def build(self):
# Create target inputs
self.label_placeholder = Input(tensor=tf.placeholder(
dtype='bool', shape=(None,
self.n_tasks), name="label_placeholder"))
self.weight_placeholder = Input(
tensor=tf.placeholder(dtype='float32',
shape=(None, self.n_tasks),
name="weight_placholder"))
feat = self.model.return_outputs()
output = model_ops.multitask_logits(feat, self.n_tasks)
return output
def build(self, graph, name_scopes, training):
"""Constructs the graph architecture as specified in its config.
This method creates the following Placeholders:
mol_features: Molecule descriptor (e.g. fingerprint) tensor with shape
batch_size x n_features.
"""
placeholder_scope = TensorflowGraph.get_placeholder_scope(
graph, name_scopes)
n_features = self.n_features
with graph.as_default():
with placeholder_scope:
self.mol_features = tf.placeholder(tf.float32,
shape=[None, n_features],
name='mol_features')
layer_sizes = self.layer_sizes
weight_init_stddevs = self.weight_init_stddevs
bias_init_consts = self.bias_init_consts
dropouts = self.dropouts
lengths_set = {
len(layer_sizes),
len(weight_init_stddevs),
len(bias_init_consts),
len(dropouts),
}
assert len(
lengths_set) == 1, 'All layer params must have same length.'
n_layers = lengths_set.pop()
assert n_layers > 0, 'Must have some layers defined.'
prev_layer = self.mol_features
prev_layer_size = n_features
for i in range(n_layers):
layer = tf.nn.relu(
model_ops.fully_connected_layer(
tensor=prev_layer,
size=layer_sizes[i],
weight_init=tf.truncated_normal(
shape=[prev_layer_size, layer_sizes[i]],
stddev=weight_init_stddevs[i]),
bias_init=tf.constant(value=bias_init_consts[i],
shape=[layer_sizes[i]])))
layer = model_ops.dropout(layer, dropouts[i], training)
prev_layer = layer
prev_layer_size = layer_sizes[i]
output = model_ops.multitask_logits(layer, self.n_tasks)
return output
def build(self, graph, name_scopes, training):
"""Constructs the graph architecture as specified in its config.
This method creates the following Placeholders:
mol_features: Molecule descriptor (e.g. fingerprint) tensor with shape
batch_size x n_features.
"""
placeholder_scope = TensorflowGraph.get_placeholder_scope(
graph, name_scopes)
n_features = self.n_features
with graph.as_default():
with placeholder_scope:
self.mol_features = tf.placeholder(
tf.float32,
shape=[None, n_features],
name='mol_features')
layer_sizes = self.layer_sizes
weight_init_stddevs = self.weight_init_stddevs
bias_init_consts = self.bias_init_consts
dropouts = self.dropouts
lengths_set = {
len(layer_sizes),
len(weight_init_stddevs),
len(bias_init_consts),
len(dropouts),
}
assert len(lengths_set) == 1, 'All layer params must have same length.'
n_layers = lengths_set.pop()
assert n_layers > 0, 'Must have some layers defined.'
prev_layer = self.mol_features
prev_layer_size = n_features
for i in range(n_layers):
layer = tf.nn.relu(model_ops.fully_connected_layer(
tensor=prev_layer,
size=layer_sizes[i],
weight_init=tf.truncated_normal(
shape=[prev_layer_size, layer_sizes[i]],
stddev=weight_init_stddevs[i]),
bias_init=tf.constant(value=bias_init_consts[i],
shape=[layer_sizes[i]])))
layer = model_ops.dropout(layer, dropouts[i], training)
prev_layer = layer
prev_layer_size = layer_sizes[i]
output = model_ops.multitask_logits(
layer, self.n_tasks)
return output
def build(self):
# Create target inputs
self.label_placeholder = tf.placeholder(
dtype='bool', shape=(None, self.n_tasks), name="label_placeholder")
self.weight_placeholder = tf.placeholder(
dtype='float32', shape=(None, self.n_tasks), name="weight_placholder")
feat = self.model.return_outputs()
################################################################ DEBUG
#print("multitask classifier")
#print("feat")
#print(feat)
################################################################ DEBUG
output = model_ops.multitask_logits(feat, self.n_tasks)
return output
def build(self):
# Create target inputs
self.label_placeholder = tf.placeholder(
dtype='bool', shape=(None, self.n_tasks), name="label_placeholder")
self.weight_placeholder = tf.placeholder(
dtype='float32', shape=(None, self.n_tasks), name="weight_placholder")
self.training = self.model.get_training_state()
feat = self.model.return_outputs()
################################################################ DEBUG
#print("multitask classifier")
#print("feat")
#print(feat)
################################################################ DEBUG
output = model_ops.multitask_logits(feat, self.n_tasks)
return output
def build(self, graph, name_scopes, training):
"""Constructs the graph architecture as specified in its config.
This method creates the following Placeholders:
mol_features: Molecule descriptor (e.g. fingerprint) tensor with shape
batch_size x n_features.
"""
warnings.warn(
"TensorflowMultiTaskClassifier is deprecated. "
"Will be removed in DeepChem 1.4.", DeprecationWarning)
placeholder_scope = TensorflowGraph.get_placeholder_scope(
graph, name_scopes)
n_features = self.n_features
with graph.as_default():
with placeholder_scope:
mol_features = tf.placeholder(tf.float32,
shape=[None, n_features],
name='mol_features')
layer_sizes = self.layer_sizes
weight_init_stddevs = self.weight_init_stddevs
bias_init_consts = self.bias_init_consts
dropouts = self.dropouts
lengths_set = {
len(layer_sizes),
len(weight_init_stddevs),
len(bias_init_consts),
len(dropouts),
}
assert len(
lengths_set) == 1, 'All layer params must have same length.'
n_layers = lengths_set.pop()
assert n_layers > 0, 'Must have some layers defined.'
label_placeholders = self.add_label_placeholders(
graph, name_scopes)
weight_placeholders = self.add_example_weight_placeholders(
graph, name_scopes)
if training:
graph.queue = tf.FIFOQueue(
capacity=5,
dtypes=[tf.float32] *
(len(label_placeholders) + len(weight_placeholders) + 1))
graph.enqueue = graph.queue.enqueue([mol_features] +
label_placeholders +
weight_placeholders)
queue_outputs = graph.queue.dequeue()
labels = queue_outputs[1:len(label_placeholders) + 1]
weights = queue_outputs[len(label_placeholders) + 1:]
prev_layer = queue_outputs[0]
else:
labels = label_placeholders
weights = weight_placeholders
prev_layer = mol_features
prev_layer_size = n_features
for i in range(n_layers):
layer = tf.nn.relu(
model_ops.fully_connected_layer(
tensor=prev_layer,
size=layer_sizes[i],
weight_init=tf.truncated_normal(
shape=[prev_layer_size, layer_sizes[i]],
stddev=weight_init_stddevs[i]),
bias_init=tf.constant(value=bias_init_consts[i],
shape=[layer_sizes[i]])))
layer = model_ops.dropout(layer, dropouts[i], training)
prev_layer = layer
prev_layer_size = layer_sizes[i]
output = model_ops.multitask_logits(layer, self.n_tasks)
return (output, labels, weights)