"""
Script that trains Tensorflow singletask models on QM7 dataset.
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import os
import deepchem as dc
import numpy as np
from deepchem.molnet import load_qm7_from_mat
np.random.seed(123)
qm7_tasks, datasets, transformers = load_qm7_from_mat(split='stratified')
train_dataset, valid_dataset, test_dataset = datasets
fit_transformers = [dc.trans.CoulombFitTransformer(train_dataset)]
regression_metric = [
dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"),
dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression")
]
model = dc.models.MultiTaskFitTransformRegressor(
n_tasks=1,
n_features=[23, 23],
learning_rate=0.001,
momentum=.8,
batch_size=25,
weight_init_stddevs=[1 / np.sqrt(400), 1 / np.sqrt(100), 1 / np.sqrt(100)],
bias_init_consts=[0., 0., 0.],
layer_sizes=[400, 100, 100],
dropouts=[0.01, 0.01, 0.01],
fit_transformers=fit_transformers,
"""
Script that trains Tensorflow singletask models on QM7 dataset.
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import os
import deepchem as dc
import numpy as np
from deepchem.molnet import load_qm7_from_mat
from deepchem.models.tensorgraph.optimizers import ExponentialDecay
np.random.seed(123)
qm7_tasks, datasets, transformers = load_qm7_from_mat(split='stratified',
move_mean=True)
train_dataset, valid_dataset, test_dataset = datasets
fit_transformers = [dc.trans.CoulombFitTransformer(train_dataset)]
metric = [
dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"),
dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression")
]
rate = 0.001
model = dc.models.MultiTaskFitTransformRegressor(
n_tasks=1,
n_features=[23, 23],
learning_rate=rate,
momentum=.8,
batch_size=25,
weight_init_stddevs=[1 / np.sqrt(400), 1 / np.sqrt(100), 1 / np.sqrt(100)],
"""
Script that trains Tensorflow singletask models on QM7 dataset.
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import os
import deepchem as dc
import numpy as np
from deepchem.molnet import load_qm7_from_mat
from deepchem.models.tensorgraph.optimizers import ExponentialDecay
np.random.seed(123)
qm7_tasks, datasets, transformers = load_qm7_from_mat(
split='stratified', move_mean=True)
train_dataset, valid_dataset, test_dataset = datasets
fit_transformers = [dc.trans.CoulombFitTransformer(train_dataset)]
metric = [
dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"),
dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression")
]
rate = 0.001
model = dc.models.MultitaskFitTransformRegressor(
n_tasks=1,
n_features=[23, 23],
learning_rate=rate,
momentum=.8,
batch_size=25,
weight_init_stddevs=[1 / np.sqrt(400), 1 / np.sqrt(100), 1 / np.sqrt(100)],