def __getitem__(self, spec):
val_grad_op = tf.gradients(self.val, self.var)
sliceval_grad_op = tf.gradients(
array_ops._NewSliceHelper(self.val, spec), self.var)
slice1_op = array_ops._NewSliceHelper(val_grad_op, spec)
slice2_op = array_ops._NewSliceHelper(sliceval_grad_op, spec)
val_grad, sliceval_grad, slice1, slice2 = self.sess.run(
[val_grad_op, sliceval_grad_op, slice1_op, slice2_op])
np_val_grad = (2 * self.varnp)
np_sliceval_grad = np.zeros(self.var.get_shape())
np_sliceval_grad[spec] = np.array(val_grad[0])[spec]
# make sure np val grad is correct
self.test.assertAllEqual(np_val_grad, val_grad[0])
# make sure slice gradient is correct
self.test.assertAllEqual(np_sliceval_grad, sliceval_grad[0])
# make sure val grad and sliceval grad are the same in sliced area
self.test.assertAllEqual(slice1, slice2)
def __getitem__(self, spec):
val_grad_op = tf.gradients(self.val, self.var)
sliceval_grad_op = tf.gradients(
array_ops._NewSliceHelper(self.val, spec), self.var)
slice1_op = array_ops._NewSliceHelper(val_grad_op, spec)
slice2_op = array_ops._NewSliceHelper(sliceval_grad_op, spec)
val_grad, sliceval_grad, slice1, slice2 = self.sess.run(
[val_grad_op, sliceval_grad_op, slice1_op, slice2_op])
np_val_grad = (2 * self.varnp)
np_sliceval_grad = np.zeros(self.var.get_shape())
np_sliceval_grad[spec] = np.array(val_grad[0])[spec]
# make sure np val grad is correct
self.test.assertAllEqual(np_val_grad, val_grad[0])
# make sure slice gradient is correct
self.test.assertAllEqual(np_sliceval_grad, sliceval_grad[0])
# make sure val grad and sliceval grad are the same in sliced area
self.test.assertAllEqual(slice1, slice2)
def __getitem__(self, spec):
# TODO(aselle): When NewSliceHelper is installed, we can switch this back
# op = self.x[spec]
op = array_ops._NewSliceHelper(self.x, spec)
tensor = op.eval()
self.test.assertAllEqual(self.x_np[spec], tensor)
self.test.assertAllEqual(tensor.shape, op.get_shape())
return tensor
def __getitem__(self, spec):
# TODO(aselle): When NewSliceHelper is installed, we can switch this back
# op = self.x[spec]
op = array_ops._NewSliceHelper(self.x, spec)
tensor = op.eval()
self.test.assertAllEqual(self.x_np[spec], tensor)
self.test.assertAllEqual(tensor.shape, op.get_shape())
return tensor
def __getitem__(self, spec):
slice_var = array_ops._NewSliceHelper(self.var, spec)
slice_val = array_ops._NewSliceHelper(self.val, spec)
# compute analytic 2nd derivative
analytic_grad2 = 2 * slice_val
dy = tf.Variable(tf.ones(shape=slice_var.get_shape(), dtype=tf.int32))
assign = dy.assign(slice_var)
slice_val_grad, = tf.gradients(slice_val, self.var, grad_ys=dy)
slice_val_grad2, = tf.gradients(slice_val_grad, dy, grad_ys=self.var)
self.sess.run(assign)
slice_val_grad_evaled, slice_val_grad2_evaled = (
self.sess.run([slice_val_grad, slice_val_grad2]))
analytic_grad2_evaled = analytic_grad2.eval()
self.test.assertAllEqual(slice_val_grad2_evaled, analytic_grad2_evaled)
# compute analytic gradient for slice
np_val_grad = (2 * self.varnp * self.varnp)
np_sliceval_grad = np.zeros(self.var.get_shape())
np_sliceval_grad[spec] = np_val_grad[spec]
# verify gradient
self.test.assertAllEqual(slice_val_grad_evaled, np_sliceval_grad)
def __getitem__(self, spec):
slice_var = array_ops._NewSliceHelper(self.var, spec)
slice_val = array_ops._NewSliceHelper(self.val, spec)
# compute analytic 2nd derivative
analytic_grad2 = 2 * slice_val
dy = tf.Variable(tf.ones(shape=slice_var.get_shape(), dtype=tf.int32))
assign = dy.assign(slice_var)
slice_val_grad, = tf.gradients(slice_val, self.var, grad_ys=dy)
slice_val_grad2, = tf.gradients(slice_val_grad, dy, grad_ys=self.var)
self.sess.run(assign)
slice_val_grad_evaled, slice_val_grad2_evaled = (self.sess.run(
[slice_val_grad, slice_val_grad2]))
analytic_grad2_evaled = analytic_grad2.eval()
self.test.assertAllEqual(slice_val_grad2_evaled, analytic_grad2_evaled)
# compute analytic gradient for slice
np_val_grad = (2 * self.varnp * self.varnp)
np_sliceval_grad = np.zeros(self.var.get_shape())
np_sliceval_grad[spec] = np_val_grad[spec]
# verify gradient
self.test.assertAllEqual(slice_val_grad_evaled, np_sliceval_grad)
def __getitem__(self, x): return array_ops._NewSliceHelper(self.tensor, x)
def __getitem__(self, spec): # TODO(aselle): When NewSliceHelper is installed, we can switch this back # op = self.x[spec] op = array_ops._NewSliceHelper(self.x, spec) return op.get_shape()
def __getitem__(self, x): return array_ops._NewSliceHelper(self.tensor, x)
def __getitem__(self, spec): # TODO(aselle): When NewSliceHelper is installed, we can switch this back # op = self.x[spec] op = array_ops._NewSliceHelper(self.x, spec) return op.get_shape()
