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from __future__ import print_function
import os
import tempfile
import pytest
import keras
from keras import layers
from keras.utils.test_utils import get_test_data
@pytest.mark.skipif(keras.backend.backend() != 'tensorflow',
reason='Requires TF backend')
def test_tf_optimizer():
import tensorflow as tf
num_hidden = 10
output_dim = 2
input_dim = 10
target = 0.8
if tf.__version__.startswith('1.'):
optimizer = tf.train.AdadeltaOptimizer(
learning_rate=1., rho=0.95, epsilon=1e-08)
else:
optimizer = tf.keras.optimizers.Adadelta(
learning_rate=1., rho=0.95, epsilon=1e-08)
(x_train, y_train), (x_test, y_test) = get_test_data(
num_train=1000, num_test=200,
input_shape=(input_dim,),
classification=True, num_classes=output_dim)
model = keras.Sequential()
model.add(layers.Dense(num_hidden,
activation='relu',
input_shape=(input_dim,)))
model.add(layers.Dense(output_dim, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
history = model.fit(x_train, y_train, epochs=8, batch_size=16,
validation_data=(x_test, y_test), verbose=2)
assert history.history['val_accuracy'][-1] >= target
# Test saving.
_, fname = tempfile.mkstemp('.h5')
model.save(fname)
model = keras.models.load_model(fname)
assert len(model.weights) == 4
os.remove(fname)
if __name__ == '__main__':
pytest.main([__file__])
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