def test_mapper_output(self): DS = datasetsIndex[1] data = DS(0) M = mappersIndex[1] m = M(data) Model = modelsIndex[3] X = m.X #pad_sequences(m.X, maxlen=100) print X.shape md = Model() # md.model.load_weights("weights") y = to_categorical(m.Y) loss = md.model.evaluate(X, y, batch_size=32, show_accuracy=True) print "Initial Loss and Accuracy: ", loss # md.model.fit(X, m.Y, batch_size=32, validation_split=0.5, nb_epoch=5, show_accuracy=True, verbose=1) # md.model.save_weights("weights", overwrite=True) # # yhat = md.model.predict_classes(X, batch_size=32) print "\n\nPredictions: ", np.min(yhat) print "\n\nPredictions: ", np.min(m.Y)
def evaluate(self, X, Y): y = to_categorical(Y) # out = self.model.evaluate( # X, y, # batch_size=32, # show_accuracy=True, # ) return BaseKeras.evaluate(self,X,y)
def train(self, X, Y, nepochs, callbacks): y = to_categorical(Y) BaseKeras.train(self,X,y,nepochs, callbacks)