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Update Sequential model to raise error for multi-output layer in the deferred mode.

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This is a update for #36624, which we should show explicit error, rather than let the code proceed and failed down the road.

PiperOrigin-RevId: 295248095
Change-Id: I9cf9d0267f222c3994f3199bd9b49d86196ebb3b
This commit is contained in:
Scott Zhu 2020-02-14 16:06:18 -08:00 committed by TensorFlower Gardener
parent 59d2ac77e4
commit 619ca02f2d
2 changed files with 20 additions and 25 deletions
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17
tensorflow/python/keras/engine/sequential.py
View File

@ -39,6 +39,11 @@ from tensorflow.python.util import tf_inspect
from tensorflow.python.util.tf_export import keras_export
SINGLE_LAYER_OUTPUT_ERROR_MSG = ('All layers in a Sequential model should have '
'a single output tensor. For multi-output '
'layers, use the functional API.')
@keras_export('keras.Sequential', 'keras.models.Sequential')
class Sequential(training.Model):
"""`Sequential` groups a linear stack of layers into a `tf.keras.Model`.
@ -195,10 +200,7 @@ class Sequential(training.Model):
if set_inputs:
# If an input layer (placeholder) is available.
if len(nest.flatten(layer._inbound_nodes[-1].output_tensors)) != 1:
raise ValueError('All layers in a Sequential model '
'should have a single output tensor. '
'For multi-output layers, '
'use the functional API.')
raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
self.outputs = [
nest.flatten(layer._inbound_nodes[-1].output_tensors)[0]
]
@ -209,10 +211,7 @@ class Sequential(training.Model):
# refresh its output.
output_tensor = layer(self.outputs[0])
if len(nest.flatten(output_tensor)) != 1:
raise TypeError('All layers in a Sequential model '
'should have a single output tensor. '
'For multi-output layers, '
'use the functional API.')
raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
self.outputs = [output_tensor]
if self.outputs:
@ -286,6 +285,8 @@ class Sequential(training.Model):
outputs = layer(inputs, **kwargs)
if len(nest.flatten(outputs)) != 1:
raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
# `outputs` will be the inputs to the next layer.
inputs = outputs
mask = outputs._keras_mask

28
tensorflow/python/keras/engine/sequential_test.py
View File

@ -215,23 +215,6 @@ class TestSequential(keras_parameterized.TestCase):
model = keras.models.Sequential()
model.add(None)
# Added layers cannot have multiple outputs
class MyLayer(keras.layers.Layer):
def call(self, inputs):
return [3 * inputs, 2 * inputs]
def compute_output_shape(self, input_shape):
return [input_shape, input_shape]
with self.assertRaises(ValueError):
model = keras.models.Sequential()
model.add(MyLayer(input_shape=(3,)))
with self.assertRaises(TypeError):
model = keras.models.Sequential()
model.add(keras.layers.Dense(1, input_dim=1))
model.add(MyLayer())
@keras_parameterized.run_all_keras_modes
def test_nested_sequential_trainability(self):
input_dim = 20
@ -405,6 +388,17 @@ class TestSequential(keras_parameterized.TestCase):
ValueError, 'should have a single output tensor'):
keras.Sequential([MultiOutputLayer(input_shape=(3,))])
with self.assertRaisesRegexp(
ValueError, 'should have a single output tensor'):
keras.Sequential([
keras.layers.Dense(1, input_shape=(3,)),
MultiOutputLayer()])
# Should also raise error in a deferred build mode
with self.assertRaisesRegexp(
ValueError, 'should have a single output tensor'):
keras.Sequential([MultiOutputLayer()])(np.zeros((10, 10)))
@keras_parameterized.run_all_keras_modes
def test_layer_add_after_compile_deferred(self):
model = keras.Sequential([keras.layers.Dense(3)])