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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests specific to `Sequential` model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python import keras
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.framework import test_util as tf_test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
from tensorflow.python.training import rmsprop
class TestSequential(test.TestCase):
"""Most Sequential model API tests are covered in `training_test.py`.
"""
@tf_test_util.run_in_graph_and_eager_modes
def test_basic_methods(self):
model = keras.models.Sequential()
model.add(keras.layers.Dense(1, input_dim=2))
model.add(keras.layers.Dropout(0.3, name='dp'))
model.add(keras.layers.Dense(2, kernel_regularizer='l2',
kernel_constraint='max_norm'))
self.assertEqual(len(model.layers), 3)
self.assertEqual(len(model.weights), 2 * 2)
self.assertEqual(model.get_layer(name='dp').name, 'dp')
@tf_test_util.run_in_graph_and_eager_modes
def test_sequential_pop(self):
num_hidden = 5
input_dim = 3
batch_size = 5
num_classes = 2
model = keras.models.Sequential()
model.add(keras.layers.Dense(num_hidden, input_dim=input_dim))
model.add(keras.layers.Dense(num_classes))
model.compile(loss='mse', optimizer=rmsprop.RMSPropOptimizer(1e-3))
x = np.random.random((batch_size, input_dim))
y = np.random.random((batch_size, num_classes))
model.fit(x, y, epochs=1)
model.pop()
self.assertEqual(len(model.layers), 1)
self.assertEqual(model.output_shape, (None, num_hidden))
model.compile(loss='mse', optimizer=rmsprop.RMSPropOptimizer(1e-3))
y = np.random.random((batch_size, num_hidden))
model.fit(x, y, epochs=1)
# Test popping single-layer model
model = keras.models.Sequential()
model.add(keras.layers.Dense(num_hidden, input_dim=input_dim))
model.pop()
self.assertEqual(model.layers, [])
self.assertEqual(model.outputs, None)
# Invalid use case
model = keras.models.Sequential()
with self.assertRaises(TypeError):
model.pop()
@tf_test_util.run_in_graph_and_eager_modes
def test_sequential_deferred_build_with_np_arrays(self):
num_hidden = 5
input_dim = 3
batch_size = 5
num_classes = 2
model = keras.models.Sequential()
# We don't specify the input shape.
model.add(keras.layers.Dense(num_hidden))
model.add(keras.layers.Dense(num_classes))
model.compile(loss='mse', optimizer=rmsprop.RMSPropOptimizer(1e-3))
self.assertEqual(len(model.layers), 2)
self.assertEqual(len(model.weights), 0)
self.assertFalse(model.built)
x = np.random.random((batch_size, input_dim))
y = np.random.random((batch_size, num_classes))
model.fit(x, y, epochs=1)
self.assertTrue(model.built)
self.assertEqual(model.inputs[0].get_shape().as_list(), [None, input_dim])
self.assertEqual(model.outputs[0].get_shape().as_list(),
[None, num_classes])
self.assertEqual(len(model.weights), 2 * 2)
@tf_test_util.run_in_graph_and_eager_modes
def test_sequential_deferred_build_with_dataset_iterators(self):
num_hidden = 5
input_dim = 3
num_classes = 2
num_samples = 50
steps_per_epoch = 10
model = keras.models.Sequential()
# We don't specify the input shape.
model.add(keras.layers.Dense(num_hidden))
model.add(keras.layers.Dense(num_classes))
model.compile(loss='mse', optimizer=rmsprop.RMSPropOptimizer(1e-3))
self.assertEqual(len(model.layers), 2)
self.assertEqual(len(model.weights), 0)
self.assertFalse(model.built)
x = array_ops.ones((num_samples, input_dim))
y = array_ops.zeros((num_samples, num_classes))
dataset = dataset_ops.Dataset.from_tensor_slices((x, y))
dataset = dataset.repeat(100)
dataset = dataset.batch(10)
iterator = dataset.make_one_shot_iterator()
model.fit(iterator, epochs=1, steps_per_epoch=steps_per_epoch)
self.assertTrue(model.built)
self.assertEqual(model.inputs[0].get_shape().as_list(), [None, input_dim])
self.assertEqual(model.outputs[0].get_shape().as_list(),
[None, num_classes])
self.assertEqual(len(model.weights), 2 * 2)
def test_training_and_eval_methods_on_symbolic_tensors(self):
with self.test_session():
def create_model():
model = keras.Sequential()
model.add(keras.layers.Dense(10, activation='relu'))
model.add(keras.layers.Dense(4, activation='softmax'))
model.compile(
optimizer=rmsprop.RMSPropOptimizer(1e-3),
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
inputs = keras.backend.zeros(shape=(10, 3))
targets = keras.backend.zeros(shape=(10, 4))
model = create_model()
model.fit(inputs, targets, epochs=10, steps_per_epoch=30)
model = create_model()
model.evaluate(inputs, targets, steps=2, verbose=0)
model = create_model()
model.predict(inputs, steps=2)
model = create_model()
model.train_on_batch(inputs, targets)
model = create_model()
model.test_on_batch(inputs, targets)
model = create_model()
model.fit(
inputs,
targets,
epochs=1,
steps_per_epoch=2,
verbose=0,
validation_data=(inputs, targets),
validation_steps=2)
@tf_test_util.run_in_graph_and_eager_modes
def test_invalid_use_cases(self):
# Added objects must be layer instances
with self.assertRaises(TypeError):
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())
@tf_test_util.run_in_graph_and_eager_modes
def test_nested_sequential_trainability(self):
input_dim = 20
num_units = 10
num_classes = 2
inner_model = keras.models.Sequential()
inner_model.add(keras.layers.Dense(num_units, input_shape=(input_dim,)))
model = keras.models.Sequential()
model.add(inner_model)
model.add(keras.layers.Dense(num_classes))
self.assertEqual(len(model.layers), 2)
self.assertEqual(len(model.trainable_weights), 4)
inner_model.trainable = False
self.assertEqual(len(model.trainable_weights), 2)
inner_model.trainable = True
self.assertEqual(len(model.trainable_weights), 4)
def test_sequential_update_disabling(self):
val_a = np.random.random((10, 4))
val_out = np.random.random((10, 4))
with self.test_session():
model = keras.models.Sequential()
model.add(keras.layers.BatchNormalization(input_shape=(4,)))
assert model.updates
model.trainable = False
assert not model.updates
model.compile('sgd', 'mse')
assert not model.updates
x1 = model.predict(val_a)
model.train_on_batch(val_a, val_out)
x2 = model.predict(val_a)
self.assertAllClose(x1, x2, atol=1e-7)
model.trainable = True
model.compile('sgd', 'mse')
assert model.updates
model.train_on_batch(val_a, val_out)
x2 = model.predict(val_a)
assert np.abs(np.sum(x1 - x2)) > 1e-5
def test_sequential_deferred_build_serialization(self):
num_hidden = 5
input_dim = 3
batch_size = 5
num_classes = 2
model = keras.models.Sequential()
# We don't specify the input shape.
model.add(keras.layers.Dense(num_hidden))
model.add(keras.layers.Dense(num_classes))
model.compile(loss='mse', optimizer=rmsprop.RMSPropOptimizer(1e-3))
self.assertFalse(model.built)
x = np.random.random((batch_size, input_dim))
y = np.random.random((batch_size, num_classes))
model.train_on_batch(x, y)
self.assertTrue(model.built)
config = model.get_config()
new_model = keras.models.Sequential.from_config(config)
self.assertTrue(new_model.built)
self.assertEqual(len(model.layers), 2)
self.assertEqual(len(model.weights), 4)
if __name__ == '__main__':
test.main()
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