Deep Dream(siraj's code).py

import numpy as numpy
from functools import partial
import PIL.Image
import tensorflow as tf
import urllib.request
import os
import zipfile


def main():
    url = 'https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip'
    data_dir  = '../data'
    model_name = os.path.split(url)[-1]
    local_zip_file = os.path.join(data_dir,model_name)

    if not os.path.exits(local_zip_file):
        model_url = urllib.request.urlopen(url)
        with open(local_zip_file,'wb') as output:
            output.write(model_url.read())

        with zipfile.ZipFile(local_zip_file, 'r') as zip_ref:
            zip_ref.extractall(data_dir)

    model_fn = 'tensorflow_inception_graph.pb'

    graph = tf.Graph()
    sess = tf.InteractiveSession(graph = graph)
    with tf.gfile.FastGFile(os.path.join(data_dir,model_fn), 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.parseFromString(f.read())
    t_input = tf.placeholder(np.float32, name='input')
    imagenet_mean= 117.0
    t_preprocessed = tf.expand_dims(t_input - imagenet_mean,0)
    tf.import_graph_def(graph_def,{'input': t_preprocessed})

    layers = [op.name for op in graph.get_operations() if op.type == 'Conv2d' and 'import/' in op.name]
    feature_nums = [int(graph.get_tensor_by_name(name + ':0').get_shape()[-1]) for name in layers]


    def render_deepdream(t_obj, img0=img_noise,
                         iter_n=10, step=1.5, octave_n=4, octave_scale=1.4):
        t_score = tf.reduce_mean(t_obj) # defining the optimization objective
        t_grad = tf.gradients(t_score, t_input)[0] # behold the power of automatic differentiation!
    
        # split the image into a number of octaves
        img = img0
        octaves = []
        for _ in range(octave_n-1):
            hw = img.shape[:2]
            lo = resize(img, np.int32(np.float32(hw)/octave_scale))
            hi = img-resize(lo, hw)
            img = lo
            octaves.append(hi)
        
        # generate details octave by octave
        for octave in range(octave_n):
            if octave>0:
                hi = octaves[-octave]
                img = resize(img, hi.shape[:2])+hi
            for _ in range(iter_n):
                g = calc_grad_tiled(img, t_grad)
                img += g*(step / (np.abs(g).mean()+1e-7))
            
            #this will usually be like 3 or 4 octaves
            #Step 5 output deep dream image via matplotlib
            showarray(img/255.0)