def setupTrain(self):
# train_model is a function that updates the model parameters by SGD
opt = Optimizer(self.grads, self.params)
updates = opt.RMSProp(self.learning_rate, 0.9, 1.0/100.)
batch_size = self.cfgParams.batch_size
givens_train = {self.x: self.train_data_x[self.index * batch_size:(self.index + 1) * batch_size]}
givens_train[self.y] = self.train_data_y[self.index * batch_size:(self.index + 1) * batch_size]
print("compiling train_model() ... ")
self.train_model = theano.function(inputs=[self.index, self.learning_rate],
outputs=self.cost,
updates=updates,
givens=givens_train)
print("done.")
print("compiling test_model_on_train() ... ")
batch_size = self.cfgParams.batch_size
givens_test_on_train = {self.x: self.train_data_x[self.index * batch_size:(self.index + 1) * batch_size]}
givens_test_on_train[self.y] = self.train_data_y[self.index * batch_size:(self.index + 1) * batch_size]
self.test_model_on_train = theano.function(inputs=[self.index],
outputs=self.errors,
givens=givens_test_on_train)
print("done.")
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