/
muv_tf.py
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/
muv_tf.py
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"""
Script that trains TF multitask models on MUV dataset.
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import os
import numpy as np
import shutil
import deepchem as dc
from deepchem.molnet import load_muv
np.random.seed(123)
# Load MUV data
muv_tasks, muv_datasets, transformers = load_muv()
train_dataset, valid_dataset, test_dataset = muv_datasets
# Build model
metric = dc.metrics.Metric(
dc.metrics.roc_auc_score, np.mean, mode="classification")
rate = dc.models.optimizers.ExponentialDecay(0.001, 0.8, 1000)
model = dc.models.MultitaskClassifier(
len(muv_tasks),
n_features=1024,
dropouts=[.25],
learning_rate=rate,
weight_init_stddevs=[.1],
batch_size=64,
verbosity="high")
# Fit trained model
model.fit(train_dataset)
# Evaluate train/test scores
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)