def fit(self,
x,
y,
c=None,
lr=None,
batch_size=None,
epoch=None,
tol=None,
optimizer=None,
animation_params=None):
if c is None:
c = self._params["c"]
if lr is None:
lr = self._params["lr"]
if batch_size is None:
batch_size = self._params["batch_size"]
if epoch is None:
epoch = self._params["epoch"]
if tol is None:
tol = self._params["tol"]
if optimizer is None:
optimizer = self._params["optimizer"]
*animation_properties, animation_params = self._get_animation_params(
animation_params)
x, y = np.atleast_2d(x), np.asarray(y)
y_2d = y[..., None]
self._w = tf.Variable(np.zeros([x.shape[1], 1]),
dtype=tf.float32,
name="w")
self._b = tf.Variable(0., dtype=tf.float32, name="b")
self._tfx = tf.placeholder(tf.float32, [None, x.shape[1]])
self._tfy = tf.placeholder(tf.float32, [None, 1])
self._y_pred_raw = tf.matmul(self._tfx, self._w) + self._b
self._y_pred = tf.sign(self._y_pred_raw)
loss = tf.reduce_sum(tf.nn.relu(
1 - self._tfy * self._y_pred_raw)) + c * tf.nn.l2_loss(self._w)
train_step = TFOptFac().get_optimizer_by_name(optimizer,
lr).minimize(loss)
self._sess.run(tf.global_variables_initializer())
bar = ProgressBar(max_value=epoch, name="TFLinearSVM")
ims = []
train_repeat = self._get_train_repeat(x, batch_size)
for i in range(epoch):
l = self._batch_training(x, y_2d, batch_size, train_repeat, loss,
train_step)
if l < tol:
bar.terminate()
break
self._handle_animation(i, x, y, ims, animation_params,
*animation_properties)
bar.update()
self._handle_mp4(ims, animation_properties)
def _prepare(self, sample_weight, **kwargs):
lr = kwargs.get("lr", self._params["lr"])
self._w = tf.Variable(np.zeros([len(self._x), 1]), dtype=tf.float32, name="w")
self._b = tf.Variable(.0, dtype=tf.float32, name="b")
self._tfx = tf.placeholder(tf.float32, [None, None])
self._tfy = tf.placeholder(tf.float32, [None])
self._y_pred_raw = tf.transpose(tf.matmul(self._tfx, self._w) + self._b)
self._y_pred = tf.sign(self._y_pred_raw)
self._loss = tf.reduce_sum(
tf.maximum(1 - self._tfy * self._y_pred_raw, 0) * sample_weight
) + 0.5 * tf.matmul(
# self._w, tf.matmul(self._tfx, self._w, transpose_b=True)
(self._y_pred_raw - self._b), self._w
)[0][0]
self._train_step = TFOptFac().get_optimizer_by_name(
self._optimizer, lr
).minimize(self._loss)
self._sess.run(tf.global_variables_initializer())