Mercurial > repos > bgruening > sklearn_discriminant_classifier

annotate keras_deep_learning.py @ 40:4335cec181ba draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f031d8ddfb73cec24572648666ac44ee47f08aad
author bgruening
date Thu, 11 Aug 2022 08:53:29 +0000
parents e76f6dfea5c9
children d769d83ec796
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1 import argparse
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2 import json
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3 import pickle
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4 import warnings
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5 from ast import literal_eval
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6
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7 import keras
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8 import pandas as pd
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9 import six
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10 from galaxy_ml.utils import get_search_params, SafeEval, try_get_attr
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11 from keras.models import Model, Sequential
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13 safe_eval = SafeEval()
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16 def _handle_shape(literal):
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17 """
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18 Eval integer or list/tuple of integers from string
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20 Parameters:
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21 -----------
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22 literal : str.
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23 """
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24 literal = literal.strip()
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25 if not literal:
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26 return None
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27 try:
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28 return literal_eval(literal)
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29 except NameError as e:
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30 print(e)
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31 return literal
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34 def _handle_regularizer(literal):
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35 """
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36 Construct regularizer from string literal
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38 Parameters
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39 ----------
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40 literal : str. E.g. '(0.1, 0)'
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41 """
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42 literal = literal.strip()
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43 if not literal:
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44 return None
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46 l1, l2 = literal_eval(literal)
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48 if not l1 and not l2:
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49 return None
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51 if l1 is None:
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52 l1 = 0.0
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53 if l2 is None:
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54 l2 = 0.0
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56 return keras.regularizers.l1_l2(l1=l1, l2=l2)
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59 def _handle_constraint(config):
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60 """
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61 Construct constraint from galaxy tool parameters.
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62 Suppose correct dictionary format
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63
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64 Parameters
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65 ----------
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66 config : dict. E.g.
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67 "bias_constraint":
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68 {"constraint_options":
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69 {"max_value":1.0,
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70 "min_value":0.0,
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71 "axis":"[0, 1, 2]"
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72 },
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73 "constraint_type":
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74 "MinMaxNorm"
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75 }
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76 """
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77 constraint_type = config["constraint_type"]
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78 if constraint_type in ("None", ""):
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79 return None
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81 klass = getattr(keras.constraints, constraint_type)
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82 options = config.get("constraint_options", {})
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83 if "axis" in options:
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84 options["axis"] = literal_eval(options["axis"])
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86 return klass(**options)
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88
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89 def _handle_lambda(literal):
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90 return None
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91
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92
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93 def _handle_layer_parameters(params):
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94 """
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95 Access to handle all kinds of parameters
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96 """
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97 for key, value in six.iteritems(params):
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98 if value in ("None", ""):
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99 params[key] = None
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100 continue
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101
37
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102 if type(value) in [int, float, bool] or (
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103 type(value) is str and value.isalpha()
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104 ):
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105 continue
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106
37
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107 if (
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108 key
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109 in [
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110 "input_shape",
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111 "noise_shape",
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112 "shape",
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113 "batch_shape",
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114 "target_shape",
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115 "dims",
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116 "kernel_size",
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117 "strides",
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118 "dilation_rate",
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119 "output_padding",
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120 "cropping",
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121 "size",
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122 "padding",
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123 "pool_size",
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124 "axis",
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125 "shared_axes",
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126 ]
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127 and isinstance(value, str)
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128 ):
26
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129 params[key] = _handle_shape(value)
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130
37
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131 elif key.endswith("_regularizer") and isinstance(value, dict):
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132 params[key] = _handle_regularizer(value)
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133
37
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134 elif key.endswith("_constraint") and isinstance(value, dict):
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135 params[key] = _handle_constraint(value)
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136
37
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137 elif key == "function": # No support for lambda/function eval
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138 params.pop(key)
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139
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140 return params
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141
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142
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143 def get_sequential_model(config):
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144 """
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145 Construct keras Sequential model from Galaxy tool parameters
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146
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147 Parameters:
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148 -----------
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149 config : dictionary, galaxy tool parameters loaded by JSON
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150 """
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151 model = Sequential()
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152 input_shape = _handle_shape(config["input_shape"])
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153 layers = config["layers"]
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154 for layer in layers:
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155 options = layer["layer_selection"]
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156 layer_type = options.pop("layer_type")
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157 klass = getattr(keras.layers, layer_type)
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158 kwargs = options.pop("kwargs", "")
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159
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160 # parameters needs special care
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161 options = _handle_layer_parameters(options)
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162
27
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163 if kwargs:
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164 kwargs = safe_eval("dict(" + kwargs + ")")
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165 options.update(kwargs)
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166
26
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167 # add input_shape to the first layer only
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168 if not getattr(model, "_layers") and input_shape is not None:
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169 options["input_shape"] = input_shape
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170
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171 model.add(klass(**options))
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172
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173 return model
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174
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175
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176 def get_functional_model(config):
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177 """
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178 Construct keras functional model from Galaxy tool parameters
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179
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180 Parameters
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181 -----------
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182 config : dictionary, galaxy tool parameters loaded by JSON
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183 """
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184 layers = config["layers"]
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185 all_layers = []
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186 for layer in layers:
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187 options = layer["layer_selection"]
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188 layer_type = options.pop("layer_type")
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189 klass = getattr(keras.layers, layer_type)
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190 inbound_nodes = options.pop("inbound_nodes", None)
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191 kwargs = options.pop("kwargs", "")
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192
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193 # parameters needs special care
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194 options = _handle_layer_parameters(options)
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195
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196 if kwargs:
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197 kwargs = safe_eval("dict(" + kwargs + ")")
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198 options.update(kwargs)
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199
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200 # merge layers
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201 if "merging_layers" in options:
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202 idxs = literal_eval(options.pop("merging_layers"))
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203 merging_layers = [all_layers[i - 1] for i in idxs]
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204 new_layer = klass(**options)(merging_layers)
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205 # non-input layers
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206 elif inbound_nodes is not None:
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207 new_layer = klass(**options)(all_layers[inbound_nodes - 1])
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208 # input layers
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209 else:
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210 new_layer = klass(**options)
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211
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212 all_layers.append(new_layer)
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213
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214 input_indexes = _handle_shape(config["input_layers"])
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215 input_layers = [all_layers[i - 1] for i in input_indexes]
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216
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217 output_indexes = _handle_shape(config["output_layers"])
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218 output_layers = [all_layers[i - 1] for i in output_indexes]
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219
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220 return Model(inputs=input_layers, outputs=output_layers)
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221
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222
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223 def get_batch_generator(config):
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224 """
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225 Construct keras online data generator from Galaxy tool parameters
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226
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227 Parameters
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228 -----------
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229 config : dictionary, galaxy tool parameters loaded by JSON
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230 """
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231 generator_type = config.pop("generator_type")
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232 if generator_type == "none":
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233 return None
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234
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235 klass = try_get_attr("galaxy_ml.preprocessors", generator_type)
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236
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237 if generator_type == "GenomicIntervalBatchGenerator":
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238 config["ref_genome_path"] = "to_be_determined"
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239 config["intervals_path"] = "to_be_determined"
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240 config["target_path"] = "to_be_determined"
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241 config["features"] = "to_be_determined"
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242 else:
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243 config["fasta_path"] = "to_be_determined"
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244
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245 return klass(**config)
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246
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247
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248 def config_keras_model(inputs, outfile):
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249 """
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250 config keras model layers and output JSON
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251
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252 Parameters
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253 ----------
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254 inputs : dict
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255 loaded galaxy tool parameters from `keras_model_config`
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256 tool.
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257 outfile : str
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258 Path to galaxy dataset containing keras model JSON.
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259 """
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260 model_type = inputs["model_selection"]["model_type"]
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261 layers_config = inputs["model_selection"]
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262
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263 if model_type == "sequential":
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264 model = get_sequential_model(layers_config)
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265 else:
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266 model = get_functional_model(layers_config)
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267
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268 json_string = model.to_json()
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269
37
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270 with open(outfile, "w") as f:
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271 json.dump(json.loads(json_string), f, indent=2)
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272
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273
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274 def build_keras_model(
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275 inputs,
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276 outfile,
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277 model_json,
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278 infile_weights=None,
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279 batch_mode=False,
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280 outfile_params=None,
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281 ):
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282 """
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283 for `keras_model_builder` tool
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284
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285 Parameters
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286 ----------
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287 inputs : dict
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288 loaded galaxy tool parameters from `keras_model_builder` tool.
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289 outfile : str
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290 Path to galaxy dataset containing the keras_galaxy model output.
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291 model_json : str
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292 Path to dataset containing keras model JSON.
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293 infile_weights : str or None
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294 If string, path to dataset containing model weights.
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295 batch_mode : bool, default=False
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296 Whether to build online batch classifier.
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297 outfile_params : str, default=None
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298 File path to search parameters output.
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299 """
37
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300 with open(model_json, "r") as f:
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301 json_model = json.load(f)
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302
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303 config = json_model["config"]
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304
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305 options = {}
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306
37
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307 if json_model["class_name"] == "Sequential":
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308 options["model_type"] = "sequential"
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309 klass = Sequential
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310 elif json_model["class_name"] == "Model":
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311 options["model_type"] = "functional"
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312 klass = Model
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313 else:
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314 raise ValueError("Unknow Keras model class: %s" % json_model["class_name"])
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315
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316 # load prefitted model
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317 if inputs["mode_selection"]["mode_type"] == "prefitted":
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318 estimator = klass.from_config(config)
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319 estimator.load_weights(infile_weights)
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320 # build train model
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321 else:
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322 cls_name = inputs["mode_selection"]["learning_type"]
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323 klass = try_get_attr("galaxy_ml.keras_galaxy_models", cls_name)
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324
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325 options["loss"] = inputs["mode_selection"]["compile_params"]["loss"]
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326 options["optimizer"] = (
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327 inputs["mode_selection"]["compile_params"]["optimizer_selection"][
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328 "optimizer_type"
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329 ]
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330 ).lower()
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331
37
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332 options.update(
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333 (
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334 inputs["mode_selection"]["compile_params"]["optimizer_selection"][
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335 "optimizer_options"
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336 ]
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337 )
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338 )
26
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339
37
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340 train_metrics = inputs["mode_selection"]["compile_params"]["metrics"]
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341 if train_metrics[-1] == "none":
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342 train_metrics = train_metrics[:-1]
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343 options["metrics"] = train_metrics
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344
37
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345 options.update(inputs["mode_selection"]["fit_params"])
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346 options["seed"] = inputs["mode_selection"]["random_seed"]
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347
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348 if batch_mode:
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349 generator = get_batch_generator(
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350 inputs["mode_selection"]["generator_selection"]
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351 )
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352 options["data_batch_generator"] = generator
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353 options["prediction_steps"] = inputs["mode_selection"]["prediction_steps"]
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354 options["class_positive_factor"] = inputs["mode_selection"][
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355 "class_positive_factor"
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356 ]
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357 estimator = klass(config, **options)
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358 if outfile_params:
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359 hyper_params = get_search_params(estimator)
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360 # TODO: remove this after making `verbose` tunable
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361 for h_param in hyper_params:
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362 if h_param[1].endswith("verbose"):
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363 h_param[0] = "@"
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364 df = pd.DataFrame(hyper_params, columns=["", "Parameter", "Value"])
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365 df.to_csv(outfile_params, sep="\t", index=False)
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366
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367 print(repr(estimator))
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368 # save model by pickle
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369 with open(outfile, "wb") as f:
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370 pickle.dump(estimator, f, pickle.HIGHEST_PROTOCOL)
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371
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372
37
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373 if __name__ == "__main__":
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374 warnings.simplefilter("ignore")
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375
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376 aparser = argparse.ArgumentParser()
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377 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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378 aparser.add_argument("-m", "--model_json", dest="model_json")
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379 aparser.add_argument("-t", "--tool_id", dest="tool_id")
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380 aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
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381 aparser.add_argument("-o", "--outfile", dest="outfile")
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382 aparser.add_argument("-p", "--outfile_params", dest="outfile_params")
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383 args = aparser.parse_args()
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384
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385 input_json_path = args.inputs
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386 with open(input_json_path, "r") as param_handler:
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387 inputs = json.load(param_handler)
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388
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389 tool_id = args.tool_id
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390 outfile = args.outfile
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391 outfile_params = args.outfile_params
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392 model_json = args.model_json
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393 infile_weights = args.infile_weights
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394
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395 # for keras_model_config tool
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396 if tool_id == "keras_model_config":
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397 config_keras_model(inputs, outfile)
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398
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399 # for keras_model_builder tool
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400 else:
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401 batch_mode = False
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402 if tool_id == "keras_batch_models":
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403 batch_mode = True
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404
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405 build_keras_model(
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406 inputs=inputs,
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407 model_json=model_json,
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408 infile_weights=infile_weights,
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409 batch_mode=batch_mode,
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410 outfile=outfile,
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411 outfile_params=outfile_params,
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412 )