from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import os
import shutil
import numpy as np
import deepchem as dc
from deepchem.molnet import load_sampl
# Only for debug!
np.random.seed(123)
# Load SAMPL dataset
n_features = 1024
SAMPL_tasks, SAMPL_datasets, transformers = load_sampl()
train_dataset, valid_dataset, test_dataset = SAMPL_datasets
# Fit models
metric = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean)
model = dc.models.TensorGraphMultiTaskRegressor(
len(SAMPL_tasks),
n_features,
layer_sizes=[1000],
dropouts=[.25],
learning_rate=0.001,
batch_size=50)
# Fit trained model
model.fit(train_dataset)
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import os
import shutil
import numpy as np
import deepchem as dc
from deepchem.molnet import load_sampl
# Only for debug!
np.random.seed(123)
# Load SAMPL dataset
n_features = 1024
SAMPL_tasks, SAMPL_datasets, transformers = load_sampl()
train_dataset, valid_dataset, test_dataset = SAMPL_datasets
# Fit models
metric = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean)
model = dc.models.MultitaskRegressor(
len(SAMPL_tasks),
n_features,
layer_sizes=[1000],
dropouts=[.25],
learning_rate=0.001,
batch_size=50)
# Fit trained model
model.fit(train_dataset)
""" Script that trains graph-conv models on Tox21 dataset. """ from __future__ import print_function from __future__ import division from __future__ import unicode_literals import numpy as np np.random.seed(123) import tensorflow as tf tf.set_random_seed(123) import deepchem as dc from deepchem.molnet import load_sampl # Load Tox21 dataset SAMPL_tasks, SAMPL_datasets, transformers = load_sampl(featurizer='GraphConv') train_dataset, valid_dataset, test_dataset = SAMPL_datasets # Fit models 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'))