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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 numpy as np
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python import keras
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util as tf_test_util
from tensorflow.python.keras import metrics as metrics_module
from tensorflow.python.platform import test
from tensorflow.python.training.rmsprop import RMSPropOptimizer
class TrainingTest(test.TestCase):
def test_model_methods_with_eager_tensors_multi_io(self):
a = keras.layers.Input(shape=(3,), name='input_a')
b = keras.layers.Input(shape=(3,), name='input_b')
dense = keras.layers.Dense(4, name='dense')
c = dense(a)
d = dense(b)
e = keras.layers.Dropout(0.5, name='dropout')(c)
model = keras.models.Model([a, b], [d, e])
optimizer = RMSPropOptimizer(learning_rate=0.001)
loss = 'mse'
loss_weights = [1., 0.5]
metrics = ['mae', metrics_module.CategoricalAccuracy()]
model.compile(
optimizer,
loss,
metrics=metrics,
loss_weights=loss_weights,
sample_weight_mode=None)
input_a = keras.backend.zeros(shape=(10, 3))
input_b = keras.backend.zeros(shape=(10, 3))
target_d = keras.backend.zeros(shape=(10, 4))
target_e = keras.backend.zeros(shape=(10, 4))
model.fit(
[input_a, input_b], [target_d, target_e],
epochs=1,
batch_size=5,
verbose=0)
# Test: no shuffle.
model.fit(
[input_a, input_b], [target_d, target_e],
epochs=1,
batch_size=5,
verbose=0,
shuffle=False)
# Test: validation data.
model.fit([input_a, input_b], [target_d, target_e],
epochs=1, batch_size=2, verbose=0,
validation_data=([input_a, input_b], [target_d, target_e]))
model.train_on_batch([input_a, input_b], [target_d, target_e])
model.predict([input_a, input_b], batch_size=5)
model.evaluate([input_a, input_b], [target_d, target_e],
batch_size=2, verbose=0)
model.test_on_batch([input_a, input_b], [target_d, target_e])
# Test: mix np and tensors.
input_b = np.zeros(shape=(10, 3)).astype('float32')
target_e = np.zeros(shape=(10, 4)).astype('float32')
model.fit(
[input_a, input_b], [target_d, target_e],
epochs=1,
batch_size=5,
verbose=0)
model.fit([input_a, input_b], [target_d, target_e],
epochs=1, batch_size=2, verbose=0,
validation_data=([input_a, input_b], [target_d, target_e]))
model.fit(
[input_a, input_b], [target_d, target_e],
epochs=1,
batch_size=5,
verbose=0,
shuffle=False)
model.train_on_batch([input_a, input_b], [target_d, target_e])
model.predict([input_a, input_b], batch_size=5)
model.evaluate([input_a, input_b], [target_d, target_e],
batch_size=2, verbose=0)
model.test_on_batch([input_a, input_b], [target_d, target_e])
def test_model_methods_with_eager_tensors_single_io(self):
x = keras.layers.Input(shape=(3,), name='input')
y = keras.layers.Dense(4, name='dense')(x)
model = keras.Model(x, y)
optimizer = RMSPropOptimizer(learning_rate=0.001)
loss = 'mse'
metrics = ['mae', metrics_module.CategoricalAccuracy()]
model.compile(optimizer, loss, metrics=metrics)
inputs = keras.backend.zeros(shape=(10, 3))
targets = keras.backend.zeros(shape=(10, 4))
model.fit(inputs, targets, epochs=1, batch_size=2, verbose=0)
model.fit(inputs, targets, epochs=1, batch_size=3, verbose=0, shuffle=False)
model.fit(inputs, targets, epochs=1, batch_size=4, verbose=0,
validation_data=(inputs, targets))
model.evaluate(inputs, targets, batch_size=2, verbose=0)
model.predict(inputs, batch_size=2)
model.train_on_batch(inputs, targets)
model.test_on_batch(inputs, targets)
def test_model_fit_and_validation_with_missing_arg_errors(self):
x = keras.layers.Input(shape=(3,), name='input')
y = keras.layers.Dense(4, name='dense')(x)
model = keras.Model(x, y)
model.compile(optimizer=RMSPropOptimizer(learning_rate=0.001), loss='mse')
x = keras.backend.zeros(shape=(10, 3))
y = keras.backend.zeros(shape=(10, 4))
dataset = dataset_ops.Dataset.from_tensor_slices((x, y)).repeat(10).batch(5)
iterator = dataset.make_one_shot_iterator()
validation_dataset = dataset_ops.Dataset.from_tensor_slices(
(x, y)).repeat(10).batch(5)
validation_iterator = validation_dataset.make_one_shot_iterator()
with self.assertRaisesRegexp(
ValueError, r'specify .* `steps_per_epoch`'):
model.fit(iterator, epochs=1, verbose=0)
with self.assertRaisesRegexp(
ValueError, r'provide either `batch_size` or `validation_steps`'):
model.fit(iterator, steps_per_epoch=2, epochs=1, verbose=0,
validation_data=(x, y))
with self.assertRaisesRegexp(
ValueError, r'provide either `batch_size` or `validation_steps`'):
model.fit(iterator, steps_per_epoch=2, epochs=1, verbose=0,
validation_data=validation_dataset)
with self.assertRaisesRegexp(
ValueError, r'provide either `batch_size` or `validation_steps`'):
model.fit(iterator, steps_per_epoch=2, epochs=1, verbose=0,
validation_data=validation_iterator)
def test_generator_methods(self):
model = keras.Sequential()
model.add(keras.layers.Dense(4, input_shape=(3,)))
optimizer = RMSPropOptimizer(learning_rate=0.001)
model.compile(
optimizer, 'mse', metrics=['mae',
metrics_module.CategoricalAccuracy()])
x = np.random.random((10, 3))
y = np.random.random((10, 4))
def iterator():
while True:
yield x, y
model.fit_generator(iterator(), steps_per_epoch=3, epochs=1)
model.evaluate_generator(iterator(), steps=3)
out = model.predict_generator(iterator(), steps=3)
self.assertEqual(out.shape, (30, 4))
class CorrectnessTest(test.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_loss_correctness(self):
# Test that training loss is the same in eager and graph
# (by comparing it to a reference value in a deterministic case)
model = keras.Sequential()
model.add(keras.layers.Dense(3,
activation='relu',
input_dim=4,
kernel_initializer='ones'))
model.add(keras.layers.Dense(2,
activation='softmax',
kernel_initializer='ones'))
model.compile(loss='sparse_categorical_crossentropy',
optimizer=RMSPropOptimizer(learning_rate=0.001))
x = np.ones((100, 4))
np.random.seed(123)
y = np.random.randint(0, 1, size=(100, 1))
history = model.fit(x, y, epochs=1, batch_size=10)
self.assertEqual(
np.around(history.history['loss'][-1], decimals=4), 0.6173)
@tf_test_util.run_in_graph_and_eager_modes
def test_loss_correctness_with_iterator(self):
# Test that training loss is the same in eager and graph
# (by comparing it to a reference value in a deterministic case)
model = keras.Sequential()
model.add(
keras.layers.Dense(
3, activation='relu', input_dim=4, kernel_initializer='ones'))
model.add(
keras.layers.Dense(2, activation='softmax', kernel_initializer='ones'))
model.compile(
loss='sparse_categorical_crossentropy',
optimizer=RMSPropOptimizer(learning_rate=0.001))
x = np.ones((100, 4), dtype=np.float32)
np.random.seed(123)
y = np.random.randint(0, 1, size=(100, 1))
dataset = dataset_ops.Dataset.from_tensor_slices((x, y))
dataset = dataset.repeat(100)
dataset = dataset.batch(10)
iterator = dataset.make_one_shot_iterator()
history = model.fit(iterator, epochs=1, steps_per_epoch=10)
self.assertEqual(np.around(history.history['loss'][-1], decimals=4), 0.6173)
def test_no_loss_in_call(self):
class HasLoss(keras.layers.Layer):
def call(self, x):
self.add_loss(x)
return x
layer = HasLoss()
with self.assertRaises(RuntimeError):
layer(1.)
with ops.Graph().as_default():
layer(1.)
if __name__ == '__main__':
ops.enable_eager_execution()
test.main()
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