mehdidc

from nolearn.lasagne import NeuralNet, BatchIterator
from lasagne import layers, nonlinearities, updates, init, objectives
import numpy as np

class EarlyStopping(object):

    def __init__(self, patience=100, criterion='valid_loss',
                 criterion_smaller_is_better=True):
        self.patience = patience
        if criterion_smaller_is_better is True:

mehdidc

from nolearn.lasagne import NeuralNet, BatchIterator
from lasagne import layers, nonlinearities, updates, init, objectives

from nolearn.lasagne.base import objective
from lasagne.objectives import aggregate
from lasagne.regularization import regularize_layer_params, l2, l1

import numpy as np

mehdidc

from keras.layers import Dense, Input, Dropout
from keras.models import Sequential
from keras.optimizers import Adadelta
from sklearn.datasets import make_blobs
from keras.utils.np_utils import to_categorical
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.regularizers import l2, l1

import matplotlib.pyplot as plt

mehdidc

from keras.layers import Dense, Input, Dropout
from keras.models import Sequential
from keras.optimizers import Adadelta
from sklearn.datasets import make_blobs
from keras.utils.np_utils import to_categorical
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.regularizers import l2, l1

import matplotlib.pyplot as plt

mehdidc

from keras.layers import Dense, Input, Dropout
from keras.models import Sequential
from keras.optimizers import Adadelta
from sklearn.datasets import make_blobs
from keras.utils.np_utils import to_categorical
from keras.callbacks import EarlyStopping, ModelCheckpoint, LearningRateScheduler
from keras.regularizers import l2, l1

import matplotlib.pyplot as plt

mehdidc

from nolearn.lasagne import NeuralNet, BatchIterator
from lasagne import layers, nonlinearities, updates, init, objectives
import numpy as np
import theano

class EarlyStopping(object):

    def __init__(self, patience=100, criterion='valid_loss',
                 criterion_smaller_is_better=True):
        self.patience = patience

mehdidc

from skopt import gp_minimize, forest_minimize, dummy_minimize, gbrt_minimize
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import Matern

from skopt.benchmarks import branin

x0 = [[1, 2], [3, 4], [5, 6]]
y0 = map(branin, x0)

res = gp_minimize(branin,

mehdidc

def minibatcher(fn, batchsize=1000):
  """
  fn : a function that takes an input and returns an output
  batchsize : divide the total input into divisions of size batchsize at most
  
  iterate through all the divisions, call fn, get the results, 
  then concatenate all the results.
  """
  def f(X):
      results = []

mehdidc

import numpy as np
from keras.layers import GRU, initializations, K
from collections import OrderedDict

class GRULN(GRU):
    '''Gated Recurrent Unit with Layer Normalization
    Current impelemtation only works with consume_less = 'gpu' which is already
    set.

    # Arguments

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class EnsembleRegressor(object):
    def __init__(self, regs=None):
        self.regs = regs

    def fit(self, X, y):
        return self

    def predict(self, X, return_std=False):
        if return_std:
            means = []