765e400f66
* Add test for epoch iterator with insufficient data * Add `steps_per_epoch` to `_test_fit_flow` * Update error tolerance * Update error tolerance * Apply format; branch on steps per epoch * Revert formatting changes
216 lines
7.1 KiB
Python
216 lines
7.1 KiB
Python
import numpy as np
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from keras_core import backend
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from keras_core import initializers
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from keras_core import layers
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from keras_core import losses
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from keras_core import metrics
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from keras_core import optimizers
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from keras_core import testing
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if backend.backend() == "jax":
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from keras_core.backend.jax.trainer import JAXTrainer as Trainer
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else:
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from keras_core.backend.tensorflow.trainer import (
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TensorFlowTrainer as Trainer,
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)
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# A model is just a layer mixed in with a Trainer.
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class ExampleModel(layers.Dense, Trainer):
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def __init__(self, units):
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layers.Dense.__init__(
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self,
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units=units,
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use_bias=False,
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kernel_initializer=initializers.Ones(),
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)
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Trainer.__init__(self)
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class OutputStructModel(layers.Layer, Trainer):
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def __init__(self, units):
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layers.Layer.__init__(self)
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Trainer.__init__(self)
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self.dense_1 = layers.Dense(
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units,
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use_bias=False,
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kernel_initializer=initializers.Ones(),
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)
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self.dense_2 = layers.Dense(
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units,
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use_bias=False,
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kernel_initializer=initializers.Ones(),
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)
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def call(self, x):
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return {
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"y_one": self.dense_1(x),
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"y_two": self.dense_2(x),
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}
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class TestTrainer(testing.TestCase):
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def test_metric_tracking(self):
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class ModelWithMetric(layers.Dense, Trainer):
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def __init__(self, units):
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layers.Dense.__init__(
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self,
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units=units,
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use_bias=False,
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kernel_initializer=initializers.Ones(),
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)
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Trainer.__init__(self)
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self.my_metric = metrics.MeanSquaredError(name="my_metric")
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model = ModelWithMetric(units=3)
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model.compile(
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optimizer=optimizers.SGD(),
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loss=losses.MeanSquaredError(),
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metrics=[metrics.MeanSquaredError()],
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)
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x = np.ones((2, 4))
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y = np.zeros((2, 3))
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# Fit the model to make sure compile_metrics are built
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model.fit(x, y, batch_size=2, epochs=1)
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# The model should have 3 metrics: loss_tracker, compile_metrics,
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# my_metric.
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self.assertEqual(len(model.metrics), 3)
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self.assertEqual(model.metrics[0], model._loss_tracker)
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self.assertEqual(model.metrics[1], model.my_metric)
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self.assertEqual(model.metrics[2], model._compile_metrics)
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# All metrics should have their weights created
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self.assertEqual(len(model._loss_tracker.variables), 2)
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self.assertEqual(len(model._compile_metrics.variables), 2)
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self.assertEqual(len(model.my_metric.variables), 2)
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# And those weights are tracked at the model level
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self.assertEqual(len(model.metrics_variables), 6)
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def _test_fit_flow(self, run_eagerly, jit_compile, use_steps_per_epoch):
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model = ExampleModel(units=3)
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epochs = 3
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batch_size = 20
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steps_per_epoch = 7
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dataset_size = batch_size * (steps_per_epoch - 2)
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x = np.ones((dataset_size, 4))
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y = np.zeros((dataset_size, 3))
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model.compile(
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optimizer=optimizers.SGD(),
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loss=losses.MeanSquaredError(),
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metrics=[metrics.MeanSquaredError()],
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run_eagerly=run_eagerly,
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jit_compile=jit_compile,
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)
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history = model.fit(
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x,
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y,
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batch_size=batch_size,
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steps_per_epoch=steps_per_epoch if use_steps_per_epoch else None,
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epochs=epochs,
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)
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history = history.history
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self.assertIn("loss", history)
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self.assertIn("mean_squared_error", history)
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self.assertAllClose(
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history["mean_squared_error"],
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[14.402393, 10.991339, 8.388159],
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atol=6.1051628e-1,
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)
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def test_fit_flow_eager(self):
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self._test_fit_flow(
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run_eagerly=True, jit_compile=False, use_steps_per_epoch=False
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)
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def test_fit_flow_graph_fn(self):
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self._test_fit_flow(
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run_eagerly=False, jit_compile=False, use_steps_per_epoch=False
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)
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def test_fit_flow_jit(self):
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self._test_fit_flow(
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run_eagerly=False, jit_compile=True, use_steps_per_epoch=False
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)
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def test_fit_steps_per_epoch_flow_eager(self):
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self._test_fit_flow(
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run_eagerly=True, jit_compile=False, use_steps_per_epoch=True
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)
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def test_fit_steps_per_epoch_flow_graph_fn(self):
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self._test_fit_flow(
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run_eagerly=False, jit_compile=False, use_steps_per_epoch=True
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)
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def test_fit_steps_per_epoch_flow_jit(self):
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self._test_fit_flow(
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run_eagerly=False, jit_compile=True, use_steps_per_epoch=True
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)
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def _test_evaluate_flow(self, run_eagerly, jit_compile):
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model = ExampleModel(units=3)
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x = np.ones((100, 4))
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y = np.zeros((100, 3))
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batch_size = 16
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model.compile(
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optimizer=optimizers.SGD(),
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loss=losses.MeanSquaredError(),
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metrics=[metrics.MeanSquaredError()],
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run_eagerly=run_eagerly,
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jit_compile=jit_compile,
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)
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output = model.evaluate(x, y, batch_size=batch_size)
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self.assertAllClose(output, [16.0, 16.0])
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output = model.evaluate(x, y, batch_size=batch_size, return_dict=True)
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self.assertTrue(isinstance(output, dict))
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self.assertIn("loss", output)
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self.assertIn("mean_squared_error", output)
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self.assertAllClose(output["mean_squared_error"], 16.0)
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def test_evaluate_flow_eager(self):
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self._test_evaluate_flow(run_eagerly=True, jit_compile=False)
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def test_evaluate_flow_graph_fn(self):
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self._test_evaluate_flow(run_eagerly=False, jit_compile=False)
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def test_evaluate_flow_jit(self):
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self._test_evaluate_flow(run_eagerly=False, jit_compile=True)
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def _test_predict_flow(self, run_eagerly, jit_compile):
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# Test basic example
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model = ExampleModel(units=3)
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model.run_eagerly = run_eagerly
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model.jit_compile = jit_compile
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x = np.ones((100, 4))
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batch_size = 16
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outputs = model.predict(x, batch_size=batch_size)
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self.assertAllClose(outputs, 4 * np.ones((100, 3)))
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# Test with output struct
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model = OutputStructModel(units=3)
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model.run_eagerly = run_eagerly
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model.jit_compile = jit_compile
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x = np.ones((100, 4))
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batch_size = 16
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outputs = model.predict(x, batch_size=batch_size)
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self.assertTrue(isinstance(outputs, dict))
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self.assertEqual(len(outputs), 2)
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self.assertAllClose(outputs["y_one"], 4 * np.ones((100, 3)))
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self.assertAllClose(outputs["y_two"], 4 * np.ones((100, 3)))
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def test_predict_flow_eager(self):
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self._test_predict_flow(run_eagerly=True, jit_compile=False)
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def test_predict_flow_graph_fn(self):
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self._test_predict_flow(run_eagerly=False, jit_compile=False)
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def test_predict_flow_jit(self):
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self._test_predict_flow(run_eagerly=False, jit_compile=True)
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