# Path: dsenser/theano_utils.py
# def floatX(a_data, a_dtype=config.floatX):
#     """Return numpy array populated with the given data.
# 
#     Args:
#       data (np.array):
#         input tensor
#       dtype (class):
#         digit type
# 
#     Returns:
#       np.array:
#         array populated with the given data
# 
#     """
#     return np.asarray(a_data, dtype=a_dtype)
# 
# def rmsprop(tparams, grads, x, y, cost):
#     """A variant of SGD that automatically scales the step size.
# 
#     Args:
#       tpramas (Theano SharedVariable):
#           Model parameters
#       grads (Theano variable):
#           Gradients of cost w.r.t to parameres
#       x (list):
#           Model inputs
#       y (Theano variable):
#           Targets
#       cost (Theano variable):
#           Objective fucntion to minimize
# 
#     Notes:
#       For more information, see [Hint2014]_.
# 
#     .. [Hint2014] Geoff Hinton, *Neural Networks for Machine Learning*,
#        lecture 6a,
#        http://cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
# 
#     """
#     zipped_grads = [theano.shared(p.get_value() * floatX(0.))
#                     for p in tparams]
#     running_grads = [theano.shared(p.get_value() * floatX(0.))
#                      for p in tparams]
#     running_grads2 = [theano.shared(p.get_value() * floatX(0.))
#                       for p in tparams]
# 
#     zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
#     rgup = [(rg, 0.95 * rg + 0.05 * g) for rg, g in zip(running_grads, grads)]
#     rg2up = [(rg2, 0.95 * rg2 + 0.05 * (g ** 2))
#              for rg2, g in zip(running_grads2, grads)]
# 
#     f_grad_shared = theano.function(x + [y], cost,
#                                     updates=zgup + rgup + rg2up,
#                                     name='rmsprop_f_grad_shared')
#     updir = [theano.shared(p.get_value() * floatX(0.))
#              for p in tparams]
#     updir_new = [(ud, 0.9 * ud - 1e-4 * zg / TT.sqrt(rg2 - rg ** 2 + 1e-4))
#                  for ud, zg, rg, rg2 in zip(updir, zipped_grads, running_grads,
#                                             running_grads2)]
#     param_up = [(p, p + udn[1])
#                 for p, udn in zip(tparams, updir_new)]
#     f_update = theano.function([], [], updates=updir_new + param_up,
#                                on_unused_input='ignore',
#                                name='rmsprop_f_update')
#     params = [zipped_grads, running_grads, running_grads2, updir]
#     return (f_grad_shared, f_update, params)
# 
# CONV_EPS = 1e-5
# 
# HE_UNIFORM = lambda x: floatX(_HE_UNIFORM.sample(x))
# 
# MAX_ITERS = 150  # 450


from dsenser.theano_utils import floatX, rmsprop, \
    CONV_EPS, HE_UNIFORM, MAX_ITERS
from datetime import datetime
from lasagne.init import HeUniform, Orthogonal
from theano import config, tensor as TT
import numpy as np
import sys
import theano
        #                           name="predict")
        gradients = TT.grad(cost, wrt=self._params)
        f_grad_shared, f_update, _ = rmsprop(self._params, gradients,
                                             [self.x], y_gold, cost)
        # perform actual training
        min_cost = INF
        best_params = []
        start_time = end_time = None
        time_delta = prev_icost = icost = 0.
        a_ts = [(floatX(x), floatX(y)) for x, y in a_ts]
        for i in xrange(MAX_ITERS):
            icost = 0.
            np.random.shuffle(a_ts)
            start_time = datetime.utcnow()
            for x_i, y_i in a_ts:
                try:
                    icost += f_grad_shared(x_i, y_i)
                    f_update()
                except Exception as e:
                    raise e
            if icost < min_cost:
                best_params = [p.get_value() for p in self._params]
                min_cost = icost
            end_time = datetime.utcnow()
            time_delta = (end_time - start_time).seconds
            print(
                "Iteration #{:d}: cost = {:f} ({:.2f} sec)".format(i,
                                                                   icost,
                                                                   time_delta),
                file=sys.stderr)

# Path: dsenser/theano_utils.py
# def floatX(a_data, a_dtype=config.floatX):
#     """Return numpy array populated with the given data.
# 
#     Args:
#       data (np.array):
#         input tensor
#       dtype (class):
#         digit type
# 
#     Returns:
#       np.array:
#         array populated with the given data
# 
#     """
#     return np.asarray(a_data, dtype=a_dtype)
# 
# def rmsprop(tparams, grads, x, y, cost):
#     """A variant of SGD that automatically scales the step size.
# 
#     Args:
#       tpramas (Theano SharedVariable):
#           Model parameters
#       grads (Theano variable):
#           Gradients of cost w.r.t to parameres
#       x (list):
#           Model inputs
#       y (Theano variable):
#           Targets
#       cost (Theano variable):
#           Objective fucntion to minimize
# 
#     Notes:
#       For more information, see [Hint2014]_.
# 
#     .. [Hint2014] Geoff Hinton, *Neural Networks for Machine Learning*,
#        lecture 6a,
#        http://cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
# 
#     """
#     zipped_grads = [theano.shared(p.get_value() * floatX(0.))
#                     for p in tparams]
#     running_grads = [theano.shared(p.get_value() * floatX(0.))
#                      for p in tparams]
#     running_grads2 = [theano.shared(p.get_value() * floatX(0.))
#                       for p in tparams]
# 
#     zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
#     rgup = [(rg, 0.95 * rg + 0.05 * g) for rg, g in zip(running_grads, grads)]
#     rg2up = [(rg2, 0.95 * rg2 + 0.05 * (g ** 2))
#              for rg2, g in zip(running_grads2, grads)]
# 
#     f_grad_shared = theano.function(x + [y], cost,
#                                     updates=zgup + rgup + rg2up,
#                                     name='rmsprop_f_grad_shared')
#     updir = [theano.shared(p.get_value() * floatX(0.))
#              for p in tparams]
#     updir_new = [(ud, 0.9 * ud - 1e-4 * zg / TT.sqrt(rg2 - rg ** 2 + 1e-4))
#                  for ud, zg, rg, rg2 in zip(updir, zipped_grads, running_grads,
#                                             running_grads2)]
#     param_up = [(p, p + udn[1])
#                 for p, udn in zip(tparams, updir_new)]
#     f_update = theano.function([], [], updates=updir_new + param_up,
#                                on_unused_input='ignore',
#                                name='rmsprop_f_update')
#     params = [zipped_grads, running_grads, running_grads2, updir]
#     return (f_grad_shared, f_update, params)
# 
# CONV_EPS = 1e-5
# 
# HE_UNIFORM = lambda x: floatX(_HE_UNIFORM.sample(x))
# 
# MAX_ITERS = 150  # 450


# Path: dsenser/judge/base.py
from dsenser.theano_utils import floatX, rmsprop, \
    CONV_EPS, HE_UNIFORM, MAX_ITERS
from datetime import datetime
from lasagne.init import HeUniform, Orthogonal
from theano import config, tensor as TT
import numpy as np
import sys
import theano

        #                           name="predict")
        gradients = TT.grad(cost, wrt=self._params)
        f_grad_shared, f_update, _ = rmsprop(self._params, gradients,
                                             [self.x], y_gold, cost)
        # perform actual training
        min_cost = INF
        best_params = []
        start_time = end_time = None
        time_delta = prev_icost = icost = 0.
        a_ts = [(floatX(x), floatX(y)) for x, y in a_ts]
        for i in xrange(MAX_ITERS):
            icost = 0.
            np.random.shuffle(a_ts)
            start_time = datetime.utcnow()
            for x_i, y_i in a_ts:
                try:
                    icost += f_grad_shared(x_i, y_i)
                    f_update()
                except Exception as e:
                    raise e
            if icost < min_cost:
                best_params = [p.get_value() for p in self._params]
                min_cost = icost
            end_time = datetime.utcnow()
            time_delta = (end_time - start_time).seconds
            print(
                "Iteration #{:d}: cost = {:f} ({:.2f} sec)".format(i,
                                                                   icost,
                                                                   time_delta),
                file=sys.stderr)

            if abs(prev_icost - icost) < CONV_EPS: