updated files

import tensorflow as tf

import numpy as np

from deepchem.nn import model_ops, initializations, regularizers, activations

from deepchem.models.tensorgraph import model_ops, initializations, regularizers, activations

import math

class Hingeloss():

  def __init__(self,in_layers=None,weights,batch_size,labels,reg,**kwargs):

  def __init__(

      self,

      weights_initializer=tf.contrib.layers.variance_scaling_initializer,

      batch_size,

      labels,

      reg,

      **kwargs):

    self.weights_initializer = weights_initializer

    self.weights=weights

    self.batch_size=batch_size

    self.labels=labels

    self.reg=reg

    super(Hingeloss,self).__init__(in_layers,**kwargs)

  def create_tensor(self,in_layers=None,set_tensors=True,**kwargs):

    inputs=self.get_input_tensors(in_layers)

    weights, batch_size, labels, reg = inputs[0], inputs[1], inputs[2], inputs[

        3]

    weights,batch_size,labels,reg=inputs[0],inputs[1],inputs[2],inputs[3]

    scores=tf.matmul(batch_size,weights)

    labels_i=scores[np.arange(scores.shape[0]),labels]

    loss_i=tf.maximum(0,scores-tf.transpose(labels_i)+1)

    loss=tf.reduce_mean(tf.sum(loss_i),axis=1)

    loss = loss + 0.5 * reg * tf.sum(weights * weights)

    loss + = 0.5*reg*tf.sum(weights*weights)

    out_tensor=loss

    if set_tensors:

      self.out_tensor=out_tensor

from deepchem.models.tensorgraph.layers import Variable

from deepchem.models.tensorgraph.layers import VinaFreeEnergy

from deepchem.models.tensorgraph.layers import WeightedError

from deepchem.models.tensorgraph.layers import Hingeloss

from deepchem.models.tensorgraph.layers import WeightedLinearCombo

from deepchem.models.tensorgraph.IRV import IRVLayer

from deepchem.models.tensorgraph.IRV import IRVRegularize

      out_tensor=Hingeloss()(input_tensor)

      out_tensor=out_tensor.eval()

      assert out_tensor.shape == (batch_size,)