def main(params):
# main training and validation loop goes here
# This code should be independent of which model we use
batch_size = params["batch_size"]
max_epochs = params["max_epochs"]
# fetch the data provider object
dp = DataProvider(params)
# Get the solver object
solver = Solver(params["solver"])
## Add the model intiailization code here
modelObj = getModelObj(params)
# Now let's build a gradient computation graph and rmsprop update mechanism
# grads = tensor.grad(cost, wrt=model.values())
# lr = tensor.scalar(name='lr',dtype=config.floatX)
# f_grad_shared, f_update, zg, rg, ud = solver.build_solver_model(lr, model, grads,
# inp_list, cost, params)
num_frames_total = dp.getSplitSize("train")
num_iters_one_epoch = num_frames_total / batch_size
max_iters = max_epochs * num_iters_one_epoch
for it in xrange(max_iters):
batch = dp.getBatch(batch_size)
