def test_activity_regularizer_batch_independent(self):
inputs = layers.Input(shape=(10,))
x = layers.Dense(10, activation='relu', activity_regularizer='l2')(inputs)
outputs = layers.Dense(1, activation='sigmoid')(x)
model = Model(inputs, outputs)
optimizer = RMSPropOptimizer(learning_rate=0.001)
model.compile(
optimizer,
run_eagerly=testing_utils.should_run_eagerly())
loss_small_batch = model.test_on_batch(np.ones((10, 10), 'float32'))
loss_big_batch = model.test_on_batch(np.ones((20, 10), 'float32'))
self.assertAlmostEqual(loss_small_batch, loss_big_batch, places=4)
def test_loss_with_sample_weight_in_layer_call(self):
class MyLayer(layers.Layer):
def __init__(self):
super(MyLayer, self).__init__()
self.bias = testing_utils.Bias()
def call(self, inputs):
out = self.bias(inputs[0])
self.add_loss(MAE()(inputs[1], out, inputs[2]))
self.add_loss(
math_ops.reduce_mean(inputs[2] * mae(inputs[1], out)))
return out
inputs = Input(shape=(1, ))
targets = Input(shape=(1, ))
sw = Input(shape=(1, ))
outputs = MyLayer()([inputs, targets, sw])
model = Model([inputs, targets, sw], outputs)
model.predict([self.x, self.y, self.w])
model.compile(optimizer_v2.gradient_descent.SGD(0.05),
run_eagerly=testing_utils.should_run_eagerly(),
experimental_run_tf_function=testing_utils.
should_run_tf_function())
history = model.fit([self.x, self.y, self.w], batch_size=3, epochs=5)
self.assertAllClose(history.history['loss'], [2., 1.8, 1.6, 1.4, 1.2],
1e-3)
output = model.evaluate([self.x, self.y, self.w])
self.assertAlmostEqual(output, 1.0, 3)
output = model.test_on_batch([self.x, self.y, self.w])
self.assertAlmostEqual(output, 1.0, 3)
images_x2 = images[images_idx_x2] / 255.
images_fov = train_fov[idx]
result = train_y[idx]
logs = model.train_on_batch(x=[images_x1, images_x2, images_fov], y=result)
sum_logs.append(logs)
if batch % 200 == 0 and batch > 0:
# check model on the validation data
valid_idx = np.random.randint(0, len(test_x1), train_batch_size)
valid_images_idx_x1 = test_x1[valid_idx]
valid_images_idx_x2 = test_x2[valid_idx]
valid_images_x1 = images[valid_images_idx_x1] / 255.
valid_images_x2 = images[valid_images_idx_x2] / 255.
valid_images_fov = train_fov[valid_idx]
valid_result = test_y[valid_idx]
v_loss = model.test_on_batch(
x=[valid_images_x1, valid_images_x2, valid_images_fov],
y=valid_result)
avg_logs = np.average(sum_logs, axis=0)
sum_logs = []
print('%d [loss: %f]' % (batch, avg_logs[0]))
write_log(callback, train_names, avg_logs, batch)
write_log(callback, val_names, v_loss, batch)
if batch % 5000 == 0 and batch > 0:
save_models(model)
