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# Copyright 2016 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 for `models.py` (model cloning, mainly)."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
from tensorflow.python import keras
from tensorflow.python.framework import test_util
from tensorflow.python.platform import test
from tensorflow.python.training import adam
class TestModelCloning(test.TestCase):
def test_clone_sequential_model(self):
with self.test_session():
val_a = np.random.random((10, 4))
val_out = np.random.random((10, 4))
model = keras.models.Sequential()
model.add(keras.layers.Dense(4, input_shape=(4,)))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(4))
# Everything should work in a new session.
keras.backend.clear_session()
with self.test_session():
# With placeholder creation
new_model = keras.models.clone_model(model)
# update ops from batch norm needs to be included
self.assertEquals(len(new_model.get_updates_for(new_model.inputs)), 2)
new_model.compile('rmsprop', 'mse')
new_model.train_on_batch(val_a, val_out)
# On top of new tensor
input_a = keras.Input(shape=(4,))
new_model = keras.models.clone_model(
model, input_tensors=input_a)
self.assertEquals(len(new_model.get_updates_for(new_model.inputs)), 2)
new_model.compile('rmsprop', 'mse')
new_model.train_on_batch(val_a, val_out)
# On top of new, non-Keras tensor
input_a = keras.backend.variable(val_a)
new_model = keras.models.clone_model(
model, input_tensors=input_a)
self.assertEquals(len(new_model.get_updates_for(new_model.inputs)), 2)
new_model.compile('rmsprop', 'mse')
new_model.train_on_batch(None, val_out)
def test_clone_functional_model(self):
with self.test_session():
val_a = np.random.random((10, 4))
val_b = np.random.random((10, 4))
val_out = np.random.random((10, 4))
input_a = keras.Input(shape=(4,))
input_b = keras.Input(shape=(4,))
dense_1 = keras.layers.Dense(4,)
dense_2 = keras.layers.Dense(4,)
x_a = dense_1(input_a)
x_a = keras.layers.Dropout(0.5)(x_a)
x_a = keras.layers.BatchNormalization()(x_a)
x_b = dense_1(input_b)
x_a = dense_2(x_a)
outputs = keras.layers.add([x_a, x_b])
model = keras.models.Model([input_a, input_b], outputs)
# Everything should work in a new session.
keras.backend.clear_session()
with self.test_session():
# With placeholder creation
new_model = keras.models.clone_model(model)
self.assertEquals(len(new_model.get_updates_for(new_model.inputs)), 2)
new_model.compile('rmsprop', 'mse')
new_model.train_on_batch([val_a, val_b], val_out)
# On top of new tensors
input_a = keras.Input(shape=(4,), name='a')
input_b = keras.Input(shape=(4,), name='b')
new_model = keras.models.clone_model(
model, input_tensors=[input_a, input_b])
self.assertEquals(len(new_model.get_updates_for(new_model.inputs)), 2)
new_model.compile('rmsprop', 'mse')
new_model.train_on_batch([val_a, val_b], val_out)
# On top of new, non-Keras tensors
input_a = keras.backend.variable(val_a)
input_b = keras.backend.variable(val_b)
new_model = keras.models.clone_model(
model, input_tensors=[input_a, input_b])
self.assertEquals(len(new_model.get_updates_for(new_model.inputs)), 2)
new_model.compile('rmsprop', 'mse')
new_model.train_on_batch(None, val_out)
def test_model_cloning_invalid_use_cases(self):
seq_model = keras.models.Sequential()
seq_model.add(keras.layers.Dense(4, input_shape=(4,)))
x = keras.Input((4,))
y = keras.layers.Dense(4)(x)
fn_model = keras.models.Model(x, y)
with self.assertRaises(ValueError):
keras.models._clone_functional_model(seq_model)
with self.assertRaises(ValueError):
keras.models._clone_functional_model(None)
with self.assertRaises(ValueError):
keras.models._clone_sequential_model(fn_model)
with self.assertRaises(ValueError):
keras.models._clone_sequential_model(seq_model, input_tensors=[x, x])
with self.assertRaises(ValueError):
keras.models._clone_sequential_model(seq_model, input_tensors=y)
class CheckpointingTests(test.TestCase):
@test_util.run_in_graph_and_eager_modes
def test_optimizer_dependency(self):
model = keras.models.Sequential()
model.add(keras.layers.Dense(1, input_shape=(4,)))
opt = adam.AdamOptimizer(0.01)
model.compile(optimizer=opt, loss='mse')
model.fit(x=np.array([[1., 2., 3., 4.]]), y=[1.], epochs=2)
save_prefix = os.path.join(self.get_temp_dir(), 'ckpt')
beta1_power, _ = opt._get_beta_accumulators()
self.evaluate(beta1_power.assign(12.))
model.save_weights(save_prefix)
self.evaluate(beta1_power.assign(13.))
model.load_weights(save_prefix)
self.assertEqual(12., self.evaluate(beta1_power))
class TestModelBackend(test.TestCase):
def test_model_backend_float64_use_cases(self):
# Test case for GitHub issue 19318
floatx = keras.backend.floatx()
keras.backend.set_floatx('float64')
x = keras.Input((5,))
y = keras.layers.Dense(1)(x)
model = keras.models.Model(x, y)
model.compile('rmsprop', 'mse')
keras.backend.set_floatx(floatx)
if __name__ == '__main__':
test.main()
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