def compare_binary_op(self, kk_func, z_layer, shape):
x = klayers.Input(shape=shape[0][1:])
y = klayers.Input(shape=shape[1][1:])
batch = shape[0][0]
kkresult = kk_func(x, y)
x_value = np.random.uniform(0, 1, shape[0])
y_value = np.random.uniform(0, 1, shape[1])
k_grad_y_pred = KK.get_session().run(KK.gradients(kkresult, [x, y]),
feed_dict={
x: x_value,
y: y_value
})
k_output = KK.get_session().run(kkresult,
feed_dict={
x: x_value,
y: y_value
})
inputs = [Input(s) for s in remove_batch(shape)]
model = Model(inputs, z_layer(inputs))
z_output = model.forward([x_value, y_value])
grad_output = np.array(z_output)
grad_output.fill(1.0)
z_grad_y_pred = model.backward(x_value, grad_output)
self.assert_allclose(z_output, k_output)
[
self.assert_allclose(z, k)
for (z, k) in zip(z_grad_y_pred, k_grad_y_pred)
]
def add(self, model):
from zoo.pipeline.api.autograd import Lambda
if (isinstance(model, Lambda)):
if not self.is_built():
if not model.input_shape:
raise Exception("You should specify inputShape for the first layer")
input_shapes = model.input_shape
else:
input_shapes = self.get_output_shape()
model = model.create(remove_batch(input_shapes))
self.value.add(model.value)
return self
def add(self, model):
from zoo.pipeline.api.autograd import Lambda
if (isinstance(model, Lambda)):
if not self.is_built():
if not model.input_shape:
raise Exception("You should specify inputShape for the first layer")
input_shapes = model.input_shape
else:
input_shapes = self.get_output_shape()
model = model.create(remove_batch(input_shapes))
self.value.add(model.value)
return self
def __call__(self, x=None):
"""
Some other modules point to current module
:param x: upstream module nodes. x is either a Node or list of Node.
:return: node containing current module
"""
x = to_list(x if x else [])
layer = self
if isinstance(self, Lambda):
input_shapes = [var.get_output_shape() for var in x]
layer = self.create(remove_batch(input_shapes))
return Variable.from_jvalue(
callBigDlFunc(self.bigdl_type, "connectInputs", layer, to_list(x)))
def __call__(self, x=None):
"""
Some other modules point to current module
:param x: upstream module nodes. x is either a Node or list of Node.
:return: node containing current module
"""
x = to_list(x if x else [])
layer = self
if isinstance(self, Lambda):
input_shapes = [var.get_output_shape() for var in x]
layer = self.create(remove_batch(input_shapes))
return Variable.from_jvalue(callBigDlFunc(self.bigdl_type,
"connectInputs",
layer,
to_list(x)))
def __call__(self, x=None):
"""
Some other modules point to current module
:param x: upstream module nodes. x is either a Node or list of Node.
:return: node containing current module
"""
x = to_list(x if x else [])
layer = self
if isinstance(self, Lambda):
input_shapes = [
ZooKerasLayer.of(node.element().value).get_output_shape()
for node in x
]
layer = self.create(remove_batch(input_shapes))
return Node.of(
callBigDlFunc(self.bigdl_type, "createNode", layer, to_list(x)))
def compare_binary_op(self, kk_func, z_layer, shape, rtol=1e-5, atol=1e-5):
x = klayers.Input(shape=shape[0][1:])
y = klayers.Input(shape=shape[1][1:])
batch = shape[0][0]
kkresult = kk_func(x, y)
x_value = np.random.uniform(0, 1, shape[0])
y_value = np.random.uniform(0, 1, shape[1])
k_grads = KK.get_session().run(KK.gradients(kkresult, [x, y]),
feed_dict={
x: x_value,
y: y_value
})
k_output = KK.get_session().run(kkresult,
feed_dict={
x: x_value,
y: y_value
})
inputs = [Input(s) for s in remove_batch(shape)]
model = Model(inputs, z_layer(inputs))
z_output = model.forward([x_value, y_value])
grad_output = np.array(z_output)
grad_output.fill(1.0)
z_grads = model.backward([x_value, y_value], grad_output)
# Check if the model can be forward/backward multiple times or not
z_output2 = model.forward([x_value, y_value])
z_grads2 = model.backward([x_value, y_value], grad_output)
self.assert_allclose(z_output, z_output2, rtol, atol)
[
self.assert_allclose(z, k, rtol, atol)
for (z, k) in zip(z_grads, z_grads2)
]
self.assert_allclose(z_output, k_output, rtol, atol)
[
self.assert_allclose(z, k, rtol, atol)
for (z, k) in zip(z_grads, k_grads)
]
def test_remove_batch(self):
from zoo.pipeline.api.utils import remove_batch
assert remove_batch([2, 3, 4]) == [3, 4]
assert remove_batch([[2, 6, 7], [2, 3, 4]]) == [[6, 7], [3, 4]]
