def test_conv_1d(self):
"""Test invoking Conv1D in eager mode."""
with context.eager_mode():
with tfe.IsolateTest():
width = 5
in_channels = 2
filters = 3
kernel_size = 2
batch_size = 10
input = np.random.rand(batch_size, width,
in_channels).astype(np.float32)
layer = layers.Conv1D(filters, kernel_size)
result = layer(input)
self.assertEqual(result.shape[0], batch_size)
self.assertEqual(result.shape[2], filters)
assert len(layer.variables) == 2
# Creating a second layer should produce different results, since it has
# different random weights.
layer2 = layers.Conv1D(filters, kernel_size)
result2 = layer2(input)
assert not np.allclose(result, result2)
# But evaluating the first layer again should produce the same result as before.
result3 = layer(input)
assert np.allclose(result, result3)
def _create_encoder(self, n_layers, dropout):
"""Create the encoder layers."""
prev_layer = self._features
for i in range(len(self._filter_sizes)):
filter_size = self._filter_sizes[i]
kernel_size = self._kernel_sizes[i]
if dropout > 0.0:
prev_layer = layers.Dropout(dropout, in_layers=prev_layer)
prev_layer = layers.Conv1D(
filters=filter_size,
kernel_size=kernel_size,
in_layers=prev_layer,
activation_fn=tf.nn.relu)
prev_layer = layers.Flatten(prev_layer)
prev_layer = layers.Dense(
self._decoder_dimension, in_layers=prev_layer, activation_fn=tf.nn.relu)
prev_layer = layers.BatchNorm(prev_layer)
if self._variational:
self._embedding_mean = layers.Dense(
self._embedding_dimension,
in_layers=prev_layer,
name='embedding_mean')
self._embedding_stddev = layers.Dense(
self._embedding_dimension, in_layers=prev_layer, name='embedding_std')
prev_layer = layers.CombineMeanStd(
[self._embedding_mean, self._embedding_stddev], training_only=True)
return prev_layer
import deepchem as dc
import deepchem.models.tensorgraph.layers as layers
import tensorflow as tf
import matplotlib.pyplot as plot
# Build the model.
model = dc.models.TensorGraph(model_dir='rnai')
features = layers.Feature(shape=(None, 21, 4))
labels = layers.Label(shape=(None, 1))
prev = features
for i in range(2):
prev = layers.Conv1D(filters=10,
kernel_size=10,
activation=tf.nn.relu,
padding='same',
in_layers=prev)
prev = layers.Dropout(dropout_prob=0.3, in_layers=prev)
output = layers.Dense(out_channels=1,
activation_fn=tf.sigmoid,
in_layers=layers.Flatten(prev))
model.add_output(output)
loss = layers.ReduceMean(layers.L2Loss(in_layers=[labels, output]))
model.set_loss(loss)
# Load the data.
train = dc.data.DiskDataset('train_siRNA')
valid = dc.data.DiskDataset('valid_siRNA')
