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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 training routines."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tempfile
import numpy as np
from tensorflow.contrib.keras.python import keras
from tensorflow.python.platform import test
try:
import h5py # pylint:disable=g-import-not-at-top
except ImportError:
h5py = None
class TestModelSaving(test.TestCase):
def test_sequential_model_saving(self):
if h5py is None:
return # Skip test if models cannot be saved.
with self.test_session():
model = keras.models.Sequential()
model.add(keras.layers.Dense(2, input_shape=(3,)))
model.add(keras.layers.RepeatVector(3))
model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
model.compile(loss=keras.losses.MSE,
optimizer=keras.optimizers.RMSprop(lr=0.0001),
metrics=[keras.metrics.categorical_accuracy],
sample_weight_mode='temporal')
x = np.random.random((1, 3))
y = np.random.random((1, 3, 3))
model.train_on_batch(x, y)
out = model.predict(x)
_, fname = tempfile.mkstemp('.h5')
keras.models.save_model(model, fname)
new_model = keras.models.load_model(fname)
os.remove(fname)
out2 = new_model.predict(x)
self.assertAllClose(out, out2, atol=1e-05)
# test that new updates are the same with both models
x = np.random.random((1, 3))
y = np.random.random((1, 3, 3))
model.train_on_batch(x, y)
new_model.train_on_batch(x, y)
out = model.predict(x)
out2 = new_model.predict(x)
self.assertAllClose(out, out2, atol=1e-05)
def test_sequential_model_saving_2(self):
if h5py is None:
return # Skip test if models cannot be saved.
with self.test_session():
# test with custom optimizer, loss
class CustomOp(keras.optimizers.RMSprop):
pass
def custom_loss(y_true, y_pred):
return keras.losses.mse(y_true, y_pred)
model = keras.models.Sequential()
model.add(keras.layers.Dense(2, input_shape=(3,)))
model.add(keras.layers.Dense(3))
model.compile(loss=custom_loss, optimizer=CustomOp(), metrics=['acc'])
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
out = model.predict(x)
_, fname = tempfile.mkstemp('.h5')
keras.models.save_model(model, fname)
model = keras.models.load_model(
fname,
custom_objects={'CustomOp': CustomOp,
'custom_loss': custom_loss})
os.remove(fname)
out2 = model.predict(x)
self.assertAllClose(out, out2, atol=1e-05)
def test_fuctional_model_saving(self):
if h5py is None:
return # Skip test if models cannot be saved.
with self.test_session():
inputs = keras.layers.Input(shape=(3,))
x = keras.layers.Dense(2)(inputs)
output = keras.layers.Dense(3)(x)
model = keras.models.Model(inputs, output)
model.compile(loss=keras.losses.MSE,
optimizer=keras.optimizers.RMSprop(lr=0.0001),
metrics=[keras.metrics.categorical_accuracy])
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
out = model.predict(x)
_, fname = tempfile.mkstemp('.h5')
keras.models.save_model(model, fname)
model = keras.models.load_model(fname)
os.remove(fname)
out2 = model.predict(x)
self.assertAllClose(out, out2, atol=1e-05)
def test_saving_without_compilation(self):
if h5py is None:
return # Skip test if models cannot be saved.
with self.test_session():
model = keras.models.Sequential()
model.add(keras.layers.Dense(2, input_shape=(3,)))
model.add(keras.layers.Dense(3))
model.compile(loss='mse', optimizer='sgd', metrics=['acc'])
_, fname = tempfile.mkstemp('.h5')
keras.models.save_model(model, fname)
model = keras.models.load_model(fname)
os.remove(fname)
def test_saving_right_after_compilation(self):
if h5py is None:
return # Skip test if models cannot be saved.
with self.test_session():
model = keras.models.Sequential()
model.add(keras.layers.Dense(2, input_shape=(3,)))
model.add(keras.layers.Dense(3))
model.compile(loss='mse', optimizer='sgd', metrics=['acc'])
model.model._make_train_function()
_, fname = tempfile.mkstemp('.h5')
keras.models.save_model(model, fname)
model = keras.models.load_model(fname)
os.remove(fname)
class TestSequential(test.TestCase):
"""Most Sequential model API tests are covered in `training_test.py`.
"""
def test_sequential_pop(self):
num_hidden = 5
input_dim = 3
batch_size = 5
num_classes = 2
with self.test_session():
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='sgd')
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='sgd')
y = np.random.random((batch_size, num_hidden))
model.fit(x, y, epochs=1)
if __name__ == '__main__':
test.main()
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