keras/keras_core/layers/layer_test.py

import numpy as np

from keras_core import backend
from keras_core import layers
from keras_core import models
from keras_core import operations as ops
from keras_core import testing


class LayerTest(testing.TestCase):
    def test_positional_arg_error(self):
        class SomeLayer(layers.Layer):
            def call(self, x, bool_arg):
                if bool_arg:
                    return x
                return x + 1

        x = backend.KerasTensor(shape=(2, 3), name="x")
        with self.assertRaisesRegex(
            ValueError, "Only input tensors may be passed as"
        ):
            SomeLayer()(x, True)

        # This works
        SomeLayer()(x, bool_arg=True)

    def test_rng_seed_tracking(self):
        class RNGLayer(layers.Layer):
            def __init__(self):
                super().__init__()
                self.seed_gen = backend.random.SeedGenerator(seed=1337)

            def call(self, x):
                return backend.random.dropout(x, rate=0.5, seed=self.seed_gen)

        layer = RNGLayer()
        self.assertEqual(layer.variables, [layer.seed_gen.state])
        self.assertAllClose(layer.variables[0], [1337, 0])
        layer(np.ones((3, 4)))
        self.assertAllClose(layer.variables[0], [1337, 1])

        # Test tracking in list attributes.
        class RNGListLayer(layers.Layer):
            def __init__(self):
                super().__init__()
                self.seed_gens = []
                self.seed_gens.append(backend.random.SeedGenerator(seed=1))
                self.seed_gens.append(backend.random.SeedGenerator(seed=10))

            def call(self, x):
                x = backend.random.dropout(x, rate=0.5, seed=self.seed_gens[0])
                x = backend.random.dropout(x, rate=0.5, seed=self.seed_gens[1])
                return x

        layer = RNGListLayer()
        self.assertEqual(
            layer.variables,
            [layer.seed_gens[0].state, layer.seed_gens[1].state],
        )
        self.assertAllClose(layer.variables[0], [1, 0])
        self.assertAllClose(layer.variables[1], [10, 0])
        layer(np.ones((3, 4)))
        self.assertAllClose(layer.variables[0], [1, 1])
        self.assertAllClose(layer.variables[1], [10, 1])

    def test_layer_tracking(self):
        class NestedLayer(layers.Layer):
            def __init__(self, units):
                super().__init__()
                self.dense1 = layers.Dense(units)
                self.layer_dict = {
                    "dense2": layers.Dense(units),
                }
                self.layer_list = [layers.Dense(units)]
                self.units = units

            def build(self, input_shape):
                self.layer_list.append(layers.Dense(self.units))

            def call(self, x):
                x = self.dense1(x)
                x = self.layer_dict["dense2"](x)
                x = self.layer_list[0](x)
                x = self.layer_list[1](x)
                return x

        class DoubleNestedLayer(layers.Layer):
            def __init__(self, units):
                super().__init__()
                self.inner_layer = NestedLayer(units)

            def call(self, x):
                return self.inner_layer(x)

        layer = NestedLayer(3)
        layer.build((1, 3))
        self.assertLen(layer._layers, 4)
        layer(np.zeros((1, 3)))
        self.assertLen(layer.weights, 8)

        layer = DoubleNestedLayer(3)
        self.assertLen(layer._layers, 1)
        layer(np.zeros((1, 3)))
        self.assertLen(layer.inner_layer.weights, 8)
        self.assertLen(layer.weights, 8)

    def test_build_on_call(self):
        class LayerWithUnbuiltState(layers.Layer):
            def __init__(self, units):
                super().__init__()
                self.dense1 = layers.Dense(units)

            def call(self, x):
                return self.dense1(x)

        layer = LayerWithUnbuiltState(2)
        layer(backend.KerasTensor((3, 4)))
        self.assertLen(layer.weights, 2)

        class KwargsLayerWithUnbuiltState(layers.Layer):
            def __init__(self, units):
                super().__init__()
                self.dense1 = layers.Dense(units)
                self.dense2 = layers.Dense(units)

            def call(self, x1, x2):
                return self.dense1(x1) + self.dense2(x2)

        layer = KwargsLayerWithUnbuiltState(2)
        layer(backend.KerasTensor((3, 4)), backend.KerasTensor((3, 4)))
        self.assertLen(layer.weights, 4)

        layer = KwargsLayerWithUnbuiltState(2)
        layer(x1=backend.KerasTensor((3, 4)), x2=backend.KerasTensor((3, 4)))
        self.assertLen(layer.weights, 4)

    def test_activity_regularization(self):
        class ActivityRegularizer(layers.Layer):
            def call(self, x):
                return x

        layer = ActivityRegularizer(activity_regularizer="l1")
        layer(np.ones((1,)))
        self.assertLen(layer.losses, 1)
        self.assertAllClose(layer.losses[0], 0.01)

        # losses are reset upon call
        layer(np.ones((1,)))
        self.assertLen(layer.losses, 1)
        self.assertAllClose(layer.losses[0], 0.01)

        # KerasTensors are no op
        layer = ActivityRegularizer(activity_regularizer="l1")
        layer(layers.Input(batch_shape=(2, 2)))
        self.assertLen(layer.losses, 0)

    def test_add_loss(self):
        class LossLayer(layers.Layer):
            def call(self, x):
                self.add_loss(ops.sum(x))
                return x

        layer = LossLayer()
        layer(np.ones((1,)))
        self.assertLen(layer.losses, 1)
        self.assertAllClose(layer.losses[0], 1.0)

        # losses are reset upon call
        layer = LossLayer()
        layer(np.ones((1,)))
        self.assertLen(layer.losses, 1)
        self.assertAllClose(layer.losses[0], 1.0)

        # It works inside a model
        model = models.Sequential([layer])
        model(np.ones((1,)))
        self.assertLen(model.losses, 1)
        self.assertAllClose(model.losses[0], 1.0)