skaae

class GruDenseLayer(lasagne.layers.Layer):
    def __init__(self, incoming, num_units,
                 b_resetgate=None,
                 b_updategate=None,
                 b_hidden_update=None,
                 W_resetgate=init.GlorotUniform(),
                 W_updategate=init.GlorotUniform(),
                 W_hidden_update=init.GlorotUniform(),
                 **kwargs):
        super(GruDenseLayer, self).__init__(incoming, **kwargs)

skaae

with open("/home/lpp/.bashrc", 'r') as f:
     lines = f.readlines()

with open('junk.txt', 'w') as f:
     f.write("".join(lines[1:5]


####### OR######

skaae

import numpy as np
import theano
import theano.tensor as T
import lasagne.nonlinearities as nonlinearities
import lasagne.init as init
from lasagne.utils import unroll_scan

from lasagne.layers import *
import lasagne.layers.helper as helper

skaae

--[[ A Confusion Matrix class

Example:

    conf = optim.ConfusionMatrix( {'cat','dog','person'} )   -- new matrix
    conf:zero()                                              -- reset matrix
    for i = 1,N do
        conf:add( neuralnet:forward(sample), label )         -- accumulate errors
    end
    print(conf)                                              -- print matrix

skaae

Function profiling
==================
  Message: /home/soren/Documents/experiments/TRANSFORMER_NET/grutranstest.py:271
  Time in 5 calls to Function.__call__: 1.034791e+01s
  Time in Function.fn.__call__: 1.034693e+01s (99.991%)
  Time in thunks: 1.022032e+01s (98.767%)
  Total compile time: 4.257923e+01s
    Number of Apply nodes: 1224
    Theano Optimizer time: 1.321189e+01s
       Theano validate time: 8.471053e-01s

skaae

class ElemwiseMergeLayer(MergeLayer):
    """
    This layer performs an elementwise merge of its input layers.
    It requires all input layers to have the same output shape.

    Parameters
    -----------
    incomings : a list of :class:`Layer` instances or tuples
        the layers feeding into this layer, or expected input shapes,
        with all incoming shapes being equal

skaae

import scipy.io
import lasagne
import theano
import theano.tensor as T
import numpy as np
import time

import logging

logger = logging.getLogger('')

skaae

    def get_output_for(self, input, mask=None, **kwargs):
        """
        Compute this layer's output function given a symbolic input variable

        Parameters
        ----------
        input : theano.TensorType
            Symbolic input variable.
        mask : theano.TensorType
            Theano variable denoting whether each time step in each

skaae

lasagne.updates

lasagne.updates

The update functions implement different methods to control the learning rate for use with stochastic gradient descent.

Update functions take a loss expression or a list of gradient expressions and a list of parameters as input and return an ordered dictionary of updates:

skaae

import numpy as np
class ConfusionMatrix:
    """
       Simple confusion matrix class
       row is the true class, column is the predicted class
    """
    def __init__(self, n_classes, class_names=None):
        self.n_classes = n_classes
        if class_names is None:
            self.class_names = map(str, range(n_classes))