def test_sigmoid_cross_entropy(self):
"""Test invoking SigmoidCrossEntropy in eager mode."""
with context.eager_mode():
batch_size = 10
n_features = 5
logits = np.random.rand(batch_size, n_features).astype(np.float32)
labels = np.random.randint(0, 2, (batch_size, n_features)).astype(
np.float32)
result = layers.SigmoidCrossEntropy()(labels, logits)
expected = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels,
logits=logits)
assert np.allclose(result, expected)
deconv2 = layers.Conv2DTranspose(16,
kernel_size=5,
stride=2,
in_layers=concat2)
concat3 = layers.Concat(in_layers=[conv1, deconv2], axis=3)
deconv3 = layers.Conv2DTranspose(1, kernel_size=5, stride=2, in_layers=concat3)
# Compute the final output.
concat4 = layers.Concat(in_layers=[features, deconv3], axis=3)
logits = layers.Conv2D(1,
kernel_size=5,
stride=1,
activation_fn=None,
in_layers=concat4)
output = layers.Sigmoid(logits)
model.add_output(output)
loss = layers.ReduceSum(layers.SigmoidCrossEntropy(in_layers=(labels, logits)))
model.set_loss(loss)
if not os.path.exists('./models'):
os.mkdir('models')
if not os.path.exists('./models/segmentation'):
os.mkdir('models/segmentation')
if not RETRAIN:
model.restore()
# Train it and evaluate performance on the test set.
if RETRAIN:
print("About to fit model for 50 epochs")
model.fit(train_dataset, nb_epoch=50, checkpoint_interval=100)
scores = []
# Build the model.
model = dc.models.TensorGraph(batch_size=1000, model_dir='chromatin')
features = layers.Feature(shape=(None, 101, 4))
accessibility = layers.Feature(shape=(None, 1))
labels = layers.Label(shape=(None, 1))
weights = layers.Weights(shape=(None, 1))
prev = features
for i in range(3):
prev = layers.Conv1D(filters=15, kernel_size=10, activation=tf.nn.relu, padding='same', in_layers=prev)
prev = layers.Dropout(dropout_prob=0.5, in_layers=prev)
prev = layers.Concat([layers.Flatten(prev), accessibility])
logits = layers.Dense(out_channels=1, in_layers=prev)
output = layers.Sigmoid(logits)
model.add_output(output)
loss = layers.SigmoidCrossEntropy(in_layers=[labels, logits])
weighted_loss = layers.WeightedError(in_layers=[loss, weights])
model.set_loss(weighted_loss)
# Load the data.
train = dc.data.DiskDataset('train_dataset')
valid = dc.data.DiskDataset('valid_dataset')
span_accessibility = {}
for line in open('accessibility.txt'):
fields = line.split()
span_accessibility[fields[0]] = float(fields[1])
# Define a generator function to produce batches.
def generate_batches(dataset, epochs):
