_readme.md

Owl-Tensorflow Converter Example: MNIST CNN Training

This example is a example of MNIST-based CNN.

• Step 1 : running OCaml script tfgraph_train.ml, which generates a file tf_convert_mnist.pbtxt in current directory.
• Step 2 : make sure tf_convert_mnist.pbtxt and tfgraph_train.py in the same graph; make sure Tensorflow/numpy etc. is installed.
• Step 3 : execute python tf_converter_mnist.py, and the expected output on screen is the training progress. After each 100 steps, loss value and model accuracy will be shown.

Here we only assume the python script writer knows where to find the output node (in collection "result") and the placeholder names (x:0).

There could be many posssible source of error at this stage, one of which could be incompatible tensorflow version; in that case, probably find this line in test_cgraph.pbtxt : tensorflow_version: "1.12.0" and then change the version number. Also, Tensorflow may yield some warning messages about version/dataset etc. Current scripts may also ignore some factors like file/directory location.

tfgraph_train.ml

#!/usr/bin/env owl

#require "owl-tensorflow"

open Owl
open Owl_tensorflow
open Owl_converter

module G = Owl_computation_cpu_engine.Make (Dense.Ndarray.S)
module T = Owl_converter.Make (G)

module CGCompiler = Owl_neural_compiler.Make (G)
open CGCompiler.Neural
open CGCompiler.Neural.Graph
open CGCompiler.Neural.Algodiff


let make_network input_shape =
  input input_shape
  |> lambda (fun x -> Maths.(x / pack_flt 256.))
  |> conv2d [|5;5;1;32|] [|1;1|] ~act_typ:Activation.Relu
  |> max_pool2d [|2;2|] [|2;2|]
  |> fully_connected 1024 ~act_typ:Activation.Relu 
  |> linear 10 ~act_typ:Activation.(Softmax 1)
  |> get_network ~name:"mnist"


let network = make_network [|28;28;1|]
let _ = Graph.init network

let x = G.var_arr "x" ~shape:[|100;28;28;1|] |> pack_arr 
let y = Graph.forward network x |> fst 

let output = [| unpack_arr y |> G.arr_to_node |]
let input  = [| unpack_arr x |> G.arr_to_node |]
let cgraph = G.make_graph ~input ~output "graph_diff"

let s = G.graph_to_dot cgraph

let pbtxt = T.(convert cgraph |> to_pbtxt)
let _ = Owl_io.write_file "tf_convert_mnist.pbtxt" pbtxt

tfgraph_train.py

#!/usr/bin/env python

import numpy as np
import os
import tensorflow as tf
from google.protobuf import text_format
from tensorflow.python.framework import graph_io

os.environ['KMP_DUPLICATE_LIB_OK']='True'

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("../data/", one_hot=True)
batch_size = 100

filename = 'tf_convert_mnist'
with open(filename + '.pbtxt', 'r') as f:
  	metagraph_def = tf.compat.v1.MetaGraphDef()
  	file_content = f.read()
  	text_format.Merge(file_content,metagraph_def)
  	graph_io.write_graph(metagraph_def, 
  		os.path.dirname(filename),
    	os.path.basename(filename) + '.pb',
    	as_text=False)

input_data = np.random.rand(100, 28, 28, 1)

with tf.Graph().as_default():
    config = tf.compat.v1.ConfigProto()
    config.gpu_options.allow_growth = True

    sess = tf.compat.v1.Session(config=config)
    saver = tf.compat.v1.train.import_meta_graph(filename+'.pb')
    graph = tf.compat.v1.get_default_graph()

    x = graph.get_tensor_by_name('x:0')
    y = tf.compat.v1.placeholder("float", [None, 10])
    result = tf.compat.v1.get_collection("result")[0]

    correct_pred = tf.equal(tf.argmax(result, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

    # Extra tensors for training
    cross_entropy = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits_v2(
        labels=y, logits=result
    ))
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

    init = tf.global_variables_initializer()
    sess.run(init)

    # Begin training
    for i in range(2000):
        batch_x, batch_y = mnist.train.next_batch(batch_size)
        batch_x = np.reshape(batch_x, (-1,28,28,1))
        sess.run(train_step, feed_dict={x: batch_x, y: batch_y})
    
        if (i % 100 == 0):
            minibatch_loss, acc = sess.run([cross_entropy, accuracy], feed_dict={x: batch_x, y: batch_y})
            print("Loss:%s" % str(minibatch_loss))
            print("Accuracy:%s\n" % str(acc))

    # saver.save(sess, "owl_model.ckpt")