# Path: scripts/keras/keras/layers/core.py
# class Layer(object):
#     def __init__(self):
#         self.params = []
# 
#     def connect(self, node):
#         self.previous = node
# 
#     def get_output(self, train):
#         raise NotImplementedError
# 
#     def get_input(self, train):
#         if hasattr(self, 'previous'):
#             return self.previous.get_output(train=train)
#         else:
#             return self.input
# 
#     def set_weights(self, weights):
#         for p, w in zip(self.params, weights):
#             p.set_value(floatX(w))
# 
#     def get_weights(self):
#         weights = []
#         for p in self.params:
#             weights.append(p.get_value())
#         return weights
# 
#     def get_config(self):
#         return {"name":self.__class__.__name__}
# 
# Path: scripts/keras/keras/constraints.py
# def unitnorm(e):
#     return e / T.sqrt(T.sum(e**2, axis=-1, keepdims=True))


import theano
import theano.tensor as T
from .. import activations, initializations
from ..layers.core import Layer
from ..constraints import unitnorm
        @input_dim: size of vocabulary (highest input integer + 1)
        @out_dim: size of dense representation
    '''
    def __init__(self, input_dim, output_dim, init='uniform', weights=None, W_regularizer=None, W_constraint=None):
        super(Embedding,self).__init__()
        self.init = initializations.get(init)
        self.input_dim = input_dim
        self.output_dim = output_dim

        self.input = T.imatrix()
        self.W = self.init((self.input_dim, self.output_dim))
        self.params = [self.W]
        self.constraints = [W_constraint]
        self.regularizers = [W_regularizer]

        if weights is not None:
            self.set_weights(weights)

    def get_output(self, train=False):
        X = self.get_input(train)
        out = self.W[X]
        return out

    def get_config(self):
        return {"name":self.__class__.__name__,
            "input_dim":self.input_dim,
            "output_dim":self.output_dim,
            "init":self.init.__name__}



# Path: scripts/keras/keras/layers/core.py
# class Layer(object):
#     def __init__(self):
#         self.params = []
# 
#     def connect(self, node):
#         self.previous = node
# 
#     def get_output(self, train):
#         raise NotImplementedError
# 
#     def get_input(self, train):
#         if hasattr(self, 'previous'):
#             return self.previous.get_output(train=train)
#         else:
#             return self.input
# 
#     def set_weights(self, weights):
#         for p, w in zip(self.params, weights):
#             p.set_value(floatX(w))
# 
#     def get_weights(self):
#         weights = []
#         for p in self.params:
#             weights.append(p.get_value())
#         return weights
# 
#     def get_config(self):
#         return {"name":self.__class__.__name__}
# 
# Path: scripts/keras/keras/constraints.py
# def unitnorm(e):
#     return e / T.sqrt(T.sum(e**2, axis=-1, keepdims=True))


# Path: scripts/keras/keras/layers/embeddings.py
import theano
import theano.tensor as T
from .. import activations, initializations
from ..layers.core import Layer
from ..constraints import unitnorm

        @input_dim: size of vocabulary (highest input integer + 1)
        @out_dim: size of dense representation
    '''
    def __init__(self, input_dim, output_dim, init='uniform', weights=None, W_regularizer=None, W_constraint=None):
        super(Embedding,self).__init__()
        self.init = initializations.get(init)
        self.input_dim = input_dim
        self.output_dim = output_dim

        self.input = T.imatrix()
        self.W = self.init((self.input_dim, self.output_dim))
        self.params = [self.W]
        self.constraints = [W_constraint]
        self.regularizers = [W_regularizer]

        if weights is not None:
            self.set_weights(weights)

    def get_output(self, train=False):
        X = self.get_input(train)
        out = self.W[X]
        return out

    def get_config(self):
        return {"name":self.__class__.__name__,
            "input_dim":self.input_dim,
            "output_dim":self.output_dim,
            "init":self.init.__name__}



class WordContextProduct(Layer):