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)