Merge pull request #1310 from rbharath/sample

import tensorflow as tf

tf.set_random_seed(123)

import deepchem as dc

from deepchem.molnet import load_sampl

# Load SAMPL(FreeSolv) dataset

SAMPL_tasks, SAMPL_datasets, transformers = load_sampl(featurizer='GraphConv')

SAMPL_tasks, SAMPL_datasets, transformers = dc.molnet.load_sampl(

    featurizer='GraphConv')

train_dataset, valid_dataset, test_dataset = SAMPL_datasets

# Fit models

# Define metric

metric = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean)

# Do setup required for tf/keras models

# Number of features on conv-mols

n_feat = 75

# Batch size of models

batch_size = 128

graph_model = dc.nn.SequentialGraph(n_feat)

graph_model.add(dc.nn.GraphConv(128, 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(128, 128, 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(256, 128, activation='relu'))

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

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

# Dense post-processing layer

model = dc.models.MultitaskGraphRegressor(

    graph_model,

    len(SAMPL_tasks),

    n_feat,

    batch_size=batch_size,

    learning_rate=1e-3,

    learning_rate_decay_time=1000,

    optimizer_type="adam",

    beta1=.9,

    beta2=.999)

batch_size = 50

model = dc.models.GraphConvModel(len(SAMPL_tasks), mode='regression')

# Fit trained model

model.fit(train_dataset, nb_epoch=20)