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import tensorflow as tf
import numpy as np


    

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graph = tf.Graph()
with graph.as_default():
    sess = tf.Session()
with sess.as_default():
    with graph.as_default():
        accuracy = tf.Variable(0.2, name="xxx")
        accuracy_ = tf.placeholder("float")
        img_ = tf.placeholder("float", shape=[None, 64, 64, 3])
        img = tf.Variable(tf.zeros([5, 64,64,3]))
        tf.scalar_summary("acc", accuracy)
        tf.image_summary("img",img)
        summary_op = tf.merge_all_summaries()
        summary_writer = tf.train.SummaryWriter("log2", graph_def=sess.graph_def)
        update = tf.assign(accuracy, accuracy_)
        update_img = tf.assign(img, img_)


    

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with sess.as_default():
    with graph.as_default():
        sess.run(tf.initialize_all_variables())
        img_input = np.zeros([5,64,64,3])
        for i in range(1000):
            update.eval(feed_dict={accuracy_: i/100.0})
            for b in range(5):
                for x in range(64):
                    for y in range(64):
                        for c in range(3):
                            img_input[b][x][y][c] = ((x*(c+2)*i**2+y*i*(c+16)+b**3)%1000)/1000.0
            update_img.eval(feed_dict={img_: img_input})
            summary_writer.add_summary(summary_op.eval(), i)