d[x] = tangent.pop_stack(d[stack], d[op_id])
@tangent_(tangent.unbroadcast)
def tunbroadcast(z, x, y):
d[z] = tangent.unbroadcast(d[x], d[y])
@tangent_(tangent.Stack)
def tstack(z):
d[z] = tangent.Stack()
@tangent_(tangent.astype)
def tastype(z, x, y):
d[z] = tangent.astype(d[x], d[y])
@tangent_(tangent.unreduce)
def tunreduce(z, array, shape, axis, keepdims):
d[z] = tangent.unreduce(d[array], d[shape], axis, keepdims)
# Until we've written the adjoints of all functions we want to support,
# we will throw an explicit "no tangent found" error for those we have not
# finished. UNIMPLEMENTED will contain the list of all of these unimplemented
# tangent functions
UNIMPLEMENTED_TANGENTS = grads.get_module_functions(
(numpy, numpy.fft, numpy.linalg, numpy.random, math)) - set(tangents)
@tangent_(tf.nn.avg_pool)
def ttfavg_pool(y, x, sizes, strides, padding):
raise tangent.ForwardNotImplementedError(tf.nn.avg_pool)
@tangent_(tf.nn.max_pool)
def ttfmax_pool(y, x, sizes, strides, padding):
raise tangent.ForwardNotImplementedError(tf.nn.max_pool)
@tangent_(tf.shape)
def tshape(y, x):
d[y] = tf.shape(d[x])
#
# Blacklist unimplemented Eager grads
#
grads.UNIMPLEMENTED_ADJOINTS.update(
grads.get_module_functions((tf, tf.distributions, tf.image, tf.layers,
tf.linalg, tf.losses, tf.nn)) -
set(grads.adjoints))
tangents.UNIMPLEMENTED_TANGENTS.update(
grads.get_module_functions((tf, tf.distributions, tf.image, tf.layers,
tf.linalg, tf.losses, tf.nn)) -
set(tangents.tangents))
@tangent_(tf.nn.avg_pool)
def ttfavg_pool(y, x, sizes, strides, padding):
raise tangent.ForwardNotImplementedError(tf.nn.avg_pool)
@tangent_(tf.nn.max_pool)
def ttfmax_pool(y, x, sizes, strides, padding):
raise tangent.ForwardNotImplementedError(tf.nn.max_pool)
@tangent_(tf.shape)
def tshape(y, x):
d[y] = tf.shape(d[x])
#
# Blacklist unimplemented Eager grads
#
grads.UNIMPLEMENTED_ADJOINTS.update(
grads.get_module_functions((tf, tf.distributions, tf.image, tf.layers,
tf.linalg, tf.losses,
tf.nn)) - set(grads.adjoints))
tangents.UNIMPLEMENTED_TANGENTS.update(
grads.get_module_functions((tf, tf.distributions, tf.image, tf.layers,
tf.linalg, tf.losses,
tf.nn)) - set(tangents.tangents))
