def test_construct(self):
m = counters.NumExamplesCounter()
self.assertEqual(m.name, 'num_examples')
self.assertTrue(m.stateful)
self.assertEqual(m.dtype, tf.int64)
self.assertLen(m.variables, 1)
self.assertEqual(m.total, 0)
m = counters.NumExamplesCounter('num_examples2')
self.assertEqual(m.name, 'num_examples2')
def create_simple_keras_model(learning_rate=0.1):
"""Returns an instance of `tf.Keras.Model` with just one dense layer.
Args:
learning_rate: The learning rate to use with the SGD optimizer.
Returns:
An instance of `tf.Keras.Model`.
"""
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(784, )),
tf.keras.layers.Dense(10, tf.nn.softmax, kernel_initializer='zeros'),
])
model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),
optimizer=tf.keras.optimizers.SGD(learning_rate),
metrics=[
tf.keras.metrics.SparseCategoricalAccuracy(),
counters.NumExamplesCounter(),
])
return model
def test_reset_to_zero(self):
m = counters.NumExamplesCounter()
self.assertGreater(m(tf.zeros([10, 1]), tf.zeros([10])), 0)
self.assertGreater(m.total, 0)
m.reset_state()
self.assertEqual(m.total, 0)
def test_update_with_sample_weight(self, batch1, batch2):
m = counters.NumExamplesCounter()
self.assertEqual(m(batch1, batch1), 10)
self.assertEqual(m.total, 10)
self.assertEqual(m.update_state(batch2, batch2), 15)
self.assertEqual(m.total, 15)
def test_update_computes_shape_of_first_input_arg(self):
m = counters.NumExamplesCounter()
self.assertEqual(m(tf.zeros([10, 1]), tf.zeros([15])), 10)
self.assertEqual(m.total, 10)
self.assertEqual(m.update_state(tf.zeros([5, 1]), tf.zeros([700])), 15)
self.assertEqual(m.total, 15)
def metrics_fn():
return [counters.NumExamplesCounter(), NumOverCounter(5.0)]
def metrics_fn():
return [
counters.NumExamplesCounter(),
counters.NumBatchesCounter(),
tf.keras.metrics.SparseCategoricalAccuracy()
]
