def train(self, dataset_uri):
im_sz = self._knobs.get('image_size')
bs = self._knobs.get('batch_size')
ep = self._knobs.get('epochs')
logger.log('Available devices: {}'.format(str(device_lib.list_local_devices())))
# Define 2 plots: Loss against time, loss against epochs
logger.define_loss_plot()
logger.define_plot('Loss Over Time', ['loss'])
dataset = dataset_utils.load_dataset_of_image_files(dataset_uri, image_size=[im_sz, im_sz])
num_classes = dataset.classes
(images, classes) = zip(*[(image, image_class) for (image, image_class) in dataset])
images = np.asarray(images)
classes = np.asarray(classes)
with self._graph.as_default():
self._model = self._build_model(num_classes)
with self._sess.as_default():
self._model.fit(
images,
classes,
verbose=0,
epochs=ep,
batch_size=bs,
callbacks=[
tf.keras.callbacks.LambdaCallback(on_epoch_end=self._on_train_epoch_end)
]
)
# Compute train accuracy
(loss, accuracy) = self._model.evaluate(images, classes)
logger.log('Train loss: {}'.format(loss))
logger.log('Train accuracy: {}'.format(accuracy))
def train(self, dataset_uri):
dataset = dataset_utils.load_dataset_of_image_files(dataset_uri)
(images, classes) = zip(*[(image, image_class)
for (image, image_class) in dataset])
X = self._prepare_X(images)
y = classes
self._clf.fit(X, y)
def evaluate(self, dataset_uri):
dataset = dataset_utils.load_dataset_of_image_files(dataset_uri)
(images, classes) = zip(*[(image, image_class) for (image, image_class) in dataset])
X = self._prepare_X(images)
y = classes
preds = self._clf.predict(X)
accuracy = sum(y == preds) / len(y)
return accuracy
def train(self, dataset_uri):
dataset = dataset_utils.load_dataset_of_image_files(dataset_uri)
(images, classes) = zip(*[(image, image_class) for (image, image_class) in dataset])
X = self._prepare_X(images)
y = classes
self._clf.fit(X, y)
# Compute train accuracy
preds = self._clf.predict(X)
accuracy = sum(y == preds) / len(y)
logger.log('Train accuracy: {}'.format(accuracy))
def evaluate(self, dataset_uri):
dataset = dataset_utils.load_dataset_of_image_files(
dataset_uri, image_size=[48, 48])
(images, classes) = zip(*[(image, image_class)
for (image, image_class) in dataset])
images = np.asarray(images)
images = np.stack([images] * 3, axis=-1)
classes = np.asarray(classes)
with self._graph.as_default():
with self._sess.as_default():
(loss, accuracy) = self._model.evaluate(images, classes)
return accuracy
def evaluate(self, dataset_uri):
im_sz = self._knobs.get('image_size')
dataset = dataset_utils.load_dataset_of_image_files(dataset_uri, image_size=[im_sz, im_sz])
(images, classes) = zip(*[(image, image_class) for (image, image_class) in dataset])
images = np.asarray(images)
classes = np.asarray(classes)
with self._graph.as_default():
with self._sess.as_default():
(loss, accuracy) = self._model.evaluate(images, classes)
logger.log('Test loss: {}'.format(loss))
return accuracy
def train(self, dataset_uri):
ep = self._knobs.get('epochs')
bs = self._knobs.get('batch_size')
dataset = dataset_utils.load_dataset_of_image_files(
dataset_uri, image_size=[48, 48])
num_classes = dataset.classes
(images, classes) = zip(*[(image, image_class)
for (image, image_class) in dataset])
images = np.asarray(images)
images = np.stack([images] * 3, axis=-1)
classes = np.asarray(classes)
with self._graph.as_default():
self._model = self._build_model(num_classes)
with self._sess.as_default():
self._model.fit(images, classes, epochs=ep, batch_size=bs)
