Revert Graph Conv For tox21

from __future__ import unicode_literals

import numpy as np

np.random.seed(123)

import tensorflow as tf

tf.set_random_seed(123)

metric = dc.metrics.Metric(

    dc.metrics.roc_auc_score, np.mean, mode="classification")

# Number of features on conv-mols

n_feat = 75

# Batch size of models

batch_size = 50

model = dc.models.tensorgraph.models.graph_conv_model(batch_size,

                                                      len(tox21_tasks))

model.fit(train_dataset, nb_epoch=10, checkpoint_interval=10)

graph_model = dc.nn.SequentialGraph(n_feat)

graph_model.add(dc.nn.GraphConv(64, n_feat, activation='relu'))

graph_model.add(dc.nn.BatchNormalization(epsilon=1e-5, mode=1))

graph_model.add(dc.nn.GraphPool())

graph_model.add(dc.nn.GraphConv(64, 64, activation='relu'))

graph_model.add(dc.nn.BatchNormalization(epsilon=1e-5, mode=1))

graph_model.add(dc.nn.GraphPool())

# Gather Projection

graph_model.add(dc.nn.Dense(128, 64, activation='relu'))

graph_model.add(dc.nn.BatchNormalization(epsilon=1e-5, mode=1))

graph_model.add(dc.nn.GraphGather(batch_size, activation="tanh"))

model = dc.models.MultitaskGraphClassifier(

    graph_model,

    len(tox21_tasks),

    n_feat,

    batch_size=batch_size,

    learning_rate=1e-3,

    learning_rate_decay_time=1000,

    optimizer_type="adam",

    beta1=.9,

    beta2=.999)

# Fit trained model

model.fit(train_dataset, nb_epoch=10)

print("Evaluating model")

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