138 lines
4.2 KiB
Python
138 lines
4.2 KiB
Python
import numpy as np
|
|
|
|
from keras_core import backend
|
|
from keras_core import layers
|
|
from keras_core import testing
|
|
from keras_core.layers.core.input_layer import Input
|
|
from keras_core.models.functional import Functional
|
|
from keras_core.models.sequential import Sequential
|
|
|
|
|
|
class SequentialTest(testing.TestCase):
|
|
def test_basic_flow_with_input(self):
|
|
model = Sequential(name="seq")
|
|
model.add(Input(shape=(2,), batch_size=3))
|
|
model.add(layers.Dense(4))
|
|
model.add(layers.Dense(5))
|
|
|
|
self.assertEqual(len(model.layers), 2)
|
|
|
|
# Test eager call
|
|
x = np.random.random((3, 2))
|
|
y = model(x)
|
|
self.assertTrue(model.built)
|
|
self.assertEqual(type(model._functional), Functional)
|
|
self.assertEqual(y.shape, (3, 5))
|
|
|
|
# Test symbolic call
|
|
x = backend.KerasTensor((3, 2))
|
|
y = model(x)
|
|
self.assertEqual(y.shape, (3, 5))
|
|
|
|
# Test `layers` constructor arg
|
|
model = Sequential(
|
|
layers=[
|
|
Input(shape=(2,), batch_size=3),
|
|
layers.Dense(4),
|
|
layers.Dense(5),
|
|
]
|
|
)
|
|
x = np.random.random((3, 2))
|
|
y = model(x)
|
|
self.assertEqual(y.shape, (3, 5))
|
|
|
|
# Test pop
|
|
model.pop()
|
|
self.assertFalse(model.built)
|
|
self.assertEqual(model._functional, None)
|
|
x = np.random.random((3, 2))
|
|
y = model(x)
|
|
self.assertTrue(model.built)
|
|
self.assertEqual(type(model._functional), Functional)
|
|
self.assertEqual(y.shape, (3, 4))
|
|
|
|
def test_basic_flow_deferred(self):
|
|
model = Sequential(name="seq")
|
|
model.add(layers.Dense(4))
|
|
model.add(layers.Dense(5))
|
|
|
|
self.assertEqual(len(model.layers), 2)
|
|
|
|
# Test eager call
|
|
x = np.random.random((3, 2))
|
|
y = model(x)
|
|
self.assertTrue(model.built)
|
|
self.assertEqual(type(model._functional), Functional)
|
|
self.assertEqual(y.shape, (3, 5))
|
|
|
|
# Test symbolic call
|
|
x = backend.KerasTensor((3, 2))
|
|
y = model(x)
|
|
self.assertEqual(y.shape, (3, 5))
|
|
|
|
# Test `layers` constructor arg
|
|
model = Sequential(
|
|
layers=[
|
|
layers.Dense(4),
|
|
layers.Dense(5),
|
|
]
|
|
)
|
|
x = np.random.random((3, 2))
|
|
y = model(x)
|
|
self.assertEqual(y.shape, (3, 5))
|
|
|
|
# Test pop
|
|
model.pop()
|
|
self.assertFalse(model.built)
|
|
self.assertEqual(model._functional, None)
|
|
x = np.random.random((3, 2))
|
|
y = model(x)
|
|
self.assertTrue(model.built)
|
|
self.assertEqual(type(model._functional), Functional)
|
|
self.assertEqual(y.shape, (3, 4))
|
|
|
|
def test_dict_inputs(self):
|
|
class DictLayer(layers.Layer):
|
|
def call(self, inputs):
|
|
assert isinstance(inputs, dict)
|
|
return inputs
|
|
|
|
model = Sequential([DictLayer()])
|
|
x = {"a": np.random.random((3, 2)), "b": np.random.random((3, 2))}
|
|
y = model(x)
|
|
self.assertEqual(type(y), dict)
|
|
|
|
def test_errors(self):
|
|
# Trying to pass 2 Inputs
|
|
model = Sequential()
|
|
model.add(Input(shape=(2,), batch_size=3))
|
|
with self.assertRaisesRegex(ValueError, "already been configured"):
|
|
model.add(Input(shape=(2,), batch_size=3))
|
|
with self.assertRaisesRegex(ValueError, "already been configured"):
|
|
model.add(layers.InputLayer(shape=(2,), batch_size=3))
|
|
|
|
# Same name 2x
|
|
model = Sequential()
|
|
model.add(layers.Dense(2, name="dense"))
|
|
with self.assertRaisesRegex(ValueError, "should have unique names"):
|
|
model.add(layers.Dense(2, name="dense"))
|
|
|
|
# No layers
|
|
model = Sequential()
|
|
x = np.random.random((3, 2))
|
|
with self.assertRaisesRegex(ValueError, "no layers"):
|
|
model(x)
|
|
|
|
# Build conflict
|
|
model = Sequential()
|
|
model.add(Input(shape=(2,), batch_size=3))
|
|
model.add(layers.Dense(2))
|
|
with self.assertRaisesRegex(ValueError, "already been configured"):
|
|
model.build((3, 4))
|
|
# But this works
|
|
model.build((3, 2))
|
|
|
|
def test_serialization(self):
|
|
# TODO
|
|
pass
|