Deep Dream Code does not generate recognizable patterns

I've tried to create my own Deep Dream Algorithm with this Code:

import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
import inception

img = np.random.rand(1,500,500,3)
net = inception.get_inception_model()
tf.import_graph_def(net['graph_def'], name='inception')
graph = tf.get_default_graph()
sess = tf.Session()
layer = graph.get_tensor_by_name('inception/mixed5b_pool_reduce_pre_relu:0')
gradient = tf.gradients(tf.reduce_mean(layer), graph.get_tensor_by_name('inception/input:0'))
softmax = sess.graph.get_tensor_by_name('inception/softmax2:0')
iters = 100
init = tf.global_variables_initializer()

sess.run(init)
for i in range(iters):
    prediction = sess.run(softmax, \
                          {'inception/input:0': img})
    grad = sess.run(gradient[0], \
                          {'inception/input:0': img})
    grad = (grad-np.mean(grad))/np.std(grad)
    img = grad
    plt.imshow(img[0])
    plt.savefig('output/'+str(i+1)+'.png')
    plt.close('all')

But even after running this loop for 100 iterations the resulting picture still looks random (I will attach said picture to this Question). Can someone please help me to optimize my code?