import os
import numpy as np
import shutil
from pcba_datasets import load_pcba
from deepchem.utils.save import load_from_disk
from deepchem.data import Dataset
from deepchem import metrics
from deepchem.metrics import Metric
from deepchem.metrics import to_one_hot
from deepchem.utils.evaluate import Evaluator
from deepchem.models.tensorflow_models.fcnet import TensorflowMultiTaskClassifier
np.random.seed(123)
pcba_tasks, pcba_datasets, transformers = load_pcba()
(train_dataset, valid_dataset, test_dataset) = pcba_datasets
metric = Metric(metrics.roc_auc_score, np.mean, mode="classification")
n_features = train_dataset.get_data_shape()[0]
model_dir = None
model = TensorflowMultiTaskClassifier(len(pcba_tasks),
n_features,
model_dir,
dropouts=[.25],
learning_rate=0.001,
weight_init_stddevs=[.1],
batch_size=64,
verbosity="high")
from deepchem.models.tensorflow_models import TensorflowModel
np.random.seed(123)
# Set some global variables up top
reload = True
verbosity = "high"
base_dir = "/tmp/pcba_tf"
model_dir = os.path.join(base_dir, "model")
if os.path.exists(base_dir):
shutil.rmtree(base_dir)
os.makedirs(base_dir)
pcba_tasks, pcba_datasets, transformers = load_pcba(base_dir, reload=reload)
(train_dataset, valid_dataset) = pcba_datasets
classification_metric = Metric(metrics.roc_auc_score,
np.mean,
verbosity=verbosity,
mode="classification")
tensorflow_model = TensorflowMultiTaskClassifier(len(pcba_tasks),
n_features,
model_dir,
dropouts=[.25],
learning_rate=0.001,
weight_init_stddevs=[.1],
batch_size=64,
verbosity=verbosity)
from deepchem.models.tensorflow_models import TensorflowModel
np.random.seed(123)
# Set some global variables up top
reload = True
verbosity = "high"
base_dir = "/tmp/pcba_tf"
model_dir = os.path.join(base_dir, "model")
if os.path.exists(base_dir):
shutil.rmtree(base_dir)
os.makedirs(base_dir)
pcba_tasks, pcba_datasets, transformers = load_pcba(
base_dir, reload=reload)
(train_dataset, valid_dataset) = pcba_datasets
classification_metric = Metric(metrics.roc_auc_score, np.mean,
verbosity=verbosity,
mode="classification")
tensorflow_model = TensorflowMultiTaskClassifier(
len(pcba_tasks), n_features, model_dir, dropouts=[.25],
learning_rate=0.001, weight_init_stddevs=[.1],
batch_size=64, verbosity=verbosity)
model = TensorflowModel(tensorflow_model, model_dir)
# Fit trained model
model.fit(train_dataset)
import os
import numpy as np
import shutil
from pcba_datasets import load_pcba
from deepchem.utils.save import load_from_disk
from deepchem.data import Dataset
from deepchem import metrics
from deepchem.metrics import Metric
from deepchem.metrics import to_one_hot
from deepchem.utils.evaluate import Evaluator
from deepchem.models.tensorflow_models.fcnet import TensorflowMultiTaskClassifier
from deepchem.models.tensorflow_models import TensorflowModel
np.random.seed(123)
pcba_tasks, pcba_datasets, transformers = load_pcba()
(train_dataset, valid_dataset) = pcba_datasets
metric = Metric(metrics.roc_auc_score, np.mean,
mode="classification")
model = TensorflowMultiTaskClassifier(
len(pcba_tasks), n_features, model_dir, dropouts=[.25],
learning_rate=0.001, weight_init_stddevs=[.1],
batch_size=64, verbosity="high")
# Fit trained model
model.fit(train_dataset)
model.save()
