([login to view URL](l2,hidden_3_layer['weights']), hidden_3_layer['biases']) l3 = [login to view URL](l3) output = [login to view URL](l3,output_layer['weights']) + output_layer['biases'] return output def train_neural_network(x): prediction = neural_network_model(x) cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y) ) optimizer = [login to view URL]().minimize(cost) hm_epochs = 10 with [login to view URL]() as sess: [login to view URL](tf.initialize_all_variables()) for epoch in range(hm_epochs): epoch_loss = 0 for _ in range(int(mnist.train.num_examples/batch_size)): epoch_x, epoch_y = mnist.train.next_batch(batch_size) _, c = [login to view URL]([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y}) epoch_loss += c print('Epoch', epoch, 'completed out of',hm_epochs,'loss:',epoch_loss) correct = [login to view URL]([login to view URL](prediction, 1), [login to view URL](y, 1)) accuracy = tf.reduce_mean([login to view URL](correct, 'float')) print('Accuracy:',[login to view URL]({x:[login to view URL], y:[login to view URL]})) train_neural_network(x)