irv&lr fix

            bias_init=tf.constant(value=bias_init_consts[0],

                                  shape=[1]))

        lg_list.append(lg)

    return lg_list

  def add_label_placeholders(self, graph, name_scopes):

        # transfer 2D prediction tensor to 2D x n_classes(=2) 

        complimentary = np.ones(np.shape(batch_outputs))

        complimentary = complimentary - batch_outputs

        batch_outputs = np.squeeze(np.stack(arrays = [complimentary,

						      batch_outputs],

                                            axis = 2))

        batch_outputs = np.concatenate([complimentary, batch_outputs],

                                            axis = batch_outputs.ndim-1)

        # reshape to batch_size x n_tasks x ...

        if batch_outputs.ndim == 3:

          batch_outputs = batch_outputs.transpose((1, 0, 2))

"""

Script that trains multitask models on hiv dataset.

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

import numpy as np

import deepchem as dc

from hiv_datasets import load_hiv

# Only for debug!

np.random.seed(123)

# Load hiv dataset

n_features = 512

hiv_tasks, hiv_datasets, transformers = load_hiv()

train_dataset, valid_dataset, test_dataset = hiv_datasets

# Fit models

metric = dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean)

transformer = dc.trans.IRVTransformer(10, len(hiv_tasks), train_dataset)

train_dataset = transformer.transform(train_dataset)

valid_dataset = transformer.transform(valid_dataset)

model = dc.models.TensorflowMultiTaskIRVClassifier(

        len(hiv_tasks),

        K=10,

        batch_size=50,

        learning_rate=0.001)

# Fit trained model

model.fit(train_dataset)

model.save()

print("Evaluating model")

train_scores = model.evaluate(train_dataset, [metric], transformers)

valid_scores = model.evaluate(valid_dataset, [metric], transformers)

print("Train scores")

print(train_scores)

print("Validation scores")

print(valid_scores)