Tensorflow Manage Experiments

1. Visualize graphs with TensorBoard

# define model# launch a session to compute the graphwith tf.Session() as sess:    writer = tf.summary.FileWriter("./graphs", sess.graph)       #    for step in range(training_steps):        sess.run([optimizer])# Go to terminal, run:# $ python [yourprogram].py# $ tensorboard --logdir="./graphs" --port 6006# Then open your browser and go to: http://localhost:6006/

2. Saving and Restoring Variables

# checkpointglobal_step = tf.Variable(0, dtype=tf.int32, trainable=False, name="global_step")  # train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy,                                                   global_step = global_step)   # # define model# launch a session to compute the graphsaver = tf.train.Saver()    # with tf.Session() as sess:    for step in range(training_steps):        sess.run([train_op])        if (i + 1)% 300 == 0:  #             saver.save(sess, './checkpoints/ckpt', global_step=global_step)  # 

# define model# launch a session to compute the graphwith tf.Session() as sess:    ckpt = tf.train.get_checkpoint_state(os.path.dirname('./checkpoints/ckpt'))   #     if ckpt and ckpt.model_checkpoint_path:    #         saver.restore(sess, ckpt.model_checkpoint_path)   #     for step in range(training_steps):        sess.run([optimizer])   

3. Visualize our summary statistics during our training

# define modelwith tf.name_scope("summaries"):   #          tf.summary.image('input', x_image, 4) #      tf.summary.scalar("accuracy", accuracy)  #      tf.summary.histogram("loss", cross_entropy)#       summary_op = tf.summary.merge_all()  #  # launch a session to compute the graphwith tf.Session() as sess:        writer = tf.summary.FileWriter("./graphs", sess.graph)    #       for step in range(training_steps):        sess.run([train_op])        summary = sess.run(summary_op, feed_dict={x: batch[0], y_: batch[1],                                                   keep_prob: 0.5})       #           writer.add_summary(summary, global_step = i)     #