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# make things wide
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))

from IPython.display import clear_output, Image, display, HTML

def strip_consts(graph_def, max_const_size=32):
    """Strip large constant values from graph_def."""
    strip_def = tf.GraphDef()
    for n0 in graph_def.node:
        n = strip_def.node.add() 
        n.MergeFrom(n0)
        if n.op == 'Const':
            tensor = n.attr['value'].tensor
            size = len(tensor.tensor_content)
            if size > max_const_size:
                tensor.tensor_content = "<stripped %d bytes>"%size
    return strip_def

def show_graph(graph_def=None, width=1200, height=800, max_const_size=32, ungroup_gradients=False):
    if not graph_def:
        graph_def = tf.get_default_graph().as_graph_def()
        
    """Visualize TensorFlow graph."""
    if hasattr(graph_def, 'as_graph_def'):
        graph_def = graph_def.as_graph_def()
    strip_def = strip_consts(graph_def, max_const_size=max_const_size)
    data = str(strip_def)
    if ungroup_gradients:
        data = data.replace('"gradients/', '"b_')
        #print(data)
    code = """
        <script>
          function load() {{
            document.getElementById("{id}").pbtxt = {data};
          }}
        </script>
        <link rel="import" href="https://tensorboard.appspot.com/tf-graph-basic.build.html" onload=load()>
        <div style="height:600px">
          <tf-graph-basic id="{id}"></tf-graph-basic>
        </div>
    """.format(data=repr(data), id='graph'+str(np.random.rand()))

    iframe = """
        <iframe seamless style="width:{}px;height:{}px;border:0" srcdoc="{}"></iframe>
    """.format(width, height, code.replace('"', '&quot;'))
    display(HTML(iframe))


    

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import numpy as np
import os
import tensorflow as tf
from tensorflow.contrib.session_bundle import exporter
import time


    

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# If this errors out, increment the `export_version` variable, restart the Kernel, and re-run

flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_integer("batch_size", 10, "The batch size to train")
flags.DEFINE_integer("epoch_number", 10, "Number of epochs to run trainer")
flags.DEFINE_integer("steps_to_validate", 1,
                     "Steps to validate and print loss")
flags.DEFINE_string("checkpoint_dir", "./checkpoint/",
                    "indicates the checkpoint dirctory")
#flags.DEFINE_string("model_path", "./model/", "The export path of the model")
flags.DEFINE_string("model_path", "/root/pipeline/prediction.ml/tensorflow/models/tensorflow_minimal/export/", "The export path of the model")
flags.DEFINE_integer("export_version", 33, "The version number of the model")


    

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# If this errors out, increment the `export_version` variable, restart the Kernel, and re-run

def main():
  # Define training data
  x = np.ones(FLAGS.batch_size)
  y = np.ones(FLAGS.batch_size)

  # Define the model
  X = tf.placeholder(tf.float32, shape=[None], name="X")
  Y = tf.placeholder(tf.float32, shape=[None], name="yhat")
  w = tf.Variable(1.0, name="weight")
  b = tf.Variable(1.0, name="bias")
  loss = tf.square(Y - tf.mul(X, w) - b)
  train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
  predict_op  = tf.mul(X, w) + b

  saver = tf.train.Saver()
  checkpoint_dir = FLAGS.checkpoint_dir
  checkpoint_file = checkpoint_dir + "/checkpoint.ckpt"
  if not os.path.exists(checkpoint_dir):
    os.makedirs(checkpoint_dir)
    
  # Start the session
  with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())

    ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
    if ckpt and ckpt.model_checkpoint_path:
      print("Continue training from the model {}".format(ckpt.model_checkpoint_path))
      saver.restore(sess, ckpt.model_checkpoint_path)

    saver_def = saver.as_saver_def()
    print(saver_def.filename_tensor_name)
    print(saver_def.restore_op_name)

    # Start training
    start_time = time.time()
    for epoch in range(FLAGS.epoch_number):
      sess.run(train_op, feed_dict={X: x, Y: y})

      # Start validating
      if epoch % FLAGS.steps_to_validate == 0:
        end_time = time.time()
        print("[{}] Epoch: {}".format(end_time - start_time, epoch))

        saver.save(sess, checkpoint_file)
        tf.train.write_graph(sess.graph_def, checkpoint_dir, 'trained_model.pb', as_text=False)
        tf.train.write_graph(sess.graph_def, checkpoint_dir, 'trained_model.txt', as_text=True)

        start_time = end_time

    # Print model variables
    w_value, b_value = sess.run([w, b])
    print("The model of w: {}, b: {}".format(w_value, b_value))

    # Export the model
    print("Exporting trained model to {}".format(FLAGS.model_path))
    model_exporter = exporter.Exporter(saver)
    model_exporter.init(
      sess.graph.as_graph_def(),
      named_graph_signatures={
        'inputs': exporter.generic_signature({"features": X}),
        'outputs': exporter.generic_signature({"prediction": predict_op})
      })
    model_exporter.export(FLAGS.model_path, tf.constant(FLAGS.export_version), sess)
    print('Done exporting!')

if __name__ == "__main__":
  main()


    

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show_graph()


    

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!ls -l /root/pipeline/prediction.ml/tensorflow/models/tensorflow_minimal/export


    

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!ls -l /root/pipeline/prediction.ml/tensorflow/models/tensorflow_minimal/export/00000033