debugging

      output = []

      with placeholder_scope:

        self.features = tf.placeholder(

            tf.float32, shape=[None, self.n_features], name='features')

            tf.float32, shape=[None, self.n_features], name='mol_features')

      with tf.name_scope('variable'):

        V = tf.Variable(tf.constant([0.01,1.]), name="vote", dtype=tf.float32)

        W = tf.Variable(tf.constant([1., 1.]), name="w", dtype=tf.float32)

        b = tf.Variable(tf.constant([0.01]), name="b", dtype=tf.float32)

        b2 = tf.Variable(tf.constant([0.01]), name="b2", dtype=tf.float32)

      for count in self.n_tasks:

      for count in range(self.n_tasks):

        similarity = self.features[:, 2*K*count:(2*K*count+K)]

        ys = tf.to_int32(self.features[:, (2*K*count+K):2*K*(count+1)])

        R = b+W[0]*similarity+W[1]*tf.constant(np.arange(K)+1, dtype=tf.float32)

        R = tf.sigmoid(R)

        z = tf.reduce_sum(R * tf.gather(V,ys), axis=1) + b2

        output.append(z)

        output.append(tf.reshape(z, shape=[-1,1]))

    return output

  def fit(self, dataset, nb_epoch=10, max_checkpoints_to_keep=5, log_every_N_batches=50, **kwargs):

fit_transformers = [dc.trans.IRVFitTransformer(K, len(tox21_tasks), train_dataset)]

model = dc.models.TensorflowMultiTaskIRVClassifier(

    len(tox21_tasks), K=K, n_features, learning_rate=0.001, penalty = 0.05, 

    len(tox21_tasks), K=K, learning_rate=0.001, penalty = 0.05, 

    batch_size=32, fit_transformers=fit_transformers)

# Fit trained model