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import pickle
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import numpy as np
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- from ibmfl . data .data_handler import DataHandler
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+ from ibm_watson_machine_learning . federated_learning .data_handler import DataHandler
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logger = logging .getLogger (__name__ )
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@@ -38,14 +38,14 @@ def get_data(self, nb_points=500):
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logger .info (
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'Loaded training data from ' + str (self .train_file_name ))
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with open (self .train_file_name , 'rb' ) as f :
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- (x_train , y_train )= pickle .load (f )
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+ (self . x_train , self . y_train )= pickle .load (f )
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logger .info (
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'Loaded test data from ' + str (self .test_file_name ))
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with open (self .test_file_name , 'rb' ) as f :
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- (x_test , y_test )= pickle .load (f )
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+ (self . x_test , self . y_test )= pickle .load (f )
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- x_train = x_train / 255.0
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- x_test = x_test / 255.0
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+ self . x_train = self . x_train / 255.0
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+ self . x_test = self . x_test / 255.0
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except Exception :
@@ -55,11 +55,11 @@ def get_data(self, nb_points=500):
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# Add a channels dimension
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import tensorflow as tf
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- x_train = x_train [..., tf .newaxis ]
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- x_test = x_test [..., tf .newaxis ]
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+ self . x_train = self . x_train [..., tf .newaxis ]
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+ self . x_test = self . x_test [..., tf .newaxis ]
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- print ('x_train shape:' , x_train .shape )
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- print (x_train .shape [0 ], 'train samples' )
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- print (x_test .shape [0 ], 'test samples' )
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+ print ('self. x_train shape:' , self . x_train .shape )
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+ print (self . x_train .shape [0 ], 'train samples' )
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+ print (self . x_test .shape [0 ], 'test samples' )
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- return (x_train , y_train ), (x_test , y_test )
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+ return (self . x_train , self . y_train ), (self . x_test , self . y_test )
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