def test_single_task_classifier(self):
n_data_points = 20
n_features = 2
X = np.random.rand(n_data_points, n_features)
y = [[0, 1] for x in range(n_data_points)]
dataset = NumpyDataset(X, y)
features = Feature(shape=(None, n_features))
dense = Dense(out_channels=2, in_layers=[features])
output = SoftMax(in_layers=[dense])
label = Label(shape=(None, 2))
smce = SoftMaxCrossEntropy(in_layers=[label, dense])
loss = ReduceMean(in_layers=[smce])
tg = dc.models.TensorGraph(learning_rate=0.01)
tg.add_output(output)
tg.set_loss(loss)
tg.fit(dataset, nb_epoch=1000)
prediction = np.squeeze(tg.predict_on_batch(X))
assert_true(np.all(np.isclose(prediction, y, atol=0.4)))
def test_invoke_model_eager(self):
"""Test invoking the model with __call__() in eager mode."""
with context.eager_mode():
with tfe.IsolateTest():
batch_size = 5
tg = dc.models.TensorGraph(batch_size=batch_size)
features = Feature(shape=(None, 10))
dense = Dense(10, in_layers=features)
loss = ReduceMean(in_layers=dense)
tg.add_output(dense)
tg.set_loss(loss)
input = np.random.rand(batch_size, 10).astype(np.float32)
# We should get the same result with either predict_on_batch() or __call__().
output1 = tg.predict_on_batch(input)
output2 = tg(input)
assert np.allclose(output1, output2.numpy())
def test_initialize_variable(self):
"""Test methods for initializing a variable."""
# Set by variable constructor.
tg = dc.models.TensorGraph(use_queue=False)
features = Feature(shape=(None, 1))
tg.set_loss(Dense(1, in_layers=features))
var = Variable([10.0])
tg.add_output(var)
assert tg.predict_on_batch(np.zeros((1, 1))) == [10.0]
# Set by set_variable_initial_values().
tg = dc.models.TensorGraph(use_queue=False)
tg.set_loss(Dense(1, in_layers=features))
var.set_variable_initial_values([[15.0]])
tg.add_output(var)
assert tg.predict_on_batch(np.zeros((1, 1))) == [15.0]
def test_multi_task_regressor(self):
n_data_points = 20
n_features = 2
X = np.random.rand(n_data_points, n_features)
y1 = np.expand_dims(np.array([0.5 for x in range(n_data_points)]),
axis=-1)
y2 = np.expand_dims(np.array([-0.5 for x in range(n_data_points)]),
axis=-1)
X = NumpyDataset(X)
ys = [NumpyDataset(y1), NumpyDataset(y2)]
databag = Databag()
features = Feature(shape=(None, n_features))
databag.add_dataset(features, X)
outputs = []
losses = []
for i in range(2):
label = Label(shape=(None, 1))
dense = Dense(out_channels=1, in_layers=[features])
loss = ReduceSquareDifference(in_layers=[dense, label])
outputs.append(dense)
losses.append(loss)
databag.add_dataset(label, ys[i])
total_loss = ReduceMean(in_layers=losses)
tg = dc.models.TensorGraph(learning_rate=0.01)
for output in outputs:
tg.add_output(output)
tg.set_loss(total_loss)
tg.fit_generator(
databag.iterbatches(epochs=1000,
batch_size=tg.batch_size,
pad_batches=True))
predictions = tg.predict_on_generator(databag.iterbatches())
for i in range(2):
y_real = ys[i].X
y_pred = predictions[i]
assert_true(np.all(np.isclose(y_pred, y_real, atol=1.5)))
def test_multi_task_classifier(self):
n_data_points = 20
n_features = 2
X = np.random.rand(n_data_points, n_features)
y1 = np.array([[0, 1] for x in range(n_data_points)])
y2 = np.array([[1, 0] for x in range(n_data_points)])
X = NumpyDataset(X)
ys = [NumpyDataset(y1), NumpyDataset(y2)]
databag = Databag()
features = Feature(shape=(None, n_features))
databag.add_dataset(features, X)
outputs = []
entropies = []
for i in range(2):
label = Label(shape=(None, 2))
dense = Dense(out_channels=2, in_layers=[features])
output = SoftMax(in_layers=[dense])
smce = SoftMaxCrossEntropy(in_layers=[label, dense])
entropies.append(smce)
outputs.append(output)
databag.add_dataset(label, ys[i])
total_loss = ReduceMean(in_layers=entropies)
tg = dc.models.TensorGraph(learning_rate=0.01)
for output in outputs:
tg.add_output(output)
tg.set_loss(total_loss)
tg.fit_generator(
databag.iterbatches(epochs=1000,
batch_size=tg.batch_size,
pad_batches=True))
predictions = tg.predict_on_generator(databag.iterbatches())
for i in range(2):
y_real = ys[i].X
y_pred = predictions[i]
assert_true(np.all(np.isclose(y_pred, y_real, atol=0.6)))
def test_mnist(self):
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
train = dc.data.NumpyDataset(mnist.train.images, mnist.train.labels)
valid = dc.data.NumpyDataset(mnist.validation.images,
mnist.validation.labels)
# Images are square 28x28 (batch, height, width, channel)
feature = Feature(shape=(None, 784), name="Feature")
make_image = Reshape(shape=(-1, 28, 28, 1), in_layers=[feature])
conv2d_1 = Conv2d(num_outputs=32, in_layers=[make_image])
maxpool_1 = MaxPool(in_layers=[conv2d_1])
conv2d_2 = Conv2d(num_outputs=64, in_layers=[maxpool_1])
maxpool_2 = MaxPool(in_layers=[conv2d_2])
flatten = Flatten(in_layers=[maxpool_2])
dense1 = Dense(out_channels=1024,
activation_fn=tf.nn.relu,
in_layers=[flatten])
dense2 = Dense(out_channels=10, in_layers=[dense1])
label = Label(shape=(None, 10), name="Label")
smce = SoftMaxCrossEntropy(in_layers=[label, dense2])
loss = ReduceMean(in_layers=[smce])
output = SoftMax(in_layers=[dense2])
tg = dc.models.TensorGraph(model_dir='/tmp/mnist',
batch_size=1000,
use_queue=True)
tg.add_output(output)
tg.set_loss(loss)
tg.fit(train, nb_epoch=2)
prediction = np.squeeze(tg.predict_proba_on_batch(valid.X))
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(10):
fpr[i], tpr[i], thresh = roc_curve(valid.y[:, i], prediction[:, i])
roc_auc[i] = auc(fpr[i], tpr[i])
assert_true(roc_auc[i] > 0.99)
def test_shared_layer(self):
n_data_points = 20
n_features = 2
X = np.random.rand(n_data_points, n_features)
y1 = np.array([[0, 1] for x in range(n_data_points)])
X = NumpyDataset(X)
ys = [NumpyDataset(y1)]
databag = Databag()
features = Feature(shape=(None, n_features))
databag.add_dataset(features, X)
outputs = []
label = Label(shape=(None, 2))
dense1 = Dense(out_channels=2, in_layers=[features])
dense2 = dense1.shared(in_layers=[features])
output1 = SoftMax(in_layers=[dense1])
output2 = SoftMax(in_layers=[dense2])
smce = SoftMaxCrossEntropy(in_layers=[label, dense1])
outputs.append(output1)
outputs.append(output2)
databag.add_dataset(label, ys[0])
total_loss = ReduceMean(in_layers=[smce])
tg = dc.models.TensorGraph(learning_rate=0.01)
for output in outputs:
tg.add_output(output)
tg.set_loss(total_loss)
tg.fit_generator(
databag.iterbatches(epochs=1,
batch_size=tg.batch_size,
pad_batches=True))
prediction = tg.predict_on_generator(databag.iterbatches())
assert_true(np.all(np.isclose(prediction[0], prediction[1],
atol=0.01)))
def test_submodels(self):
"""Test optimizing submodels."""
tg = dc.models.TensorGraph(learning_rate=0.1, batch_size=1, use_queue=False)
features = Feature(shape=(None, 1))
var1 = Variable([2.0])
var2 = Variable([2.0])
tg.add_output(var1)
tg.add_output(var2)
loss = (var1 - 1) * (var1 - 1) + (var2 - 1) * (var2 - 1) + features
tg.set_loss(loss)
subloss1 = var1 * var1 + features
subloss2 = var1 * var1 + var2 * var2 + features
submodel1 = tg.create_submodel(loss=subloss1)
submodel2 = tg.create_submodel(layers=[var2], loss=subloss2)
data = np.zeros((1, 1))
generator = [{features: data}] * 500
# Optimize submodel 1. This should send var1 to 0 while leaving var2 unchanged.
tg.fit_generator(generator, submodel=submodel1)
self.assertAlmostEqual(
0.0, tg.predict_on_batch(data, outputs=var1)[0], places=4)
self.assertAlmostEqual(
2.0, tg.predict_on_batch(data, outputs=var2)[0], places=4)
# Optimize the main loss. This should send both variables toward 1.
tg.fit_generator(generator)
self.assertAlmostEqual(
1.0, tg.predict_on_batch(data, outputs=var1)[0], places=4)
self.assertAlmostEqual(
1.0, tg.predict_on_batch(data, outputs=var2)[0], places=4)
# Optimize submodel 2. This should send var2 to 0 while leaving var1 unchanged.
tg.fit_generator(generator, submodel=submodel2)
self.assertAlmostEqual(
1.0, tg.predict_on_batch(data, outputs=var1)[0], places=4)
self.assertAlmostEqual(
0.0, tg.predict_on_batch(data, outputs=var2)[0], places=4)
def test_compute_model_performance_multitask_classifier(self):
n_data_points = 20
n_features = 1
n_tasks = 2
n_classes = 2
X = np.ones(shape=(n_data_points // 2, n_features)) * -1
X1 = np.ones(shape=(n_data_points // 2, n_features))
X = np.concatenate((X, X1))
class_1 = np.array([[0.0, 1.0] for x in range(int(n_data_points / 2))])
class_0 = np.array([[1.0, 0.0] for x in range(int(n_data_points / 2))])
y1 = np.concatenate((class_0, class_1))
y2 = np.concatenate((class_1, class_0))
y = np.stack([y1, y2], axis=1)
dataset = NumpyDataset(X, y)
features = Feature(shape=(None, n_features))
label = Label(shape=(None, n_tasks, n_classes))
dense = Dense(out_channels=n_tasks * n_classes, in_layers=[features])
logits = Reshape(shape=(None, n_tasks, n_classes), in_layers=dense)
output = SoftMax(in_layers=[logits])
smce = SoftMaxCrossEntropy(in_layers=[label, logits])
total_loss = ReduceMean(in_layers=smce)
tg = dc.models.TensorGraph(learning_rate=0.01,
batch_size=n_data_points)
tg.add_output(output)
tg.set_loss(total_loss)
tg.fit(dataset, nb_epoch=1000)
metric = dc.metrics.Metric(dc.metrics.roc_auc_score,
np.mean,
mode="classification")
scores = tg.evaluate_generator(tg.default_generator(dataset), [metric],
labels=[label],
per_task_metrics=True)
scores = list(scores[1].values())
# Loosening atol to see if tests stop failing sporadically
assert_true(np.all(np.isclose(scores, [1.0, 1.0], atol=0.50)))
def build_graph(self):
print("building")
features = Feature(shape=(None, self.n_features))
last_layer = features
for layer_size in self.encoder_layers:
last_layer = Dense(in_layers=last_layer,
activation_fn=tf.nn.elu,
out_channels=layer_size)
self.mean = Dense(in_layers=last_layer,
activation_fn=None,
out_channels=1)
self.std = Dense(in_layers=last_layer,
activation_fn=None,
out_channels=1)
readout = CombineMeanStd([self.mean, self.std], training_only=True)
last_layer = readout
for layer_size in self.decoder_layers:
last_layer = Dense(in_layers=readout,
activation_fn=tf.nn.elu,
out_channels=layer_size)
self.reconstruction = Dense(in_layers=last_layer,
activation_fn=None,
out_channels=self.n_features)
weights = Weights(shape=(None, self.n_features))
reproduction_loss = L2Loss(
in_layers=[features, self.reconstruction, weights])
reproduction_loss = ReduceSum(in_layers=reproduction_loss, axis=0)
global_step = TensorWrapper(self._get_tf("GlobalStep"))
kl_loss = KLDivergenceLoss(
in_layers=[self.mean, self.std, global_step],
annealing_start_step=self.kl_annealing_start_step,
annealing_stop_step=self.kl_annealing_stop_step)
loss = Add(in_layers=[kl_loss, reproduction_loss], weights=[0.5, 1])
self.add_output(self.mean)
self.add_output(self.reconstruction)
self.set_loss(loss)
def _build_graph(self, tf_graph, scope, model_dir):
"""Construct a TensorGraph containing the policy and loss calculations."""
state_shape = self._env.state_shape
state_dtype = self._env.state_dtype
if not self._state_is_list:
state_shape = [state_shape]
state_dtype = [state_dtype]
features = []
for s, d in zip(state_shape, state_dtype):
features.append(Feature(shape=[None] + list(s), dtype=tf.as_dtype(d)))
policy_layers = self._policy.create_layers(features)
action_prob = policy_layers['action_prob']
value = policy_layers['value']
rewards = Weights(shape=(None,))
advantages = Weights(shape=(None,))
old_action_prob = Weights(shape=(None,))
actions = Label(shape=(None, self._env.n_actions))
loss = PPOLoss(
self.value_weight,
self.entropy_weight,
self.clipping_width,
in_layers=[
rewards, actions, action_prob, value, advantages, old_action_prob
])
graph = TensorGraph(
batch_size=self.max_rollout_length,
use_queue=False,
graph=tf_graph,
model_dir=model_dir)
for f in features:
graph._add_layer(f)
graph.add_output(action_prob)
graph.add_output(value)
graph.set_loss(loss)
graph.set_optimizer(self._optimizer)
with graph._get_tf("Graph").as_default():
with tf.variable_scope(scope):
graph.build()
assert len(loss.components) > 0
return graph, features, rewards, actions, action_prob, value, advantages, old_action_prob, loss.components
def test_copy_layers(self):
"""Test copying layers."""
tg = dc.models.TensorGraph()
features = Feature(shape=(None, 10))
dense = Dense(
10, in_layers=features, biases_initializer=tf.random_normal_initializer)
constant = Constant(10.0)
output = dense + constant
tg.add_output(output)
tg.set_loss(output)
tg.fit_generator([])
replacements = {constant: Constant(20.0)}
copy = output.copy(replacements, tg)
assert isinstance(copy, Add)
assert isinstance(copy.in_layers[0], Dense)
assert isinstance(copy.in_layers[0].in_layers[0], Feature)
assert copy.in_layers[1] == replacements[constant]
variables = tg.get_layer_variables(dense)
with tg._get_tf("Graph").as_default():
values = tg.session.run(variables)
for v1, v2 in zip(values, copy.in_layers[0].variable_values):
assert np.array_equal(v1, v2)
def build_graph(self):
"""Constructs the graph architecture of IRV as described in:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2750043/
"""
self.mol_features = Feature(shape=(None, self.n_features))
predictions = IRVLayer(self.n_tasks, self.K, in_layers=[self.mol_features])
costs = []
self.labels_fd = []
for task in range(self.n_tasks):
task_output = Slice(task, 1, in_layers=[predictions])
sigmoid = Sigmoid(in_layers=[task_output])
self.add_output(sigmoid)
label = Label(shape=(None, 1))
self.labels_fd.append(label)
cost = SigmoidCrossEntropy(in_layers=[label, task_output])
costs.append(cost)
all_cost = Concat(in_layers=costs, axis=1)
self.weights = Weights(shape=(None, self.n_tasks))
loss = WeightedError(in_layers=[all_cost, self.weights]) + \
IRVRegularize(predictions, self.penalty, in_layers=[predictions])
self.set_loss(loss)
def test_copy_layers_shared(self):
"""Test copying layers with shared variables."""
tg = dc.models.TensorGraph()
features = Feature(shape=(None, 10))
dense = Dense(
10, in_layers=features, biases_initializer=tf.random_normal_initializer)
constant = Constant(10.0)
output = dense + constant
tg.add_output(output)
tg.set_loss(output)
replacements = {features: features, constant: Constant(20.0)}
copy = output.copy(replacements, shared=True)
tg.add_output(copy)
assert isinstance(copy, Add)
assert isinstance(copy.in_layers[0], Dense)
assert isinstance(copy.in_layers[0].in_layers[0], Feature)
assert copy.in_layers[1] == replacements[constant]
variables1 = tg.get_layer_variables(dense)
variables2 = tg.get_layer_variables(copy.in_layers[0])
for v1, v2, in zip(variables1, variables2):
assert v1 == v2
feed_dict = {features: np.random.random((5, 10))}
v1, v2 = tg.predict_on_generator([feed_dict], outputs=[output, copy])
assert_true(np.all(np.isclose(v1 + 10, v2)))
def test_neighbor_list_simple(self):
"""Test that neighbor lists can be constructed."""
N_atoms = 10
start = 0
stop = 12
nbr_cutoff = 3
ndim = 3
M = 6
X = np.random.rand(N_atoms, ndim)
y = np.random.rand(N_atoms, 1)
dataset = NumpyDataset(X, y)
features = Feature(shape=(N_atoms, ndim))
labels = Label(shape=(N_atoms,))
nbr_list = NeighborList(
N_atoms, M, ndim, nbr_cutoff, start, stop, in_layers=[features])
nbr_list = ToFloat(in_layers=[nbr_list])
# This isn't a meaningful loss, but just for test
loss = ReduceSum(in_layers=[nbr_list])
tg = dc.models.TensorGraph(use_queue=False)
tg.add_output(nbr_list)
tg.set_loss(loss)
tg.build()
def _build_graph(self, tf_graph, scope, model_dir):
"""Construct a TensorGraph containing the policy and loss calculations."""
state_shape = self._env.state_shape
state_dtype = self._env.state_dtype
if not self._state_is_list:
state_shape = [state_shape]
state_dtype = [state_dtype]
features = []
for s, d in zip(state_shape, state_dtype):
features.append(
Feature(shape=[None] + list(s), dtype=tf.as_dtype(d)))
policy_layers = self._policy.create_layers(features)
action_prob = policy_layers['action_prob']
value = policy_layers['value']
search_prob = Label(shape=(None, self._env.n_actions))
search_value = Label(shape=(None, ))
loss = MCTSLoss(
self.value_weight,
in_layers=[action_prob, value, search_prob, search_value])
graph = TensorGraph(batch_size=self.max_search_depth,
use_queue=False,
graph=tf_graph,
model_dir=model_dir)
for f in features:
graph._add_layer(f)
graph.add_output(action_prob)
graph.add_output(value)
graph.set_loss(loss)
graph.set_optimizer(self._optimizer)
with graph._get_tf("Graph").as_default():
with tf.variable_scope(scope):
graph.build()
if len(graph.rnn_initial_states) > 0:
raise ValueError(
'MCTS does not support policies with recurrent layers')
return graph, features, action_prob, value, search_prob, search_value
def build_graph(self):
self.vertex_features = Feature(shape=(None, self.max_atoms, 75))
self.adj_matrix = Feature(shape=(None, self.max_atoms, 1,
self.max_atoms))
self.mask = Feature(shape=(None, self.max_atoms, 1))
gcnn1 = BatchNorm(
GraphCNN(
num_filters=64,
in_layers=[self.vertex_features, self.adj_matrix, self.mask]))
gcnn1 = Dropout(self.dropout, in_layers=gcnn1)
gcnn2 = BatchNorm(
GraphCNN(num_filters=64,
in_layers=[gcnn1, self.adj_matrix, self.mask]))
gcnn2 = Dropout(self.dropout, in_layers=gcnn2)
gc_pool, adj_matrix = GraphCNNPool(
num_vertices=32, in_layers=[gcnn2, self.adj_matrix, self.mask])
gc_pool = BatchNorm(gc_pool)
gc_pool = Dropout(self.dropout, in_layers=gc_pool)
gcnn3 = BatchNorm(
GraphCNN(num_filters=32, in_layers=[gc_pool, adj_matrix]))
gcnn3 = Dropout(self.dropout, in_layers=gcnn3)
gc_pool2, adj_matrix2 = GraphCNNPool(num_vertices=8,
in_layers=[gcnn3, adj_matrix])
gc_pool2 = BatchNorm(gc_pool2)
gc_pool2 = Dropout(self.dropout, in_layers=gc_pool2)
flattened = Flatten(in_layers=gc_pool2)
readout = Dense(out_channels=256,
activation_fn=tf.nn.relu,
in_layers=flattened)
costs = []
self.my_labels = []
for task in range(self.n_tasks):
if self.mode == 'classification':
classification = Dense(out_channels=2,
activation_fn=None,
in_layers=[readout])
softmax = SoftMax(in_layers=[classification])
self.add_output(softmax)
label = Label(shape=(None, 2))
self.my_labels.append(label)
cost = SoftMaxCrossEntropy(in_layers=[label, classification])
costs.append(cost)
if self.mode == 'regression':
regression = Dense(out_channels=1,
activation_fn=None,
in_layers=[readout])
self.add_output(regression)
label = Label(shape=(None, 1))
self.my_labels.append(label)
cost = L2Loss(in_layers=[label, regression])
costs.append(cost)
if self.mode == "classification":
entropy = Stack(in_layers=costs, axis=-1)
elif self.mode == "regression":
entropy = Stack(in_layers=costs, axis=1)
self.my_task_weights = Weights(shape=(None, self.n_tasks))
loss = WeightedError(in_layers=[entropy, self.my_task_weights])
self.set_loss(loss)
def build_graph(self):
"""Building graph structures:
Features => WeaveLayer => WeaveLayer => Dense => WeaveGather => Classification or Regression
"""
self.atom_features = Feature(shape=(None, self.n_atom_feat))
self.pair_features = Feature(shape=(None, self.n_pair_feat))
combined = Combine_AP(in_layers=[self.atom_features, self.pair_features])
self.pair_split = Feature(shape=(None,), dtype=tf.int32)
self.atom_split = Feature(shape=(None,), dtype=tf.int32)
self.atom_to_pair = Feature(shape=(None, 2), dtype=tf.int32)
weave_layer1 = WeaveLayer(
n_atom_input_feat=self.n_atom_feat,
n_pair_input_feat=self.n_pair_feat,
n_atom_output_feat=self.n_hidden,
n_pair_output_feat=self.n_hidden,
in_layers=[combined, self.pair_split, self.atom_to_pair])
weave_layer2 = WeaveLayer(
n_atom_input_feat=self.n_hidden,
n_pair_input_feat=self.n_hidden,
n_atom_output_feat=self.n_hidden,
n_pair_output_feat=self.n_hidden,
update_pair=False,
in_layers=[weave_layer1, self.pair_split, self.atom_to_pair])
separated = Separate_AP(in_layers=[weave_layer2])
dense1 = Dense(
out_channels=self.n_graph_feat,
activation_fn=tf.nn.tanh,
in_layers=[separated])
batch_norm1 = BatchNormalization(epsilon=1e-5, mode=1, in_layers=[dense1])
weave_gather = WeaveGather(
self.batch_size,
n_input=self.n_graph_feat,
gaussian_expand=True,
in_layers=[batch_norm1, self.atom_split])
costs = []
self.labels_fd = []
for task in range(self.n_tasks):
if self.mode == "classification":
classification = Dense(
out_channels=2, activation_fn=None, in_layers=[weave_gather])
softmax = SoftMax(in_layers=[classification])
self.add_output(softmax)
label = Label(shape=(None, 2))
self.labels_fd.append(label)
cost = SoftMaxCrossEntropy(in_layers=[label, classification])
costs.append(cost)
if self.mode == "regression":
regression = Dense(
out_channels=1, activation_fn=None, in_layers=[weave_gather])
self.add_output(regression)
label = Label(shape=(None, 1))
self.labels_fd.append(label)
cost = L2Loss(in_layers=[label, regression])
costs.append(cost)
if self.mode == "classification":
all_cost = Concat(in_layers=costs, axis=1)
elif self.mode == "regression":
all_cost = Stack(in_layers=costs, axis=1)
self.weights = Weights(shape=(None, self.n_tasks))
loss = WeightedError(in_layers=[all_cost, self.weights])
self.set_loss(loss)
def __init__(self,
n_tasks,
n_features,
layer_sizes=[1000],
weight_init_stddevs=0.02,
bias_init_consts=1.0,
weight_decay_penalty=0.0,
weight_decay_penalty_type="l2",
dropouts=0.5,
activation_fns=tf.nn.relu,
n_classes=2,
**kwargs):
"""Create a MultitaskClassifier.
In addition to the following arguments, this class also accepts
all the keyword arguments from TensorGraph.
Parameters
----------
n_tasks: int
number of tasks
n_features: int
number of features
layer_sizes: list
the size of each dense layer in the network. The length of
this list determines the number of layers.
weight_init_stddevs: list or float
the standard deviation of the distribution to use for weight
initialization of each layer. The length of this list should
equal len(layer_sizes). Alternatively this may be a single
value instead of a list, in which case the same value is used
for every layer.
bias_init_consts: list or loat
the value to initialize the biases in each layer to. The
length of this list should equal len(layer_sizes).
Alternatively this may be a single value instead of a list, in
which case the same value is used for every layer.
weight_decay_penalty: float
the magnitude of the weight decay penalty to use
weight_decay_penalty_type: str
the type of penalty to use for weight decay, either 'l1' or 'l2'
dropouts: list or float
the dropout probablity to use for each layer. The length of this list should equal len(layer_sizes).
Alternatively this may be a single value instead of a list, in which case the same value is used for every layer.
activation_fns: list or object
the Tensorflow activation function to apply to each layer. The length of this list should equal
len(layer_sizes). Alternatively this may be a single value instead of a list, in which case the
same value is used for every layer.
n_classes: int
the number of classes
"""
super(MultitaskClassifier, self).__init__(**kwargs)
self.n_tasks = n_tasks
self.n_features = n_features
self.n_classes = n_classes
n_layers = len(layer_sizes)
if not isinstance(weight_init_stddevs, collections.Sequence):
weight_init_stddevs = [weight_init_stddevs] * n_layers
if not isinstance(bias_init_consts, collections.Sequence):
bias_init_consts = [bias_init_consts] * n_layers
if not isinstance(dropouts, collections.Sequence):
dropouts = [dropouts] * n_layers
if not isinstance(activation_fns, collections.Sequence):
activation_fns = [activation_fns] * n_layers
# Add the input features.
mol_features = Feature(shape=(None, n_features))
prev_layer = mol_features
# Add the dense layers
for size, weight_stddev, bias_const, dropout, activation_fn in zip(
layer_sizes, weight_init_stddevs, bias_init_consts, dropouts,
activation_fns):
layer = Dense(in_layers=[prev_layer],
out_channels=size,
activation_fn=activation_fn,
weights_initializer=TFWrapper(
tf.truncated_normal_initializer,
stddev=weight_stddev),
biases_initializer=TFWrapper(tf.constant_initializer,
value=bias_const))
if dropout > 0.0:
layer = Dropout(dropout, in_layers=[layer])
prev_layer = layer
# Compute the loss function for each label.
self.neural_fingerprint = prev_layer
logits = Reshape(shape=(-1, n_tasks, n_classes),
in_layers=[
Dense(in_layers=[prev_layer],
out_channels=n_tasks * n_classes)
])
output = SoftMax(logits)
self.add_output(output)
labels = Label(shape=(None, n_tasks, n_classes))
weights = Weights(shape=(None, n_tasks))
loss = SoftMaxCrossEntropy(in_layers=[labels, logits])
weighted_loss = WeightedError(in_layers=[loss, weights])
if weight_decay_penalty != 0.0:
weighted_loss = WeightDecay(weight_decay_penalty,
weight_decay_penalty_type,
in_layers=[weighted_loss])
self.set_loss(weighted_loss)
def graph_conv_net(batch_size, prior, num_task):
"""
Build a tensorgraph for multilabel classification task
Return: features and labels layers
"""
tg = TensorGraph(use_queue=False)
if prior == True:
add_on = num_task
else:
add_on = 0
atom_features = Feature(shape=(None, 75 + 2 * add_on))
circular_features = Feature(shape=(batch_size, 256), dtype=tf.float32)
degree_slice = Feature(shape=(None, 2), dtype=tf.int32)
membership = Feature(shape=(None, ), dtype=tf.int32)
deg_adjs = []
for i in range(0, 10 + 1):
deg_adj = Feature(shape=(None, i + 1), dtype=tf.int32)
deg_adjs.append(deg_adj)
gc1 = GraphConv(64 + add_on,
activation_fn=tf.nn.elu,
in_layers=[atom_features, degree_slice, membership] +
deg_adjs)
batch_norm1 = BatchNorm(in_layers=[gc1])
gp1 = GraphPool(in_layers=[batch_norm1, degree_slice, membership] +
deg_adjs)
gc2 = GraphConv(64 + add_on,
activation_fn=tf.nn.elu,
in_layers=[gc1, degree_slice, membership] + deg_adjs)
batch_norm2 = BatchNorm(in_layers=[gc2])
gp2 = GraphPool(in_layers=[batch_norm2, degree_slice, membership] +
deg_adjs)
add = Concat(in_layers=[gp1, gp2])
add = Dropout(0.5, in_layers=[add])
dense = Dense(out_channels=128, activation_fn=tf.nn.elu, in_layers=[add])
batch_norm3 = BatchNorm(in_layers=[dense])
readout = GraphGather(batch_size=batch_size,
activation_fn=tf.nn.tanh,
in_layers=[batch_norm3, degree_slice, membership] +
deg_adjs)
batch_norm4 = BatchNorm(in_layers=[readout])
dense1 = Dense(out_channels=128,
activation_fn=tf.nn.elu,
in_layers=[circular_features])
dense1 = BatchNorm(in_layers=[dense1])
dense1 = Dropout(0.5, in_layers=[dense1])
dense1 = Dense(out_channels=128,
activation_fn=tf.nn.elu,
in_layers=[circular_features])
dense1 = BatchNorm(in_layers=[dense1])
dense1 = Dropout(0.5, in_layers=[dense1])
merge_feat = Concat(in_layers=[dense1, batch_norm4])
merge = Dense(out_channels=256,
activation_fn=tf.nn.elu,
in_layers=[merge_feat])
costs = []
labels = []
for task in range(num_task):
classification = Dense(out_channels=2,
activation_fn=None,
in_layers=[merge])
softmax = SoftMax(in_layers=[classification])
tg.add_output(softmax)
label = Label(shape=(None, 2))
labels.append(label)
cost = SoftMaxCrossEntropy(in_layers=[label, classification])
costs.append(cost)
all_cost = Stack(in_layers=costs, axis=1)
weights = Weights(shape=(None, num_task))
loss = WeightedError(in_layers=[all_cost, weights])
tg.set_loss(loss)
#if prior == True:
# return tg, atom_features,circular_features, degree_slice, membership, deg_adjs, labels, weights#, prior_layer
return tg, atom_features, circular_features, degree_slice, membership, deg_adjs, labels, weights
def build_graph(self):
"""
Building graph structures:
"""
self.atom_features = Feature(shape=(None, 75))
self.degree_slice = Feature(shape=(None, 2), dtype=tf.int32)
self.membership = Feature(shape=(None,), dtype=tf.int32)
self.deg_adjs = []
for i in range(0, 10 + 1):
deg_adj = Feature(shape=(None, i + 1), dtype=tf.int32)
self.deg_adjs.append(deg_adj)
gc1 = GraphConv(
64,
activation_fn=tf.nn.relu,
in_layers=[self.atom_features, self.degree_slice, self.membership] +
self.deg_adjs)
batch_norm1 = BatchNorm(in_layers=[gc1])
gp1 = GraphPool(in_layers=[batch_norm1, self.degree_slice, self.membership]
+ self.deg_adjs)
gc2 = GraphConv(
64,
activation_fn=tf.nn.relu,
in_layers=[gp1, self.degree_slice, self.membership] + self.deg_adjs)
batch_norm2 = BatchNorm(in_layers=[gc2])
gp2 = GraphPool(in_layers=[batch_norm2, self.degree_slice, self.membership]
+ self.deg_adjs)
dense = Dense(out_channels=128, activation_fn=tf.nn.relu, in_layers=[gp2])
batch_norm3 = BatchNorm(in_layers=[dense])
readout = GraphGather(
batch_size=self.batch_size,
activation_fn=tf.nn.tanh,
in_layers=[batch_norm3, self.degree_slice, self.membership] +
self.deg_adjs)
if self.error_bars == True:
readout = Dropout(in_layers=[readout], dropout_prob=0.2)
costs = []
self.my_labels = []
for task in range(self.n_tasks):
if self.mode == 'classification':
classification = Dense(
out_channels=2, activation_fn=None, in_layers=[readout])
softmax = SoftMax(in_layers=[classification])
self.add_output(softmax)
label = Label(shape=(None, 2))
self.my_labels.append(label)
cost = SoftMaxCrossEntropy(in_layers=[label, classification])
costs.append(cost)
if self.mode == 'regression':
regression = Dense(
out_channels=1, activation_fn=None, in_layers=[readout])
self.add_output(regression)
label = Label(shape=(None, 1))
self.my_labels.append(label)
cost = L2Loss(in_layers=[label, regression])
costs.append(cost)
if self.mode == "classification":
entropy = Concat(in_layers=costs, axis=-1)
elif self.mode == "regression":
entropy = Stack(in_layers=costs, axis=1)
self.my_task_weights = Weights(shape=(None, self.n_tasks))
loss = WeightedError(in_layers=[entropy, self.my_task_weights])
self.set_loss(loss)
def graph_conv_model(batch_size, tasks):
model = TensorGraph(model_dir=model_dir,
batch_size=batch_size,
use_queue=False)
atom_features = Feature(shape=(None, 75))
degree_slice = Feature(shape=(None, 2), dtype=tf.int32)
membership = Feature(shape=(None, ), dtype=tf.int32)
deg_adjs = []
for i in range(0, 10 + 1):
deg_adj = Feature(shape=(None, i + 1), dtype=tf.int32)
deg_adjs.append(deg_adj)
gc1 = GraphConv(64,
activation_fn=tf.nn.relu,
in_layers=[atom_features, degree_slice, membership] +
deg_adjs)
batch_norm1 = BatchNorm(in_layers=[gc1])
gp1 = GraphPool(in_layers=[batch_norm1, degree_slice, membership] +
deg_adjs)
gc2 = GraphConv(64,
activation_fn=tf.nn.relu,
in_layers=[gp1, degree_slice, membership] + deg_adjs)
batch_norm2 = BatchNorm(in_layers=[gc2])
gp2 = GraphPool(in_layers=[batch_norm2, degree_slice, membership] +
deg_adjs)
dense = Dense(out_channels=128, activation_fn=None, in_layers=[gp2])
batch_norm3 = BatchNorm(in_layers=[dense])
gg1 = GraphGather(batch_size=batch_size,
activation_fn=tf.nn.tanh,
in_layers=[batch_norm3, degree_slice, membership] +
deg_adjs)
costs = []
labels = []
for task in tasks:
classification = Dense(out_channels=2,
activation_fn=None,
in_layers=[gg1])
softmax = SoftMax(in_layers=[classification])
model.add_output(softmax)
label = Label(shape=(None, 2))
labels.append(label)
cost = SoftMaxCrossEntropy(in_layers=[label, classification])
costs.append(cost)
entropy = Concat(in_layers=costs)
task_weights = Weights(shape=(None, len(tasks)))
loss = WeightedError(in_layers=[entropy, task_weights])
model.set_loss(loss)
def feed_dict_generator(dataset, batch_size, epochs=1):
for epoch in range(epochs):
for ind, (X_b, y_b, w_b, ids_b) in enumerate(
dataset.iterbatches(batch_size, pad_batches=True)):
d = {}
for index, label in enumerate(labels):
d[label] = to_one_hot(y_b[:, index])
d[task_weights] = w_b
multiConvMol = ConvMol.agglomerate_mols(X_b)
d[atom_features] = multiConvMol.get_atom_features()
d[degree_slice] = multiConvMol.deg_slice
d[membership] = multiConvMol.membership
for i in range(1, len(multiConvMol.get_deg_adjacency_lists())):
d[deg_adjs[i -
1]] = multiConvMol.get_deg_adjacency_lists()[i]
yield d
return model, feed_dict_generator, labels, task_weights
def atomic_conv_model(frag1_num_atoms=70,
frag2_num_atoms=634,
complex_num_atoms=701,
max_num_neighbors=12,
batch_size=24,
at=[
6, 7., 8., 9., 11., 12., 15., 16., 17., 20., 25.,
30., 35., 53., -1.
],
radial=[[
1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0,
6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0,
11.5, 12.0
], [0.0, 4.0, 8.0], [0.4]],
layer_sizes=[32, 32, 16],
learning_rate=0.001):
rp = [x for x in itertools.product(*radial)]
frag1_X = Feature(shape=(batch_size, frag1_num_atoms, 3))
frag1_nbrs = Feature(shape=(batch_size, frag1_num_atoms,
max_num_neighbors))
frag1_nbrs_z = Feature(shape=(batch_size, frag1_num_atoms,
max_num_neighbors))
frag1_z = Feature(shape=(batch_size, frag1_num_atoms))
frag2_X = Feature(shape=(batch_size, frag2_num_atoms, 3))
frag2_nbrs = Feature(shape=(batch_size, frag2_num_atoms,
max_num_neighbors))
frag2_nbrs_z = Feature(shape=(batch_size, frag2_num_atoms,
max_num_neighbors))
frag2_z = Feature(shape=(batch_size, frag2_num_atoms))
complex_X = Feature(shape=(batch_size, complex_num_atoms, 3))
complex_nbrs = Feature(shape=(batch_size, complex_num_atoms,
max_num_neighbors))
complex_nbrs_z = Feature(shape=(batch_size, complex_num_atoms,
max_num_neighbors))
complex_z = Feature(shape=(batch_size, complex_num_atoms))
frag1_conv = AtomicConvolution(
atom_types=at,
radial_params=rp,
boxsize=None,
in_layers=[frag1_X, frag1_nbrs, frag1_nbrs_z])
frag2_conv = AtomicConvolution(
atom_types=at,
radial_params=rp,
boxsize=None,
in_layers=[frag2_X, frag2_nbrs, frag2_nbrs_z])
complex_conv = AtomicConvolution(
atom_types=at,
radial_params=rp,
boxsize=None,
in_layers=[complex_X, complex_nbrs, complex_nbrs_z])
score = AtomicConvScore(at,
layer_sizes,
in_layers=[
frag1_conv, frag2_conv, complex_conv, frag1_z,
frag2_z, complex_z
])
label = Label(shape=(None, 1))
loss = ReduceMean(in_layers=L2Loss(in_layers=[score, label]))
def feed_dict_generator(dataset, batch_size, epochs=1, pad_batches=True):
def replace_atom_types(z):
def place_holder(i):
if i in at:
return i
return -1
return np.array([place_holder(x) for x in z])
for epoch in range(epochs):
for ind, (F_b, y_b, w_b, ids_b) in enumerate(
dataset.iterbatches(batch_size,
deterministic=True,
pad_batches=pad_batches)):
N = complex_num_atoms
N_1 = frag1_num_atoms
N_2 = frag2_num_atoms
M = max_num_neighbors
orig_dict = {}
batch_size = F_b.shape[0]
num_features = F_b[0][0].shape[1]
frag1_X_b = np.zeros((batch_size, N_1, num_features))
for i in range(batch_size):
frag1_X_b[i] = F_b[i][0]
orig_dict[frag1_X] = frag1_X_b
frag2_X_b = np.zeros((batch_size, N_2, num_features))
for i in range(batch_size):
frag2_X_b[i] = F_b[i][3]
orig_dict[frag2_X] = frag2_X_b
complex_X_b = np.zeros((batch_size, N, num_features))
for i in range(batch_size):
complex_X_b[i] = F_b[i][6]
orig_dict[complex_X] = complex_X_b
frag1_Nbrs = np.zeros((batch_size, N_1, M))
frag1_Z_b = np.zeros((batch_size, N_1))
for i in range(batch_size):
z = replace_atom_types(F_b[i][2])
frag1_Z_b[i] = z
frag1_Nbrs_Z = np.zeros((batch_size, N_1, M))
for atom in range(N_1):
for i in range(batch_size):
atom_nbrs = F_b[i][1].get(atom, "")
frag1_Nbrs[i,
atom, :len(atom_nbrs)] = np.array(atom_nbrs)
for j, atom_j in enumerate(atom_nbrs):
frag1_Nbrs_Z[i, atom, j] = frag1_Z_b[i, atom_j]
orig_dict[frag1_nbrs] = frag1_Nbrs
orig_dict[frag1_nbrs_z] = frag1_Nbrs_Z
orig_dict[frag1_z] = frag1_Z_b
frag2_Nbrs = np.zeros((batch_size, N_2, M))
frag2_Z_b = np.zeros((batch_size, N_2))
for i in range(batch_size):
z = replace_atom_types(F_b[i][5])
frag2_Z_b[i] = z
frag2_Nbrs_Z = np.zeros((batch_size, N_2, M))
for atom in range(N_2):
for i in range(batch_size):
atom_nbrs = F_b[i][4].get(atom, "")
frag2_Nbrs[i,
atom, :len(atom_nbrs)] = np.array(atom_nbrs)
for j, atom_j in enumerate(atom_nbrs):
frag2_Nbrs_Z[i, atom, j] = frag2_Z_b[i, atom_j]
orig_dict[frag2_nbrs] = frag2_Nbrs
orig_dict[frag2_nbrs_z] = frag2_Nbrs_Z
orig_dict[frag2_z] = frag2_Z_b
complex_Nbrs = np.zeros((batch_size, N, M))
complex_Z_b = np.zeros((batch_size, N))
for i in range(batch_size):
z = replace_atom_types(F_b[i][8])
complex_Z_b[i] = z
complex_Nbrs_Z = np.zeros((batch_size, N, M))
for atom in range(N):
for i in range(batch_size):
atom_nbrs = F_b[i][7].get(atom, "")
complex_Nbrs[i, atom, :len(atom_nbrs)] = np.array(
atom_nbrs)
for j, atom_j in enumerate(atom_nbrs):
complex_Nbrs_Z[i, atom, j] = complex_Z_b[i, atom_j]
orig_dict[complex_nbrs] = complex_Nbrs
orig_dict[complex_nbrs_z] = complex_Nbrs_Z
orig_dict[complex_z] = complex_Z_b
orig_dict[label] = np.reshape(y_b, newshape=(batch_size, 1))
yield orig_dict
tg = TensorGraph(batch_size=batch_size,
mode=str("regression"),
model_dir=str("/tmp/atom_conv"),
learning_rate=learning_rate)
tg.add_output(score)
tg.set_loss(loss)
return tg, feed_dict_generator, label
def __init__(self,
n_tasks,
n_features,
layer_sizes=[1000],
weight_init_stddevs=[0.02],
bias_init_consts=[1.0],
weight_decay_penalty=0.0,
weight_decay_penalty_type="l2",
dropouts=[0.5],
n_classes=2,
**kwargs):
"""Create a TensorGraphMultiTaskClassifier.
In addition to the following arguments, this class also accepts all the keywork arguments
from TensorGraph.
Parameters
----------
n_tasks: int
number of tasks
n_features: int
number of features
layer_sizes: list
the size of each dense layer in the network. The length of this list determines the number of layers.
weight_init_stddevs: list
the standard deviation of the distribution to use for weight initialization of each layer. The length
of this list should equal len(layer_sizes).
bias_init_consts: list
the value to initialize the biases in each layer to. The length of this list should equal len(layer_sizes).
weight_decay_penalty: float
the magnitude of the weight decay penalty to use
weight_decay_penalty_type: str
the type of penalty to use for weight decay, either 'l1' or 'l2'
dropouts: list
the dropout probablity to use for each layer. The length of this list should equal len(layer_sizes).
n_classes: int
the number of classes
"""
super(TensorGraphMultiTaskClassifier,
self).__init__(mode='classification', **kwargs)
self.n_tasks = n_tasks
self.n_features = n_features
self.n_classes = n_classes
# Add the input features.
mol_features = Feature(shape=(None, n_features))
prev_layer = mol_features
# Add the dense layers
for size, weight_stddev, bias_const, dropout in zip(
layer_sizes, weight_init_stddevs, bias_init_consts, dropouts):
layer = Dense(in_layers=[prev_layer],
out_channels=size,
activation_fn=tf.nn.relu,
weights_initializer=TFWrapper(
tf.truncated_normal_initializer,
stddev=weight_stddev),
biases_initializer=TFWrapper(tf.constant_initializer,
value=bias_const))
if dropout > 0.0:
layer = Dropout(dropout, in_layers=[layer])
prev_layer = layer
# Compute the loss function for each label.
output = Reshape(shape=(-1, n_tasks, n_classes),
in_layers=[
Dense(in_layers=[prev_layer],
out_channels=n_tasks * n_classes)
])
self.add_output(output)
labels = Label(shape=(None, n_tasks, n_classes))
weights = Weights(shape=(None, n_tasks))
loss = SoftMaxCrossEntropy(in_layers=[labels, output])
weighted_loss = WeightedError(in_layers=[loss, weights])
if weight_decay_penalty != 0.0:
weighted_loss = WeightDecay(weight_decay_penalty,
weight_decay_penalty_type,
in_layers=[weighted_loss])
self.set_loss(weighted_loss)
def __init__(self,
n_tasks,
n_features,
layer_sizes=[1000],
weight_init_stddevs=0.02,
bias_init_consts=1.0,
weight_decay_penalty=0.0,
weight_decay_penalty_type="l2",
dropouts=0.5,
activation_fns=tf.nn.relu,
uncertainty=False,
**kwargs):
"""Create a MultitaskRegressor.
In addition to the following arguments, this class also accepts all the keywork arguments
from TensorGraph.
Parameters
----------
n_tasks: int
number of tasks
n_features: int
number of features
layer_sizes: list
the size of each dense layer in the network. The length of this list determines the number of layers.
weight_init_stddevs: list or float
the standard deviation of the distribution to use for weight initialization of each layer. The length
of this list should equal len(layer_sizes)+1. The final element corresponds to the output layer.
Alternatively this may be a single value instead of a list, in which case the same value is used for every layer.
bias_init_consts: list or float
the value to initialize the biases in each layer to. The length of this list should equal len(layer_sizes)+1.
The final element corresponds to the output layer. Alternatively this may be a single value instead of a list,
in which case the same value is used for every layer.
weight_decay_penalty: float
the magnitude of the weight decay penalty to use
weight_decay_penalty_type: str
the type of penalty to use for weight decay, either 'l1' or 'l2'
dropouts: list or float
the dropout probablity to use for each layer. The length of this list should equal len(layer_sizes).
Alternatively this may be a single value instead of a list, in which case the same value is used for every layer.
activation_fns: list or object
the Tensorflow activation function to apply to each layer. The length of this list should equal
len(layer_sizes). Alternatively this may be a single value instead of a list, in which case the
same value is used for every layer.
uncertainty: bool
if True, include extra outputs and loss terms to enable the uncertainty
in outputs to be predicted
"""
super(MultitaskRegressor, self).__init__(**kwargs)
self.n_tasks = n_tasks
self.n_features = n_features
n_layers = len(layer_sizes)
if not isinstance(weight_init_stddevs, collections.Sequence):
weight_init_stddevs = [weight_init_stddevs] * (n_layers + 1)
if not isinstance(bias_init_consts, collections.Sequence):
bias_init_consts = [bias_init_consts] * (n_layers + 1)
if not isinstance(dropouts, collections.Sequence):
dropouts = [dropouts] * n_layers
if not isinstance(activation_fns, collections.Sequence):
activation_fns = [activation_fns] * n_layers
if uncertainty:
if any(d == 0.0 for d in dropouts):
raise ValueError(
'Dropout must be included in every layer to predict uncertainty'
)
# Add the input features.
mol_features = Feature(shape=(None, n_features))
prev_layer = mol_features
# Add the dense layers
for size, weight_stddev, bias_const, dropout, activation_fn in zip(
layer_sizes, weight_init_stddevs, bias_init_consts, dropouts,
activation_fns):
layer = Dense(in_layers=[prev_layer],
out_channels=size,
activation_fn=activation_fn,
weights_initializer=TFWrapper(
tf.truncated_normal_initializer,
stddev=weight_stddev),
biases_initializer=TFWrapper(tf.constant_initializer,
value=bias_const))
if dropout > 0.0:
layer = Dropout(dropout, in_layers=[layer])
prev_layer = layer
self.neural_fingerprint = prev_layer
# Compute the loss function for each label.
output = Reshape(shape=(-1, n_tasks, 1),
in_layers=[
Dense(in_layers=[prev_layer],
out_channels=n_tasks,
weights_initializer=TFWrapper(
tf.truncated_normal_initializer,
stddev=weight_init_stddevs[-1]),
biases_initializer=TFWrapper(
tf.constant_initializer,
value=bias_init_consts[-1]))
])
self.add_output(output)
labels = Label(shape=(None, n_tasks, 1))
weights = Weights(shape=(None, n_tasks, 1))
if uncertainty:
log_var = Reshape(
shape=(-1, n_tasks, 1),
in_layers=[
Dense(in_layers=[prev_layer],
out_channels=n_tasks,
weights_initializer=TFWrapper(
tf.truncated_normal_initializer,
stddev=weight_init_stddevs[-1]),
biases_initializer=TFWrapper(tf.constant_initializer,
value=0.0))
])
var = Exp(log_var)
self.add_variance(var)
diff = labels - output
weighted_loss = weights * (diff * diff / var + log_var)
weighted_loss = ReduceSum(ReduceMean(weighted_loss, axis=[1, 2]))
else:
weighted_loss = ReduceSum(
L2Loss(in_layers=[labels, output, weights]))
if weight_decay_penalty != 0.0:
weighted_loss = WeightDecay(weight_decay_penalty,
weight_decay_penalty_type,
in_layers=[weighted_loss])
self.set_loss(weighted_loss)
def build_graph(self):
"""Building graph structures:
Features => WeaveLayer => WeaveLayer => Dense => WeaveGather => Classification or Regression
"""
self.atom_features = Feature(shape=(None, self.n_atom_feat))
self.pair_features = Feature(shape=(None, self.n_pair_feat))
self.pair_split = Feature(shape=(None, ), dtype=tf.int32)
self.atom_split = Feature(shape=(None, ), dtype=tf.int32)
self.atom_to_pair = Feature(shape=(None, 2), dtype=tf.int32)
weave_layer1A, weave_layer1P = WeaveLayerFactory(
n_atom_input_feat=self.n_atom_feat,
n_pair_input_feat=self.n_pair_feat,
n_atom_output_feat=self.n_hidden,
n_pair_output_feat=self.n_hidden,
in_layers=[
self.atom_features, self.pair_features, self.pair_split,
self.atom_to_pair
])
weave_layer2A, weave_layer2P = WeaveLayerFactory(
n_atom_input_feat=self.n_hidden,
n_pair_input_feat=self.n_hidden,
n_atom_output_feat=self.n_hidden,
n_pair_output_feat=self.n_hidden,
update_pair=False,
in_layers=[
weave_layer1A, weave_layer1P, self.pair_split,
self.atom_to_pair
])
dense1 = Dense(out_channels=self.n_graph_feat,
activation_fn=tf.nn.tanh,
in_layers=weave_layer2A)
batch_norm1 = BatchNorm(epsilon=1e-5, in_layers=[dense1])
weave_gather = WeaveGather(self.batch_size,
n_input=self.n_graph_feat,
gaussian_expand=True,
in_layers=[batch_norm1, self.atom_split])
n_tasks = self.n_tasks
weights = Weights(shape=(None, n_tasks))
if self.mode == 'classification':
n_classes = self.n_classes
labels = Label(shape=(None, n_tasks, n_classes))
logits = Reshape(shape=(None, n_tasks, n_classes),
in_layers=[
Dense(in_layers=weave_gather,
out_channels=n_tasks * n_classes)
])
output = SoftMax(logits)
self.add_output(output)
loss = SoftMaxCrossEntropy(in_layers=[labels, logits])
weighted_loss = WeightedError(in_layers=[loss, weights])
self.set_loss(weighted_loss)
else:
labels = Label(shape=(None, n_tasks))
output = Reshape(shape=(None, n_tasks),
in_layers=[
Dense(in_layers=weave_gather,
out_channels=n_tasks)
])
self.add_output(output)
weighted_loss = ReduceSum(
L2Loss(in_layers=[labels, output, weights]))
self.set_loss(weighted_loss)
def build_graph(self):
# Build placeholders
self.atom_features = Feature(shape=(None, self.n_atom_feat))
self.pair_features = Feature(shape=(None, self.n_pair_feat))
self.atom_split = Feature(shape=(None, ), dtype=tf.int32)
self.atom_to_pair = Feature(shape=(None, 2), dtype=tf.int32)
message_passing = MessagePassing(self.T,
message_fn='enn',
update_fn='gru',
n_hidden=self.n_hidden,
in_layers=[
self.atom_features,
self.pair_features,
self.atom_to_pair
])
atom_embeddings = Dense(self.n_hidden, in_layers=[message_passing])
mol_embeddings = SetGather(
self.M,
self.batch_size,
n_hidden=self.n_hidden,
in_layers=[atom_embeddings, self.atom_split])
dense1 = Dense(out_channels=2 * self.n_hidden,
activation_fn=tf.nn.relu,
in_layers=[mol_embeddings])
n_tasks = self.n_tasks
weights = Weights(shape=(None, n_tasks))
if self.mode == 'classification':
n_classes = self.n_classes
labels = Label(shape=(None, n_tasks, n_classes))
logits = Reshape(shape=(None, n_tasks, n_classes),
in_layers=[
Dense(in_layers=dense1,
out_channels=n_tasks * n_classes)
])
logits = TrimGraphOutput([logits, weights])
output = SoftMax(logits)
self.add_output(output)
loss = SoftMaxCrossEntropy(in_layers=[labels, logits])
weighted_loss = WeightedError(in_layers=[loss, weights])
self.set_loss(weighted_loss)
else:
labels = Label(shape=(None, n_tasks))
output = Reshape(
shape=(None, n_tasks),
in_layers=[Dense(in_layers=dense1, out_channels=n_tasks)])
output = TrimGraphOutput([output, weights])
self.add_output(output)
if self.uncertainty:
log_var = Reshape(
shape=(None, n_tasks),
in_layers=[Dense(in_layers=dense1, out_channels=n_tasks)])
log_var = TrimGraphOutput([log_var, weights])
var = Exp(log_var)
self.add_variance(var)
diff = labels - output
weighted_loss = weights * (diff * diff / var + log_var)
weighted_loss = ReduceSum(ReduceMean(weighted_loss, axis=[1]))
else:
weighted_loss = ReduceSum(
L2Loss(in_layers=[labels, output, weights]))
self.set_loss(weighted_loss)
def build_graph(self):
"""
Building graph structures:
"""
self.atom_features = Feature(shape=(None, self.number_atom_features))
self.degree_slice = Feature(shape=(None, 2), dtype=tf.int32)
self.membership = Feature(shape=(None, ), dtype=tf.int32)
self.deg_adjs = []
for i in range(0, 10 + 1):
deg_adj = Feature(shape=(None, i + 1), dtype=tf.int32)
self.deg_adjs.append(deg_adj)
in_layer = self.atom_features
for layer_size, dropout in zip(self.graph_conv_layers, self.dropout):
gc1_in = [in_layer, self.degree_slice, self.membership
] + self.deg_adjs
gc1 = GraphConv(layer_size,
activation_fn=tf.nn.relu,
in_layers=gc1_in)
batch_norm1 = BatchNorm(in_layers=[gc1])
if dropout > 0.0:
batch_norm1 = Dropout(dropout, in_layers=batch_norm1)
gp_in = [batch_norm1, self.degree_slice, self.membership
] + self.deg_adjs
in_layer = GraphPool(in_layers=gp_in)
dense = Dense(out_channels=self.dense_layer_size,
activation_fn=tf.nn.relu,
in_layers=[in_layer])
batch_norm3 = BatchNorm(in_layers=[dense])
if self.dropout[-1] > 0.0:
batch_norm3 = Dropout(self.dropout[-1], in_layers=batch_norm3)
readout = GraphGather(
batch_size=self.batch_size,
activation_fn=tf.nn.tanh,
in_layers=[batch_norm3, self.degree_slice, self.membership] +
self.deg_adjs)
n_tasks = self.n_tasks
weights = Weights(shape=(None, n_tasks))
if self.mode == 'classification':
n_classes = self.n_classes
labels = Label(shape=(None, n_tasks, n_classes))
logits = Reshape(shape=(None, n_tasks, n_classes),
in_layers=[
Dense(in_layers=readout,
out_channels=n_tasks * n_classes)
])
logits = TrimGraphOutput([logits, weights])
output = SoftMax(logits)
self.add_output(output)
loss = SoftMaxCrossEntropy(in_layers=[labels, logits])
weighted_loss = WeightedError(in_layers=[loss, weights])
self.set_loss(weighted_loss)
else:
labels = Label(shape=(None, n_tasks))
output = Reshape(
shape=(None, n_tasks),
in_layers=[Dense(in_layers=readout, out_channels=n_tasks)])
output = TrimGraphOutput([output, weights])
self.add_output(output)
if self.uncertainty:
log_var = Reshape(
shape=(None, n_tasks),
in_layers=[Dense(in_layers=readout, out_channels=n_tasks)])
log_var = TrimGraphOutput([log_var, weights])
var = Exp(log_var)
self.add_variance(var)
diff = labels - output
weighted_loss = weights * (diff * diff / var + log_var)
weighted_loss = ReduceSum(ReduceMean(weighted_loss, axis=[1]))
else:
weighted_loss = ReduceSum(
L2Loss(in_layers=[labels, output, weights]))
self.set_loss(weighted_loss)
def sluice_model(batch_size, tasks):
model = TensorGraph(model_dir=model_dir,
batch_size=batch_size,
use_queue=False,
tensorboard=True)
atom_features = Feature(shape=(None, 75))
degree_slice = Feature(shape=(None, 2), dtype=tf.int32)
membership = Feature(shape=(None, ), dtype=tf.int32)
sluice_loss = []
deg_adjs = []
for i in range(0, 10 + 1):
deg_adj = Feature(shape=(None, i + 1), dtype=tf.int32)
deg_adjs.append(deg_adj)
gc1 = GraphConv(64,
activation_fn=tf.nn.relu,
in_layers=[atom_features, degree_slice, membership] +
deg_adjs)
as1 = AlphaShare(in_layers=[gc1, gc1])
sluice_loss.append(gc1)
batch_norm1a = BatchNorm(in_layers=[as1[0]])
batch_norm1b = BatchNorm(in_layers=[as1[1]])
gp1a = GraphPool(in_layers=[batch_norm1a, degree_slice, membership] +
deg_adjs)
gp1b = GraphPool(in_layers=[batch_norm1b, degree_slice, membership] +
deg_adjs)
gc2a = GraphConv(64,
activation_fn=tf.nn.relu,
in_layers=[gp1a, degree_slice, membership] + deg_adjs)
gc2b = GraphConv(64,
activation_fn=tf.nn.relu,
in_layers=[gp1b, degree_slice, membership] + deg_adjs)
as2 = AlphaShare(in_layers=[gc2a, gc2b])
sluice_loss.append(gc2a)
sluice_loss.append(gc2b)
batch_norm2a = BatchNorm(in_layers=[as2[0]])
batch_norm2b = BatchNorm(in_layers=[as2[1]])
gp2a = GraphPool(in_layers=[batch_norm2a, degree_slice, membership] +
deg_adjs)
gp2b = GraphPool(in_layers=[batch_norm2b, degree_slice, membership] +
deg_adjs)
densea = Dense(out_channels=128, activation_fn=None, in_layers=[gp2a])
denseb = Dense(out_channels=128, activation_fn=None, in_layers=[gp2b])
batch_norm3a = BatchNorm(in_layers=[densea])
batch_norm3b = BatchNorm(in_layers=[denseb])
as3 = AlphaShare(in_layers=[batch_norm3a, batch_norm3b])
sluice_loss.append(batch_norm3a)
sluice_loss.append(batch_norm3b)
gg1a = GraphGather(batch_size=batch_size,
activation_fn=tf.nn.tanh,
in_layers=[as3[0], degree_slice, membership] + deg_adjs)
gg1b = GraphGather(batch_size=batch_size,
activation_fn=tf.nn.tanh,
in_layers=[as3[1], degree_slice, membership] + deg_adjs)
costs = []
labels = []
count = 0
for task in tasks:
if count < len(tasks) / 2:
classification = Dense(out_channels=2,
activation_fn=None,
in_layers=[gg1a])
print("first half:")
print(task)
else:
classification = Dense(out_channels=2,
activation_fn=None,
in_layers=[gg1b])
print('second half')
print(task)
count += 1
softmax = SoftMax(in_layers=[classification])
model.add_output(softmax)
label = Label(shape=(None, 2))
labels.append(label)
cost = SoftMaxCrossEntropy(in_layers=[label, classification])
costs.append(cost)
entropy = Concat(in_layers=costs)
task_weights = Weights(shape=(None, len(tasks)))
task_loss = WeightedError(in_layers=[entropy, task_weights])
s_cost = SluiceLoss(in_layers=sluice_loss)
total_loss = Add(in_layers=[task_loss, s_cost])
model.set_loss(total_loss)
def feed_dict_generator(dataset, batch_size, epochs=1):
for epoch in range(epochs):
for ind, (X_b, y_b, w_b, ids_b) in enumerate(
dataset.iterbatches(batch_size, pad_batches=True)):
d = {}
for index, label in enumerate(labels):
d[label] = to_one_hot(y_b[:, index])
d[task_weights] = w_b
multiConvMol = ConvMol.agglomerate_mols(X_b)
d[atom_features] = multiConvMol.get_atom_features()
d[degree_slice] = multiConvMol.deg_slice
d[membership] = multiConvMol.membership
for i in range(1, len(multiConvMol.get_deg_adjacency_lists())):
d[deg_adjs[i -
1]] = multiConvMol.get_deg_adjacency_lists()[i]
yield d
return model, feed_dict_generator, labels, task_weights
def build_graph(self):
"""Building graph structures:
Features => DAGLayer => DAGGather => Classification or Regression
"""
self.atom_features = Feature(shape=(None, self.n_atom_feat))
self.parents = Feature(shape=(None, self.max_atoms, self.max_atoms),
dtype=tf.int32)
self.calculation_orders = Feature(shape=(None, self.max_atoms),
dtype=tf.int32)
self.calculation_masks = Feature(shape=(None, self.max_atoms),
dtype=tf.bool)
self.membership = Feature(shape=(None, ), dtype=tf.int32)
self.n_atoms = Feature(shape=(), dtype=tf.int32)
dag_layer1 = DAGLayer(n_graph_feat=self.n_graph_feat,
n_atom_feat=self.n_atom_feat,
max_atoms=self.max_atoms,
layer_sizes=self.layer_sizes,
dropout=self.dropout,
batch_size=self.batch_size,
in_layers=[
self.atom_features, self.parents,
self.calculation_orders,
self.calculation_masks, self.n_atoms
])
dag_gather = DAGGather(n_graph_feat=self.n_graph_feat,
n_outputs=self.n_outputs,
max_atoms=self.max_atoms,
layer_sizes=self.layer_sizes_gather,
dropout=self.dropout,
in_layers=[dag_layer1, self.membership])
n_tasks = self.n_tasks
weights = Weights(shape=(None, n_tasks))
if self.mode == 'classification':
n_classes = self.n_classes
labels = Label(shape=(None, n_tasks, n_classes))
logits = Reshape(shape=(None, n_tasks, n_classes),
in_layers=[
Dense(in_layers=dag_gather,
out_channels=n_tasks * n_classes)
])
output = SoftMax(logits)
self.add_output(output)
loss = SoftMaxCrossEntropy(in_layers=[labels, logits])
weighted_loss = WeightedError(in_layers=[loss, weights])
self.set_loss(weighted_loss)
else:
labels = Label(shape=(None, n_tasks))
output = Reshape(
shape=(None, n_tasks),
in_layers=[Dense(in_layers=dag_gather, out_channels=n_tasks)])
self.add_output(output)
if self.uncertainty:
log_var = Reshape(shape=(None, n_tasks),
in_layers=[
Dense(in_layers=dag_gather,
out_channels=n_tasks)
])
var = Exp(log_var)
self.add_variance(var)
diff = labels - output
weighted_loss = weights * (diff * diff / var + log_var)
weighted_loss = ReduceSum(ReduceMean(weighted_loss, axis=[1]))
else:
weighted_loss = ReduceSum(
L2Loss(in_layers=[labels, output, weights]))
self.set_loss(weighted_loss)
