def _build_toy_problem(self):
"""Construct a toy linear regression problem.
Initial loss should be,
2.5 = 0.5 * (1^2 + 2^2)
Returns:
loss: 0-D Tensor representing loss to be minimized.
accuracy: 0-D Tensors representing model accuracy.
layer_collection: LayerCollection instance describing model architecture.
"""
x = np.asarray([[1.], [2.]]).astype(np.float32)
y = np.asarray([1., 2.]).astype(np.float32)
x, y = (tf.data.Dataset.from_tensor_slices(
(x, y)).repeat(100).batch(2).make_one_shot_iterator().get_next())
w = tf.get_variable("w",
shape=[1, 1],
initializer=tf.zeros_initializer())
y_hat = tf.matmul(x, w)
loss = tf.reduce_mean(0.5 * tf.square(y_hat - y))
accuracy = loss
layer_collection = lc.LayerCollection()
layer_collection.register_fully_connected(params=w,
inputs=x,
outputs=y_hat)
layer_collection.register_normal_predictive_distribution(y_hat)
return loss, accuracy, layer_collection
def testBuildModel(self):
with tf.Graph().as_default():
x = tf.placeholder(tf.float32, [None, 6, 6, 3])
y = tf.placeholder(tf.int64, [None])
layer_collection = lc.LayerCollection()
loss, accuracy = convnet.build_model(
x, y, num_labels=5, layer_collection=layer_collection)
# Ensure layers and logits were registered.
self.assertEqual(len(layer_collection.fisher_blocks), 3)
self.assertEqual(len(layer_collection.losses), 1)
# Ensure inference doesn't crash.
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
feed_dict = {
x: np.random.randn(10, 6, 6, 3).astype(np.float32),
y: np.random.randint(5, size=10).astype(np.int64),
}
sess.run([loss, accuracy], feed_dict=feed_dict)
