def train(fine_tuning):
"""Build a Keras model and train with mock data."""
features = np.array(["my first sentence", "my second sentence"])
labels = np.array([1, 0])
dataset = tf.data.Dataset.from_tensor_slices((features, labels))
module = tf.saved_model.load(FLAGS.model_dir)
# Create the sequential keras model.
l = tf.keras.layers
model = tf.keras.Sequential()
model.add(l.Reshape((), batch_input_shape=[None, 1], dtype=tf.string))
model.add(
util.CustomLayer(module, output_shape=[10], trainable=fine_tuning))
model.add(l.Dense(100, activation="relu"))
model.add(l.Dense(50, activation="relu"))
model.add(l.Dense(1, activation="sigmoid"))
model.compile(
optimizer="adam",
loss="binary_crossentropy",
metrics=["accuracy"],
# TODO(b/124446120): Remove after fixed.
run_eagerly=True)
model.fit_generator(generator=dataset.batch(1), epochs=5)
def main(argv):
del argv
features = np.array(["my first sentence", "my second sentence"])
labels = np.array([1, 0])
dataset = tf.data.Dataset.from_tensor_slices((features, labels))
embed = tf.saved_model.load(FLAGS.model_dir)
# Create the sequential keras model.
model = tf.keras.Sequential()
model.add(
util.CustomLayer(embed,
batch_input_shape=[None],
output_shape=[10],
dtype=tf.string))
model.add(tf.keras.layers.Dense(100, activation="relu"))
model.add(tf.keras.layers.Dense(50, activation="relu"))
model.add(tf.keras.layers.Dense(1, activation="sigmoid"))
model.compile(optimizer="adam",
loss="binary_crossentropy",
metrics=["accuracy"])
model.fit_generator(generator=dataset.batch(1), epochs=5)
def main(argv):
del argv
# Load a pre-trained feature extractor and wrap it for use in Keras.
obj = tf.saved_model.load(FLAGS.export_dir)
scale_regularization_losses(obj, FLAGS.regularization_loss_multiplier)
arguments = {}
if FLAGS.dropout_rate is not None:
arguments['dropout_rate'] = FLAGS.dropout_rate
feature_extractor = util.CustomLayer(obj,
output_shape=[10],
trainable=FLAGS.retrain,
arguments=arguments)
# Build a classifier with it.
model = make_classifier(feature_extractor)
# Train the classifier (possibly on a different dataset).
(x_train, y_train), (x_test, y_test) = mnist_util.load_reshaped_data(
use_fashion_mnist=FLAGS.use_fashion_mnist,
fake_tiny_data=FLAGS.fast_test_mode)
model.compile(loss=tf.keras.losses.categorical_crossentropy,
optimizer=tf.keras.optimizers.SGD(),
metrics=['accuracy'])
print('Training on %s with %d trainable and %d untrainable variables.' %
('Fashion MNIST' if FLAGS.use_fashion_mnist else 'MNIST',
len(model.trainable_variables), len(model.non_trainable_variables)))
model.fit(x_train,
y_train,
batch_size=128,
epochs=FLAGS.epochs,
verbose=1,
validation_data=(x_test, y_test))
def main(argv):
del argv
# Load a pre-trained feature extractor and wrap it for use in Keras.
obj = tf.saved_model.load(FLAGS.export_dir)
feature_extractor = util.CustomLayer(obj,
output_shape=[128],
trainable=FLAGS.retrain)
# Build a classifier with it.
model = make_classifier(feature_extractor)
# Train the classifier (possibly on a different dataset).
(x_train, y_train), (x_test, y_test) = mnist_util.load_reshaped_data(
use_fashion_mnist=FLAGS.use_fashion_mnist,
fake_tiny_data=FLAGS.fast_test_mode)
model.compile(
loss=tf.keras.losses.categorical_crossentropy,
optimizer=tf.keras.optimizers.SGD(),
metrics=['accuracy'],
# TODO(arnoegw): Remove after investigating huge allocs.
run_eagerly=True)
print('Training on %s with %d trainable and %d untrainable variables.' %
('Fashion MNIST' if FLAGS.use_fashion_mnist else 'MNIST',
len(model.trainable_variables), len(model.non_trainable_variables)))
model.fit(x_train,
y_train,
batch_size=128,
epochs=FLAGS.epochs,
steps_per_epoch=3,
verbose=1,
validation_data=(x_test, y_test))
