--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/MLaaS/ktrain.py	Tue Mar 28 15:07:30 2023 +0000
@@ -0,0 +1,205 @@
+#!/usr/bin/env python
+#-*- coding: utf-8 -*-
+#pylint: disable=
+"""
+File       : ktrain.py
+Author     : Valentin Kuznetsov <vkuznet AT gmail dot com>
+Description: Keras based ML network to train over MNIST dataset
+"""
+
+# system modules
+import os
+import sys
+import json
+import gzip
+import pickle
+import argparse
+
+# third-party modules
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras import backend as K
+from tensorflow.python.tools import saved_model_utils
+
+
+def modelGraph(model_dir):
+    """
+    Provide input/output names used by TF Graph along with graph itself
+    The code is based on TF saved_model_cli.py script.
+    """
+    input_names = []
+    output_names = []
+    tag_sets = saved_model_utils.get_saved_model_tag_sets(model_dir)
+    for tag_set in sorted(tag_sets):
+        print('%r' % ', '.join(sorted(tag_set)))
+        meta_graph_def = saved_model_utils.get_meta_graph_def(model_dir, tag_set[0])
+        for key in meta_graph_def.signature_def.keys():
+            meta = meta_graph_def.signature_def[key]
+            if hasattr(meta, 'inputs') and hasattr(meta, 'outputs'):
+                inputs = meta.inputs
+                outputs = meta.outputs
+                input_signatures = list(meta.inputs.values())
+                input_names = [signature.name for signature in input_signatures]
+                if len(input_names) > 0:
+                    output_signatures = list(meta.outputs.values())
+                    output_names = [signature.name for signature in output_signatures]
+    return input_names, output_names, meta_graph_def
+
+def readData(fin, num_classes):
+    """
+    Helper function to read MNIST data and provide it to
+    upstream code, e.g. to the training layer
+    """
+    # Load the data and split it between train and test sets
+#     (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
+    f = gzip.open(fin, 'rb')
+    if sys.version_info < (3,):
+        mnist_data = pickle.load(f)
+    else:
+        mnist_data = pickle.load(f, encoding='bytes')
+    f.close()
+    (x_train, y_train), (x_test, y_test) = mnist_data
+
+    # Scale images to the [0, 1] range
+    x_train = x_train.astype("float32") / 255
+    x_test = x_test.astype("float32") / 255
+    # Make sure images have shape (28, 28, 1)
+    x_train = np.expand_dims(x_train, -1)
+    x_test = np.expand_dims(x_test, -1)
+    print("x_train shape:", x_train.shape)
+    print(x_train.shape[0], "train samples")
+    print(x_test.shape[0], "test samples")
+
+
+    # convert class vectors to binary class matrices
+    y_train = keras.utils.to_categorical(y_train, num_classes)
+    y_test = keras.utils.to_categorical(y_test, num_classes)
+    return x_train, y_train, x_test, y_test
+
+
+def train(fin, fout=None, model_name=None, epochs=1, batch_size=128, h5=False):
+    """
+    train function for MNIST
+    """
+    # Model / data parameters
+    num_classes = 10
+    input_shape = (28, 28, 1)
+
+    # create ML model
+    model = keras.Sequential(
+        [
+            keras.Input(shape=input_shape),
+            layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
+            layers.MaxPooling2D(pool_size=(2, 2)),
+            layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
+            layers.MaxPooling2D(pool_size=(2, 2)),
+            layers.Flatten(),
+            layers.Dropout(0.5),
+            layers.Dense(num_classes, activation="softmax"),
+        ]
+    )
+
+    model.summary()
+    print("model input", model.input, type(model.input), model.input.__dict__)
+    print("model output", model.output, type(model.output), model.output.__dict__)
+    model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
+
+    # train model
+    x_train, y_train, x_test, y_test = readData(fin, num_classes)
+    model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)
+
+    # evaluate trained model
+    score = model.evaluate(x_test, y_test, verbose=0)
+    print("Test loss:", score[0])
+    print("Test accuracy:", score[1])
+    print("save model to", fout)
+    writer(fout, model_name, model, input_shape, h5)
+
+def writer(fout, model_name, model, input_shape, h5=False):
+    """
+    Writer provide write function for given model
+    """
+    if not fout:
+        return
+    model.save(fout)
+    if h5:
+        model.save('{}/{}'.format(fout, h5), save_format='h5')
+    pbModel = '{}/saved_model.pb'.format(fout)
+    pbtxtModel = '{}/saved_model.pbtxt'.format(fout)
+    convert(pbModel, pbtxtModel)
+
+    # get meta-data information about our ML model
+    input_names, output_names, model_graph = modelGraph(model_name)
+    print("### input", input_names)
+    print("### output", output_names)
+    # ML uses (28,28,1) shape, i.e. 28x28 black-white images
+    # if we'll use color images we'll use shape (28, 28, 3)
+    img_channels = input_shape[2]  # last item represent number of colors
+    meta = {'name': model_name,
+            'model': 'saved_model.pb',
+            'labels': 'labels.txt',
+            'img_channels': img_channels,
+            'input_name': input_names[0].split(':')[0],
+            'output_name': output_names[0].split(':')[0],
+            'input_node': model.input.name,
+            'output_node': model.output.name
+    }
+    with open(fout+'/params.json', 'w') as ostream:
+        ostream.write(json.dumps(meta))
+    with open(fout+'/labels.txt', 'w') as ostream:
+        for i in range(0, 10):
+            ostream.write(str(i)+'\n')
+    with open(fout + '/model.graph', 'wb') as ostream:
+        ostream.write(model_graph.SerializeToString())
+
+def convert(fin, fout):
+    """
+    convert input model.pb into output model.pbtxt
+    Based on internet search:
+    - https://www.tensorflow.org/guide/saved_model
+    - https://www.programcreek.com/python/example/123317/tensorflow.core.protobuf.saved_model_pb2.SavedModel
+    """
+    import google.protobuf
+    from tensorflow.core.protobuf import saved_model_pb2
+    import tensorflow as tf
+
+    saved_model = saved_model_pb2.SavedModel()
+
+    with open(fin, 'rb') as f:
+        saved_model.ParseFromString(f.read())
+        
+    with open(fout, 'w') as f:
+        f.write(google.protobuf.text_format.MessageToString(saved_model))
+
+
+class OptionParser():
+    def __init__(self):
+        "User based option parser"
+        self.parser = argparse.ArgumentParser(prog='PROG')
+        self.parser.add_argument("--fin", action="store",
+            dest="fin", default="", help="Input MNIST file")
+        self.parser.add_argument("--fout", action="store",
+            dest="fout", default="", help="Output models area")
+        self.parser.add_argument("--model", action="store",
+            dest="model", default="mnist", help="model name")
+        self.parser.add_argument("--epochs", action="store",
+            dest="epochs", default=1, help="number of epochs to use in ML training")
+        self.parser.add_argument("--batch_size", action="store",
+            dest="batch_size", default=128, help="batch size to use in training")
+        self.parser.add_argument("--h5", action="store",
+            dest="h5", default="mnist", help="h5 model file name")
+
+def main():
+    "Main function"
+    optmgr  = OptionParser()
+    opts = optmgr.parser.parse_args()
+    train(opts.fin, opts.fout,
+          model_name=opts.model,
+          epochs=opts.epochs,
+          batch_size=opts.batch_size,
+          h5=opts.h5)
+
+if __name__ == '__main__':
+    main()