Mercurial > repos > kls286 > chap_test_20230328

annotate build/lib/MLaaS/ktrain.py @ 0:cbbe42422d56 draft

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planemo upload for repository https://github.com/CHESSComputing/ChessAnalysisPipeline/tree/galaxy commit 1401a7e1ae007a6bda260d147f9b879e789b73e0-dirty
author kls286
date Tue, 28 Mar 2023 15:07:30 +0000
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1 #!/usr/bin/env python
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2 #-*- coding: utf-8 -*-
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3 #pylint: disable=
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4 """
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5 File : ktrain.py
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6 Author : Valentin Kuznetsov <vkuznet AT gmail dot com>
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7 Description: Keras based ML network to train over MNIST dataset
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8 """
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9
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10 # system modules
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11 import os
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12 import sys
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13 import json
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14 import gzip
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15 import pickle
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16 import argparse
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17
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18 # third-party modules
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19 import numpy as np
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20 import tensorflow as tf
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21 from tensorflow import keras
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22 from tensorflow.keras import layers
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23 from tensorflow.keras import backend as K
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24 from tensorflow.python.tools import saved_model_utils
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26
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27 def modelGraph(model_dir):
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28 """
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29 Provide input/output names used by TF Graph along with graph itself
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30 The code is based on TF saved_model_cli.py script.
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31 """
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32 input_names = []
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33 output_names = []
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34 tag_sets = saved_model_utils.get_saved_model_tag_sets(model_dir)
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35 for tag_set in sorted(tag_sets):
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36 print('%r' % ', '.join(sorted(tag_set)))
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37 meta_graph_def = saved_model_utils.get_meta_graph_def(model_dir, tag_set[0])
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38 for key in meta_graph_def.signature_def.keys():
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39 meta = meta_graph_def.signature_def[key]
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40 if hasattr(meta, 'inputs') and hasattr(meta, 'outputs'):
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41 inputs = meta.inputs
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42 outputs = meta.outputs
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43 input_signatures = list(meta.inputs.values())
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44 input_names = [signature.name for signature in input_signatures]
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45 if len(input_names) > 0:
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46 output_signatures = list(meta.outputs.values())
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47 output_names = [signature.name for signature in output_signatures]
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48 return input_names, output_names, meta_graph_def
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49
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50 def readData(fin, num_classes):
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51 """
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52 Helper function to read MNIST data and provide it to
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53 upstream code, e.g. to the training layer
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54 """
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55 # Load the data and split it between train and test sets
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56 # (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
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57 f = gzip.open(fin, 'rb')
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58 if sys.version_info < (3,):
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59 mnist_data = pickle.load(f)
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60 else:
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61 mnist_data = pickle.load(f, encoding='bytes')
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62 f.close()
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63 (x_train, y_train), (x_test, y_test) = mnist_data
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64
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65 # Scale images to the [0, 1] range
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66 x_train = x_train.astype("float32") / 255
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67 x_test = x_test.astype("float32") / 255
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68 # Make sure images have shape (28, 28, 1)
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69 x_train = np.expand_dims(x_train, -1)
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70 x_test = np.expand_dims(x_test, -1)
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71 print("x_train shape:", x_train.shape)
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72 print(x_train.shape[0], "train samples")
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73 print(x_test.shape[0], "test samples")
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74
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75
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76 # convert class vectors to binary class matrices
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77 y_train = keras.utils.to_categorical(y_train, num_classes)
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78 y_test = keras.utils.to_categorical(y_test, num_classes)
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79 return x_train, y_train, x_test, y_test
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80
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81
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82 def train(fin, fout=None, model_name=None, epochs=1, batch_size=128, h5=False):
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83 """
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84 train function for MNIST
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85 """
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86 # Model / data parameters
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87 num_classes = 10
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88 input_shape = (28, 28, 1)
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89
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90 # create ML model
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91 model = keras.Sequential(
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92 [
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93 keras.Input(shape=input_shape),
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94 layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
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95 layers.MaxPooling2D(pool_size=(2, 2)),
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96 layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
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97 layers.MaxPooling2D(pool_size=(2, 2)),
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98 layers.Flatten(),
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99 layers.Dropout(0.5),
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100 layers.Dense(num_classes, activation="softmax"),
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101 ]
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102 )
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103
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104 model.summary()
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105 print("model input", model.input, type(model.input), model.input.__dict__)
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106 print("model output", model.output, type(model.output), model.output.__dict__)
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107 model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
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108
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109 # train model
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110 x_train, y_train, x_test, y_test = readData(fin, num_classes)
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111 model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)
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112
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113 # evaluate trained model
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114 score = model.evaluate(x_test, y_test, verbose=0)
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115 print("Test loss:", score[0])
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116 print("Test accuracy:", score[1])
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117 print("save model to", fout)
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118 writer(fout, model_name, model, input_shape, h5)
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119
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120 def writer(fout, model_name, model, input_shape, h5=False):
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121 """
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122 Writer provide write function for given model
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123 """
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124 if not fout:
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125 return
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126 model.save(fout)
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127 if h5:
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128 model.save('{}/{}'.format(fout, h5), save_format='h5')
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129 pbModel = '{}/saved_model.pb'.format(fout)
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130 pbtxtModel = '{}/saved_model.pbtxt'.format(fout)
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131 convert(pbModel, pbtxtModel)
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132
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133 # get meta-data information about our ML model
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134 input_names, output_names, model_graph = modelGraph(model_name)
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135 print("### input", input_names)
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136 print("### output", output_names)
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137 # ML uses (28,28,1) shape, i.e. 28x28 black-white images
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138 # if we'll use color images we'll use shape (28, 28, 3)
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139 img_channels = input_shape[2] # last item represent number of colors
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140 meta = {'name': model_name,
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141 'model': 'saved_model.pb',
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142 'labels': 'labels.txt',
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143 'img_channels': img_channels,
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144 'input_name': input_names[0].split(':')[0],
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145 'output_name': output_names[0].split(':')[0],
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146 'input_node': model.input.name,
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147 'output_node': model.output.name
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148 }
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149 with open(fout+'/params.json', 'w') as ostream:
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150 ostream.write(json.dumps(meta))
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151 with open(fout+'/labels.txt', 'w') as ostream:
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152 for i in range(0, 10):
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153 ostream.write(str(i)+'\n')
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154 with open(fout + '/model.graph', 'wb') as ostream:
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155 ostream.write(model_graph.SerializeToString())
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156
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157 def convert(fin, fout):
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158 """
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159 convert input model.pb into output model.pbtxt
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160 Based on internet search:
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161 - https://www.tensorflow.org/guide/saved_model
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162 - https://www.programcreek.com/python/example/123317/tensorflow.core.protobuf.saved_model_pb2.SavedModel
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163 """
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164 import google.protobuf
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165 from tensorflow.core.protobuf import saved_model_pb2
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166 import tensorflow as tf
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167
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168 saved_model = saved_model_pb2.SavedModel()
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169
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170 with open(fin, 'rb') as f:
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171 saved_model.ParseFromString(f.read())
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172
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173 with open(fout, 'w') as f:
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174 f.write(google.protobuf.text_format.MessageToString(saved_model))
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175
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176
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177 class OptionParser():
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178 def __init__(self):
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179 "User based option parser"
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180 self.parser = argparse.ArgumentParser(prog='PROG')
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181 self.parser.add_argument("--fin", action="store",
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182 dest="fin", default="", help="Input MNIST file")
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183 self.parser.add_argument("--fout", action="store",
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184 dest="fout", default="", help="Output models area")
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185 self.parser.add_argument("--model", action="store",
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186 dest="model", default="mnist", help="model name")
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187 self.parser.add_argument("--epochs", action="store",
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188 dest="epochs", default=1, help="number of epochs to use in ML training")
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189 self.parser.add_argument("--batch_size", action="store",
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190 dest="batch_size", default=128, help="batch size to use in training")
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191 self.parser.add_argument("--h5", action="store",
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192 dest="h5", default="mnist", help="h5 model file name")
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193
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194 def main():
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195 "Main function"
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196 optmgr = OptionParser()
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197 opts = optmgr.parser.parse_args()
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198 train(opts.fin, opts.fout,
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199 model_name=opts.model,
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200 epochs=opts.epochs,
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201 batch_size=opts.batch_size,
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202 h5=opts.h5)
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203
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204 if __name__ == '__main__':
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205 main()