def prepGeometricTensor(state, vrs):
nparams = prep_variables(vrs)
phi_r = tf.math.real(state)
phi_c = tf.math.imag(state)
jac_r = jacobian(phi_r, vrs)
jac_c = jacobian(phi_c, vrs)
if len(vrs) == 1:
jac = tf.reshape(tf.complex(jac_r, jac_c), (state.shape + [nparams]))
jac = tf.split(jac, nparams, axis=-1)
jac = [tf.reshape(v, state.shape) for v in jac]
else:
jac = [tf.complex(jac_r[i], jac_c[i]) for i in range(nparams)]
return jac, nparams
def test_jacobian_fixed_shape(self):
x = random_ops.random_uniform([2, 2])
y = math_ops.matmul(x, x, transpose_a=True)
jacobian_pfor = gradients.jacobian(y, x, use_pfor=True)
jacobian_while = gradients.jacobian(y, x, use_pfor=False)
answer = ops.convert_to_tensor([[
gradient_ops.gradients(y[0][0], x)[0],
gradient_ops.gradients(y[0][1], x)[0]
], [
gradient_ops.gradients(y[1][0], x)[0],
gradient_ops.gradients(y[1][1], x)[0]
]])
self.run_and_assert_equal(answer, jacobian_pfor)
self.run_and_assert_equal(answer, jacobian_while)
def create_lstm_hessian(batch_size, state_size, steps):
_, output = lstm_model_fn(batch_size, state_size, steps)
weights = variables.trainable_variables()
pfor_jacobians = gradients.jacobian(output, weights, use_pfor=True)
pfor_hessians = [
gradients.jacobian(x, weights, use_pfor=True) for x in pfor_jacobians
]
# TODO(agarwal): using two nested while_loop doesn't seem to work here.
# Hence we use pfor_jacobians for computing while_hessians.
while_jacobians = pfor_jacobians
while_hessians = [
gradients.jacobian(x, weights, use_pfor=False) for x in while_jacobians
]
return pfor_hessians, while_hessians
def create_lstm_hessian(batch_size, state_size, steps):
_, output = lstm_model_fn(batch_size, state_size, steps)
weights = variables.trainable_variables()
pfor_jacobians = gradients.jacobian(output, weights, use_pfor=True)
pfor_hessians = [
gradients.jacobian(x, weights, use_pfor=True) for x in pfor_jacobians
]
# TODO(agarwal): using two nested while_loop doesn't seem to work here.
# Hence we use pfor_jacobians for computing while_hessians.
while_jacobians = pfor_jacobians
while_hessians = [
gradients.jacobian(x, weights, use_pfor=False) for x in while_jacobians
]
return pfor_hessians, while_hessians
def _build_hessian_op_grid(self, y, wrt_x1, wrt_x2):
# DEBUG: self.monolith_hessian = jacobian(tf.gradients(y, wrt_x1)[0], wrt_x2, use_pfor=False)
# DEBUG: self.monolith_hessian = tf.reshape(self.monolith_hessian, shape=(self._full_height, self._full_width))
# Compute the full gradient wrt x1. We will slice this later when computing the hessian in a blocky manner.
full_gradient = tf.gradients(y, wrt_x1)[0]
full_gradient = tf.reshape(full_gradient, shape=(self._full_height, ))
# Redefining for readability
grid_height = self._grid_height
block_height = self._block_height
full_height = self._full_height
# Build a grid of Tensorflow operations - each operation computes a chunk of the Hessian.
# The grid is actually just a list of chunks of shape [block_height, n_elements(x2)].
# When assembled, the hessian chunk will have shape [n_elements(x1), n_elements(x2)].
op_grid = [None] * grid_height
for i_block in range(grid_height):
# Parameters for the j-axis of the hessian.
i_start_ix = i_block * block_height
i_end_ix = min(full_height, (i_block + 1) * block_height)
# Add to output op grid.
grad_chunk = full_gradient[i_start_ix:i_end_ix]
# TODO: it is not clear whether to use pfor or not (since it is still experimental - maybe we can test).
# See https://github.com/tensorflow/tensorflow/issues/675#issuecomment-404665051
hess_chunk = jacobian(grad_chunk, wrt_x2, use_pfor=False)
hess_chunk = tf.reshape(hess_chunk,
shape=(grad_chunk.shape[0],
self._full_width))
op_grid[i_block] = hess_chunk
return op_grid
def caljacob(self, z):
is_training = False
jacob = self.sess.run(jacobian(self.x_hat, self.z),
feed_dict={
self.z: z,
self.is_training: is_training
})
return jacob
def inverse(self, z):
q, p = extract_q_p(x)
q_prime = self._f.inverse(q)
df = tf_gradients_ops.jacobian(self._f(q_prime),
q_prime,
use_pfor=True)
return join_q_p(q_prime,
tf.tensordot(df, p, [[4, 5, 6, 7], [0, 1, 2, 3]]))
def run_pyket(args):
hilbert_state_shape = (args.input_size, 1)
padding = ((0, args.kernel_size - 1), )
inputs = Input(shape=hilbert_state_shape, dtype='int8')
x = ToComplex128()(inputs)
for i in range(args.depth):
x = PeriodicPadding(padding)(x)
x = ComplexConv1D(args.width,
args.kernel_size,
use_bias=False,
dtype=tf.complex128)(x)
x = Activation(lncosh)(x)
x = Flatten()(x)
predictions = Lambda(lambda y: tf.reduce_sum(y, axis=1, keepdims=True))(x)
model = Model(inputs=inputs, outputs=predictions)
if args.fast_jacobian:
predictions_jacobian = lambda x: get_predictions_jacobian(keras_model=
model)
else:
predictions_jacobian = lambda x: gradients.jacobian(
tf.real(model.output), x, use_pfor=not args.no_pfor)
if args.use_stochastic_reconfiguration:
optimizer = ComplexValuesStochasticReconfiguration(
model,
predictions_jacobian,
lr=args.learning_rate,
diag_shift=10.0,
iterative_solver=args.use_iterative,
use_cholesky=args.use_cholesky,
iterative_solver_max_iterations=None)
model.compile(optimizer=optimizer,
loss=loss_for_energy_minimization,
metrics=optimizer.metrics)
else:
optimizer = SGD(lr=args.learning_rate)
model.compile(optimizer=optimizer, loss=loss_for_energy_minimization)
model.summary()
operator = Heisenberg(hilbert_state_shape=hilbert_state_shape, pbc=True)
sampler = MetropolisHastingsHamiltonian(
model,
args.batch_size,
operator,
num_of_chains=args.pyket_num_of_chains,
unused_sampels=numpy.prod(hilbert_state_shape))
variational_monte_carlo = VariationalMonteCarlo(model, operator, sampler)
model.fit_generator(variational_monte_carlo.to_generator(),
steps_per_epoch=5,
epochs=1,
max_queue_size=0,
workers=0)
start_time = time.time()
model.fit_generator(variational_monte_carlo.to_generator(),
steps_per_epoch=args.num_of_iterations,
epochs=1,
max_queue_size=0,
workers=0)
end_time = time.time()
return end_time - start_time
def call(self, x):
q, p = extract_q_p(x)
q_prime = self._f(q)
# Df(q)^{-1} = D(f^{-1}( q_prime ))
df_inverse = tf_gradients_ops.jacobian(self._f.inverse(q_prime),
q_prime,
use_pfor=True)
return join_q_p(
q_prime, tf.tensordot(df_inverse, p, [[4, 5, 6, 7], [0, 1, 2, 3]]))
def test_jacobian_unknown_shape(self):
with self.test_session() as sess:
x = array_ops.placeholder(dtypes.float32, shape=[None, None])
y = math_ops.matmul(x, x, transpose_a=True)
jacobian_pfor = gradients.jacobian(y, x, use_pfor=True)
jacobian_while = gradients.jacobian(y, x, use_pfor=False)
answer = ops.convert_to_tensor([[
gradient_ops.gradients(y[0][0], x)[0],
gradient_ops.gradients(y[0][1], x)[0]
], [
gradient_ops.gradients(y[1][0], x)[0],
gradient_ops.gradients(y[1][1], x)[0]
]])
ans, pfor_value, while_value = sess.run(
[answer, jacobian_pfor, jacobian_while],
feed_dict={x: [[1, 2], [3, 4]]})
self.assertAllClose(ans, pfor_value)
self.assertAllClose(ans, while_value)
def test_jacobian_scan_shape(self):
# Shape x: [3, 4]
x = random_ops.random_uniform([3, 4])
elems = random_ops.random_uniform([6])
# Shape y: [6, 3, 4]
y = functional_ops.scan(lambda a, e: a + e, elems, initializer=x)
jacobian = gradients.jacobian(y, x)
expected_shape = [6, 3, 4, 3, 4]
self.assertAllEqual(expected_shape, jacobian.shape.as_list())
def test_jacobian_scan_shape(self):
# Shape x: [3, 4]
x = random_ops.random_uniform([3, 4])
elems = random_ops.random_uniform([6])
# Shape y: [6, 3, 4]
y = functional_ops.scan(lambda a, e: a + e, elems, initializer=x)
jacobian = gradients.jacobian(y, x)
expected_shape = [6, 3, 4, 3, 4]
self.assertAllEqual(expected_shape, jacobian.shape.as_list())
def mixed_partials(self, conf):
# optimized version to speed things up a little bit.
grads = self.gradients(conf)
reverse_shaped = jacobian(grads, self.params, use_pfor=False)
properly_shaped = []
for p in reverse_shaped:
if len(p.get_shape()) == 3:
properly_shaped.append(tf.transpose(p, perm=(2, 0, 1)))
if len(p.get_shape()) == 4:
# properly_shaped.append(tf.reshape(fixed, [-1, fixed.shape[2], fixed.shape[3]]))
properly_shaped.append(tf.transpose(p, perm=(2, 3, 0, 1)))
return properly_shaped
def test_jacobian_while_loop_shape(self):
# Shape x: [3, 4]
x = random_ops.random_uniform([3, 4])
_, y = tf_control_flow_ops.while_loop(lambda i, a: i > 5.,
lambda i, a: (i + 1, a + i),
(constant_op.constant(0.), x))
# Shape y: [2, 3]
y = y[:2, :3]
jacobian = gradients.jacobian(y, x)
expected_shape = [2, 3, 3, 4]
self.assertAllEqual(expected_shape, jacobian.shape.as_list())
def test_jacobian_while_loop_shape(self):
# Shape x: [3, 4]
x = random_ops.random_uniform([3, 4])
_, y = tf_control_flow_ops.while_loop(lambda i, a: i > 5., lambda i, a:
(i + 1, a + i),
(constant_op.constant(0.), x))
# Shape y: [2, 3]
y = y[:2, :3]
jacobian = gradients.jacobian(y, x)
expected_shape = [2, 3, 3, 4]
self.assertAllEqual(expected_shape, jacobian.shape.as_list())
def test_sorting():
# convert to TF tensors
dtype = tf.float64
tf_matrices = bitonic_matrices(8)
for max_fn in [softmax, smoothmax, softmax_smooth]:
test = to_tf(np.random.randint(-200, 200, 8), dtype=dtype)
tf_output = tf.reshape(diff_sort(tf_matrices, test), (-1,))
tf_ranks = diff_argsort(tf_matrices, test)
tf_argsort = diff_argsort(tf_matrices, test, transpose=True)
tf_grads = tf.squeeze(jacobian(tf_output, test))
# compute output and gradient
with tf.Session() as s:
s.run((tf_output, tf_grads, tf_ranks, tf_argsort))
def mixed_partials(self, conf):
"""
Returns list of tensors of mixed partial derivatives evaluated at the input geometry.
Parameters
----------
conf: tf.placeholder
An N x 3 configuration placeholder
Returns
-------
tf.Tensor of size len(self.params)
Returns an unflattened list of mixed partial derivatives [(p_shape), N, 3]
matching each parameter in get_params()
"""
# (ytz): Note for implementation purposes, the order of differentiation
# actually matters. The jacobian system in tensorflow expects a fixed size
# tensor for the outputs, while permitting a variable list of tensors for
# inputs. This means that we should naturally use the coordinate derivatives
# as they all have a fixed N x 3 structure, where as the input parameters
# can take on a variadic list of tensors of varying sizes.
# optimized version to speed things up a little bit.
grads = self.gradients(conf)
# taken from tf src gradients_impl.py _IndexedSlicesToTensor
if isinstance(grads, tf.IndexedSlices):
grads = tf.unsorted_segment_sum(grads.values, grads.indices,
grads.dense_shape[0])
reverse_shaped = jacobian(grads, self.params, use_pfor=False)
if isinstance(reverse_shaped, tf.Tensor):
# shove to a list
reverse_shaped = [reverse_shaped]
properly_shaped = []
for p in reverse_shaped:
if len(p.get_shape()) == 2:
properly_shaped.append(p) # already ready to go
elif len(p.get_shape()) == 3:
properly_shaped.append(tf.transpose(p, perm=(2, 0, 1)))
elif len(p.get_shape()) == 4:
# properly_shaped.append(tf.reshape(fixed, [-1, fixed.shape[2], fixed.shape[3]]))
properly_shaped.append(tf.transpose(p, perm=(2, 3, 0, 1)))
else:
# should be easy to support, just add perm=(2,3,...,0,1)
raise NotImplementedError("Shapes > 4 not supported")
return properly_shaped
def jacobians(ys, xs, parallel_iterations=None):
"""Compute the jacobians of `ys` with respect to `xs`.
Args:
ys: tf.Tensor.
xs: tf.Tensor. The variables wrt to compute the Jacobian.
parallel_iterations: The number of iterations to be done in paralel. Used to
trade-off memory consumption for speed: if None, the Jacobian
computation is done in parallel, but requires most memory.
Returns:
a tf.Tensor of Jacobians.
"""
return pfor_gradients.jacobian(ys,
xs,
use_pfor=True,
parallel_iterations=parallel_iterations)
def create_fc_per_eg_jacobians(batch_size, activation_size, num_layers):
model = FullyConnectedModel(activation_size=activation_size,
num_layers=num_layers)
inp = random_ops.random_normal([batch_size, activation_size])
output = model(inp)
jacobians = gradients.jacobian(output, variables.trainable_variables())
def loop_fn(i, use_pfor):
inp_i = array_ops.expand_dims(array_ops.gather(inp, i), 0)
output = array_ops.reshape(model(inp_i), [-1])
return gradients.jacobian(
output, variables.trainable_variables(), use_pfor=use_pfor)
per_eg_jacobians_pfor = control_flow_ops.pfor(
functools.partial(loop_fn, use_pfor=True),
batch_size)
per_eg_jacobians_while = control_flow_ops.for_loop(
functools.partial(loop_fn, use_pfor=False),
[dtypes.float32] * len(variables.trainable_variables()), batch_size)
return jacobians, per_eg_jacobians_pfor, per_eg_jacobians_while
def __init__(self, model_class, dataset, params):
"""
Creates necessary ops for deepfool in the tensorflow graph
Args:
model_class: The class of the model to construct.
Expects subclass of BasicModel
dataset: The dataset to use.
Only necessary for shape and number of classes
params: Additional parameters to pass to the model init
"""
self.image = tf.placeholder(dtype=tf.float32, shape=dataset.shape)
self.model = model_class(tf.expand_dims(self.image, axis=0),
trainable=False,
num_classes=dataset.num_classes,
**params)
self.logits = self.model.logits[0]
self.num_classes = self.logits.shape.as_list()[0]
self.logits_grad = jacobian(self.logits, self.image)
def create_fc_per_eg_jacobians(batch_size, activation_size, num_layers):
model = FullyConnectedModel(activation_size=activation_size,
num_layers=num_layers)
inp = random_ops.random_normal([batch_size, activation_size])
output = model(inp)
jacobians = gradients.jacobian(output, variables.trainable_variables())
def loop_fn(i, use_pfor):
inp_i = array_ops.expand_dims(array_ops.gather(inp, i), 0)
output = array_ops.reshape(model(inp_i), [-1])
return gradients.jacobian(output,
variables.trainable_variables(),
use_pfor=use_pfor)
per_eg_jacobians_pfor = control_flow_ops.pfor(
functools.partial(loop_fn, use_pfor=True), batch_size)
per_eg_jacobians_while = control_flow_ops.for_loop(
functools.partial(loop_fn, use_pfor=False),
[dtypes.float32] * len(variables.trainable_variables()), batch_size)
return jacobians, per_eg_jacobians_pfor, per_eg_jacobians_while
def list_jacobian(outputs, inputs):
"""
Parameters
----------
outputs: tf.Tensor
inputs: list of tf.Tensor
Returns
-------
list of jacobians [
tf.Tensor(p0_d0,p0_d1,...,N,3),
tf.Tensor(p1_d0,p1_d2,...,N,3),
...
]
"""
# This is a slightly more advanced version of tensorflow's jacobian system that allows
# for sparse gradients as well as automatically reshaping the results if outputs is a list.
# taken from tf src gradients_impl.py _IndexedSlicesToTensor
outputs = densify(outputs)
output_dims = list(range(len(outputs.get_shape().as_list()))) # [0,1]
n_out_dims = len(output_dims)
# if isinstance(reverse_shaped, tf.Tensor):
# shove to a list
# reverse_shaped = [reverse_shaped]
result = []
for inp, jac in zip(inputs, jacobian(outputs, inputs, use_pfor=False)):
input_dims = list(range(len(inp.get_shape().as_list()))) # [0,1]
perm = [(idx + n_out_dims) for idx in input_dims
] + output_dims # generate permutation indices
result.append(tf.transpose(jac, perm=perm))
return result
def test_equal_to_builtin_jacobian(model_builder, batch_size):
with DEFAULT_TF_GRAPH.as_default():
keras_model = model_builder()
keras_model.summary()
gradient_per_example_t = gradient_per_example(
tf.real(keras_model.output), keras_model)
tensorflow_jacobian_t = gradients.jacobian(tf.real(keras_model.output),
keras_model.weights,
use_pfor=False)
print(gradient_per_example_t)
print(tensorflow_jacobian_t)
gradient_per_example_func = K.function(inputs=[keras_model.input],
outputs=gradient_per_example_t)
tensorflow_jacobian_func = K.function(inputs=[keras_model.input],
outputs=tensorflow_jacobian_t)
size = (batch_size, ) + K.int_shape(keras_model.input)[1:]
batch = np.random.rand(*size)
gradient_per_example_vals = gradient_per_example_func([batch])
tensorflow_jacobian_vals = tensorflow_jacobian_func([batch])
allclose = [
np.allclose(a, b, rtol=1e-3) for a, b in zip(
gradient_per_example_vals, tensorflow_jacobian_vals)
]
assert np.all(allclose)
def compute_task_jacobian(self, policy_loss, policy_loss_quad, tangents):
# compute hvp
params1 = self.warmup_policy1.parameters()
params2 = self.warmup_policy2.parameters()
params3 = self.warmup_policy3.parameters()
self.op_task_hvp_Ax = nn.utils.quadgrad_vec_prod(policy_loss_quad,
params1,
params2,
params3,
tangents,
AAx=False)
task_hvp = nn.utils.quadgrad_vec_prod(policy_loss_quad,
params1,
params2,
params3,
tangents,
AAx=True)
if self.meanAAx:
nparam = nn.utils.n_parameters_int(
self.warmup_policy.parameters()).astype(np.float32)
print("meanAAx nparam:", nparam)
print(type(nparam))
task_hvp = task_hvp / nparam
print("task_hvp:", task_hvp)
print("task_hvp_Ax:", self.op_task_hvp_Ax)
self.op_quad_hvp = nn.utils.hessian_vec_prod(
self.quad_loss, self.warmup_policy.parameters(), tangents)
self.op_hessian_hvp = nn.utils.hessian_vec_prod(
policy_loss, self.warmup_policy.parameters(), tangents)
self.op_hvp = nn.utils.hessian_vec_prod(
self.mean_kl, self.warmup_policy.parameters(), tangents)
# compute jacobian
task_params = self.task.parameters()
policy_params = self.warmup_policy.parameters()
policy_gradient_flat = nn.utils.parameters_to_vector(
tf.gradients(policy_loss, policy_params))
self.pg_flat = policy_gradient_flat
self.p_flat = nn.utils.parameters_to_vector(
self.warmup_policy.parameters())
print("pg_flat:", self.pg_flat)
print("p_flat:", self.p_flat)
task_jacobian = jacobian(policy_gradient_flat,
task_params,
use_pfor=False)
print(task_jacobian.shape)
task_jacobian = tf.reshape(task_jacobian, (-1, self.task.n_dim))
if self.AAx:
ATb = []
for i in range(self.task.n_dim):
ATb_i = nn.utils.quadgrad_vec_prod(policy_loss_quad,
params1,
params2,
params3,
task_jacobian[:, i],
AAx=False)
if self.meanAAx:
ATb_i = ATb_i / nn.utils.n_parameters_int(
self.warmup_policy.parameters()).astype(np.float32)
ATb.append(ATb_i)
self.op_ATb = tf.stack(ATb, axis=0) #n_dim x |theta|
print(self.op_ATb.shape)
#exit(0)
#############################################################
jacobian_op = []
task_gradients = nn.utils.parameters_to_vector(
tf.gradients(policy_loss, task_params))
self.task_gradients = task_gradients
for i in range(self.task.n_dim):
b = nn.utils.parameters_to_vector(
tf.gradients(task_gradients[i], policy_params))
jacobian_op.append(b)
self.jacobian_op = tf.stack(jacobian_op, axis=0)
print("jacobian_op:", self.jacobian_op)
print("task_gradients:", self.task_gradients)
print("task_jacobian:", task_jacobian)
#############################################################
return task_jacobian, task_hvp
def test_jacobian_parallel_iterations(self):
x = constant_op.constant([[1., 2], [3, 4]])
y = math_ops.matmul(x, x)
self.assertAllClose(gradients.jacobian(y, x, parallel_iterations=2),
gradients.jacobian(y, x, parallel_iterations=3))
def jacobian(x):
return gradients.jacobian(tensorflow.math.real(x),
params,
use_pfor=self.use_pfor)
def test_indexed_slice(self):
inp = random_ops.random_uniform([3, 2])
output = nn.embedding_lookup(inp, [0, 2])
pfor_jacobian = gradients.jacobian(output, inp, use_pfor=True)
while_jacobian = gradients.jacobian(output, inp, use_pfor=False)
self.run_and_assert_equal(while_jacobian, pfor_jacobian)
def test_jacobian_parallel_iterations(self):
x = constant_op.constant([[1., 2], [3, 4]])
y = math_ops.matmul(x, x)
self.assertAllClose(gradients.jacobian(y, x, parallel_iterations=2),
gradients.jacobian(y, x, parallel_iterations=3))
def CTC_Loss():
# ctc_loss v1에서는 sparse matrix가 들어가기 때문에, gt(label)에 0번 character가 포함되어 있으면, 0번에 대한 loss를 계산못한다.
# v2에서도 sparse를 넣어주면 같은 결과가 나온다.
# 이는 0번을 padding으로 인식하는 문제가 있기 때문이다.
# 따라서, 0번에는 의미 있는 charcter를 부여하면 안된다.
# v2에서 label에 sparse가 아닌, dense를 넣어주어야 한다.
batch_size = 2
output_T = 5
target_T = 3 # target의 길이. Model이 만들어 내는 out_T는 target보다 길다.
num_class = 4 # 0, 1, 2는 character이고, 마지막 3은 blank이다.
x = np.arange(40).reshape(batch_size, output_T,
num_class).astype(np.float32)
x = np.random.randn(batch_size, output_T, num_class)
x = np.array([[[0.74273746, 0.07847633, -0.89669566, 0.87111101],
[0.35377891, 0.87161664, 0.45004634, -0.01664156],
[-0.4019564, 0.59862392, -0.90470981, -0.16236736],
[0.28194173, 0.82136263, 0.06700599, -0.43223688],
[0.1487472, 1.04652007, -0.51399114, -0.4759599]],
[[-0.53616811, -2.025543, -0.06641838, -1.88901458],
[-0.75484499, 0.24393693, -0.08489008, -1.79244747],
[0.36912486, 0.93965647, 0.42183299, 0.89334628],
[-0.6257366, -2.25099419, -0.59857886, 0.35591563],
[0.72191422, 0.37786281, 1.70582983,
0.90937337]]]).astype(np.float32)
xx = tf.convert_to_tensor(x)
xx = tf.Variable(xx)
logits = tf.transpose(xx, [1, 0, 2])
yy = np.random.randint(0, num_class - 1,
size=(batch_size,
target_T)) # low=0, high=3 ==> 0,1,2
yy = np.array([[1, 2, 2], [1, 0, 1]]).astype(np.int32)
#yy = np.array([[1, 2, 2,0,0,0],[1,0,2,0,0,0]]).astype(np.int32) # 끝에 붙은 0은 pad로 간주한다. 중간에 있는 0은 character로 간주
zero = tf.constant(0, dtype=tf.int32)
where = tf.not_equal(yy, zero)
indices = tf.where(where)
values = tf.gather_nd(yy, indices)
targets = tf.SparseTensor(indices, values, yy.shape)
# preprocess_collapse_repeated=False ---> label은 반복되는 character가 있을 수 있으니, 당연히 False
# ctc_merge_repeated=False ---> 모델이 예측한 반복된 character를 merge하지 않는다. 이것은 ctc loss의 취지와 다르다.
loss0 = tf.nn.ctc_loss(labels=targets,
inputs=logits,
sequence_length=[output_T] * batch_size,
ctc_merge_repeated=False)
# 이 loss0는 의미 없음.
loss1 = tf.nn.ctc_loss(labels=targets,
inputs=logits,
sequence_length=[output_T] * batch_size)
loss2 = tf.nn.ctc_loss_v2(labels=yy,
logits=logits,
label_length=[target_T] * batch_size,
logit_length=[output_T] * batch_size,
logits_time_major=True,
blank_index=num_class - 1)
# lables에 sparse tensor를 넣으면, v1과 결과가 같다.
loss3 = tf.nn.ctc_loss_v2(labels=targets,
logits=logits,
label_length=[3, 3],
logit_length=[output_T] * batch_size,
logits_time_major=True,
blank_index=num_class - 1)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1)
gradient = optimizer.compute_gradients(loss1)
prob = tf.nn.softmax(xx, axis=-1)
# jacobian을 이용해서 logits에 대한 softmax값의 미분을 구한다.
a = xx[0, 1]
b = tf.nn.softmax(a)
grad = jacobian(b, a)
# logit에 대한 미분을 softmax에 대한 미분으로 변환하기 위해 grad의 inverse를 곱한다.
# grad의 역행렬이 존재하지 않는다.
sess = tf.Session()
sess.run(tf.global_variables_initializer())
l0 = sess.run(loss0)
l1 = sess.run(loss1)
l2 = sess.run(loss2)
l3 = sess.run(loss3)
print('loss: ', l0, l1, l2, l3)
g = sess.run(gradient[0][0])
p = sess.run(prob)
gg = sess.run(grad)
def model_l1_l2_func(nm_set_points, n_in, nn_1, opt_obj, **kwargs_vals):
hess_approx_flag = False
neurons_cnt_x1, initializer = kwargs_vals['neurons_cnt'], kwargs_vals[
'initializer']
wb_sizes_classif, wb_shapes = kwargs_vals['sizes'], kwargs_vals['shapes']
x_trained = kwargs_vals['xtr']
y_trained = kwargs_vals['ytr']
sess_values = kwargs_vals['sess']
neurons_cnt = kwargs_vals['neurons_cnt']
# pcsv = np.genfromtxt('results_paramsparse.csv', delimiter='\t')
# p = tf.Variable(pcsv, dtype=tf.float64)
p = tf.Variable(initializer([neurons_cnt], dtype=tf.float64))
p_store = tf.Variable(tf.zeros([neurons_cnt_x1], dtype=tf.float64))
save_params_p = tf.assign(p_store, p)
restore_params_p = tf.assign(p, p_store)
I_mat = tf.eye(neurons_cnt_x1, dtype=tf.float64)
shaped_new = np.int(wb_sizes_classif[0]) + np.int(wb_sizes_classif[1])
# l2_norm_val, all_reg0 = func_structured_l2pen(p, wb_sizes_classif, wb_shapes)
lambda_param = kwargs_vals['lambda_param']
lambda_param2 = kwargs_vals['lambda_param2']
# all_reg_0 = tf.reduce_sum(tf.abs(lasso_p))
# l2 structured norm loss function
y_hat_model, y_hat_model_flat_x, y_labeled, x_in, r, l2_norm_val, all_reg0, l2_p, lassop = func_mse_l2(
n_in, p, nn_1, kwargs_vals)
r1 = y_labeled - y_hat_model
loss_val = tf.reduce_sum(
tf.square(r1)) + lambda_param * all_reg0 + lambda_param2 * l2_norm_val
mu = tf.placeholder(tf.float64, shape=[1]) # LM parameter
# initialized store for all params, grad and hessian to be trained
feed_dict = {x_in: x_trained, y_labeled: y_trained}
if hess_approx_flag:
jcb = jacobian(y_hat_model, p)
grads = tf.stack(
[tf.gradients(yi, p)[0] for yi in tf.unstack(y_hat_model, axis=1)],
axis=1)
print(grads.shape)
# g_vals = sess_values.run(grads, feed_dict=feed_dict)
t_jcb = tf.matmul(tf.transpose(jcb), jcb)
j1 = jacobian_mse(y_hat_model, p, nm_set_points, wb_sizes_classif,
wb_shapes)
jt = tf.transpose(j1)
partitioned = tf.dynamic_partition(j1,
nm_set_points,
1,
name='dynamic_unstack')
print(len(partitioned))
l2_grad = tf.gradients(l2_norm_val, l2_p)[0]
dxdt = tf.expand_dims(tf.gradients(all_reg0, lassop)[0], 1)
hess_l2_ps = tf.hessians(l2_norm_val, l2_p)[0]
print('The shape is;', j1.shape)
jtj1 = tf.matmul(jt, j1)
jtr1 = 2 * tf.matmul(jt, r1)
l2grad = tf.expand_dims(l2_grad, 1)
s_l2grad = tf.matmul(l2grad, tf.transpose(l2grad))
# compute gradient of l2 params
reshaped_gradl2 = jtr1[0:shaped_new]
reshaped_l20 = reshaped_gradl2 + lambda_param2 * l2grad # l2_p_grads, 1)
# build another hessian
jt_hess = jt[0:shaped_new] + lambda_param2 * l2grad # l2_p_grads, 1)
jt_hess_end = tf.concat([jt_hess, jt[shaped_new:, :]], axis=0)
j1_t = tf.transpose(jt_hess_end)
# calculate gradient for lasso params group
reshaped_gradl1 = jtr1[shaped_new:]
reshaped_gradl0 = reshaped_gradl1 + lambda_param * dxdt # tf.expand_dims(dxdt, 1) #tf.sign(lasso_p), 1)
# Assemble the lasso group
jtj = tf.matmul(jt_hess_end, j1_t)
jtr = tf.concat([reshaped_l20, reshaped_gradl0], axis=0)
jtr = tf.reshape(jtr, shape=(neurons_cnt_x1, 1))
# The other hess using hessian for in --> hid1
hess_part2 = jtj1[0:shaped_new,
0:shaped_new] + s_l2grad #hess_l2_ps# + h_mat_l2
hess_partsconc = tf.concat(
[hess_part2, jtj1[0:shaped_new, shaped_new:]], axis=1)
jtj3 = tf.concat([hess_partsconc, jtj1[shaped_new:, :]], axis=0)
# remove it
else:
# remove it
# stop_grads = tf.where(tf.math.equal(p, 0))
jtj = tf.squeeze(tf.hessians(loss_val, p)[0])
jtr = -tf.gradients(loss_val, [p])[
0] # , stop_gradients=stop_grads, unconnected_gradients='zero')[0]
jtr = tf.reshape(jtr, shape=(neurons_cnt_x1, 1))
# jtj = hessian_multivar(loss_val, [p])
jtj_store = tf.Variable(
tf.zeros((neurons_cnt_x1, neurons_cnt_x1), dtype=tf.float64))
jtr_store = tf.Variable(tf.zeros((neurons_cnt_x1, 1), dtype=tf.float64))
save_jtj_jtr = [tf.assign(jtj_store, jtj), tf.assign(jtr_store, jtr)]
input_mat = jtj_store + tf.multiply(mu, I_mat)
try:
dx = tf.matmul(tf.linalg.inv(input_mat, adjoint=None), jtr_store)
except:
c = tf.constant(1, dtype=tf.float64)
input_mat += np.identity(input_mat.shape) * c
dx = tf.matmul(tf.linalg.inv(input_mat, adjoint=None), jtr_store)
dx = tf.squeeze(dx)
lm = opt_obj.apply_gradients([(-dx, p)])
# p2 = p.assign(p + dx)
sess_values = kwargs_vals['sess']
feed_dict[mu] = np.array([0.01], dtype=np.float64)
i_cnt = 0
step = 0
mat_values = []
sess_values.run(tf.global_variables_initializer())
current_loss = sess_values.run(loss_val, feed_dict)
while feed_dict[mu] > 1e-6 and step < 500:
p0 = sess_values.run(p)
p_0_indices = np.where(p == 0)
p0[p_0_indices] = 0.0
step += 1
sess_values.run(save_params_p)
sess_values.run(restore_params_p)
if math.log(step, 2).is_integer():
print('step', 'mu: ', 'current loss: ')
print(step, feed_dict[mu][0], current_loss)
success = False
sess_values.run(jtj_store, feed_dict)
sess_values.run(jtr_store, feed_dict)
sess_values.run(save_jtj_jtr, feed_dict)
for _ in range(400):
# p0 equals session object with run of p2 and feed dict
sess_values.run(jtj_store, feed_dict)
sess_values.run(jtr_store, feed_dict)
sess_values.run(save_jtj_jtr, feed_dict)
sess_values.run(lm, feed_dict)
p0 = sess_values.run(p)
p0[np.where(p0 == 0)] = 0
values_vec = np.where(p0 == 0.0)
p0[values_vec] = 0.0
new_loss = sess_values.run(loss_val, feed_dict)
# sess_values.run(save_jtj_jtr, feed_dict)
if new_loss < current_loss:
# divide parameters to 2 groups: 1 for l1 and the other for structured l2
# shaped_new = np.int(wb_sizes_classif[0]) + np.int(wb_sizes_classif[1])
lasso_p0 = p0[shaped_new:]
in2_hidden_params = p0[0:shaped_new]
# mat_values.append(lasso_p0)
mat_values.append(p0)
i_cnt += 1
if len(mat_values) == 3:
sgn1 = mat_values[0] * mat_values[1]
sgn2 = mat_values[1] * mat_values[2]
# send the parameters to compute the values of structured penalty after
# checking if parameters are locally close to zero
px = mat_values[2]
osc_vec0 = np.where((sgn1 < 0.0) & (sgn2 < 0.0))
px[osc_vec0] = 0.0
# join both sets of parameter lists here
px0 = tf.concat([in2_hidden_params, px], 0)
if lambda_param2 > 0.0 and np.mod(step, 5) == 0:
px0 = sess_values.run(px0)
new_all_params, ws_bs_in1_hid1, condvec = func_compute_cond(
px0, lambda_param2, kwargs_vals)
else:
new_all_params = np.array(sess_values.run(px0))
p0 = func_collect_allparams(new_all_params,
wb_sizes_classif, wb_shapes)
p.assign(p0)
mat_values = []
# mat_values = [px]
else:
p.assign(p0)
# sess_values.run(jtj_store, feed_dict)
# sess_values.run(jtr_store, feed_dict)
# sess_values.run(save_jtj_jtr, feed_dict)
# sess_values.run(save_params_p)
feed_dict[mu] /= 10
current_loss = new_loss
success = True
break
else:
feed_dict[mu] *= 10
p.assign(p0)
# sess_values.run(save_params_p)
sess_values.run(restore_params_p)
# sess_values.run(save_jtj_jtr, feed_dict)
# sess_values.run(save_params_p)
if not success:
print('Failed to improve')
break
p_new = sess_values.run(restore_params_p)
abs_p = np.abs(p_new)
idx_absp = np.where(abs_p < 0.01)
p_new[idx_absp] = 0.0
new_all_params, ws_bs_in1_hid1, condvec = func_compute_cond(
p_new, lambda_param2, kwargs_vals)
p_new = func_collect_allparams(p_new, wb_sizes_classif, wb_shapes)
# p_new[osc_vec0]=0.0
non_zero = np.count_nonzero(p_new)
y_predict, x_inputs = func_pred_new(n_in, nn_1, p_new, **kwargs_vals)
inw_hid1 = tf.reshape(p_new[0:shaped_new],
shape=(wb_shapes[0][0] + wb_shapes[1][0],
wb_shapes[0][1]))
feed_dict2 = {x_inputs: x_trained}
print('ENDED ON STEP: ', ' FINAL LOSS:')
print(step, current_loss)
print('Input -> hidden layer 1 Parameters: ')
print(sess_values.run(inw_hid1))
# cv.close()
y_model = sess_values.run(y_predict, feed_dict2)
return restore_params_p, p_new, y_model, current_loss, non_zero
def loop_fn(i, use_pfor):
inp_i = array_ops.expand_dims(array_ops.gather(inp, i), 0)
output = array_ops.reshape(model(inp_i), [-1])
return gradients.jacobian(output,
variables.trainable_variables(),
use_pfor=use_pfor)
def func_classifier_l2l1(xtest1, ytest, kwargs1, kwargspred, **kwargs):
hess_approx_flag = False
initializer = kwargs1['initializer']
mu1, _, mu_dec, max_inc = kwargs['mu'], kwargs['mu_inc'], kwargs[
'mu_dec'], kwargs['mu_inc']
wb_shapes, wb_sizes_classif, hidden = kwargspred['wb_shapes'], kwargspred[
'wb_sizes'], kwargspred['hidden']
activation, xydat, ydatrain = kwargspred['activation'], kwargspred[
'xydat'], kwargspred['xydatrain']
x_in, nclasses = kwargspred['xtr'], kwargs1['nclasses']
y_labeled = kwargspred['ytr']
nm_set_points = x_in.shape[0]
sess, neurons_cnt_x1 = kwargspred['sess'], kwargspred['neurons_cnt']
opt_obj = kwargspred['opt_obj']
params0 = tf.Variable(initializer([neurons_cnt_x1], dtype=tf.float64))
loss, x, y, y_hat_model, l2_norm_val = func_cross_entropy_loss(
wb_sizes_classif, params0, kwargs1)
feed_dict = {x: x_in, y: y_labeled}
feed_dict2 = {x: xtest1, y: ytest}
# check paper and add selected features
# add correlation for Park data set
# tuning parameters
lambda_param = 0.008
lambda_param2 = 0.4
# l2 structured norm loss function
mu = tf.placeholder(tf.float64, shape=[1])
# initialized store for all parameters, gradient and H-matrix to be trained # LM parameter
p_store = tf.Variable(tf.zeros([neurons_cnt_x1], dtype=tf.float64))
save_params_p = tf.compat.v1.assign(p_store, params0)
restore_params_p = tf.compat.v1.assign(params0, p_store)
I_mat = tf.eye(neurons_cnt_x1, dtype=tf.float64)
shaped_new = np.int(wb_sizes_classif[0]) + np.int(wb_sizes_classif[1])
lasso_p = params0[shaped_new:]
l2_p = params0[0:shaped_new]
print(lasso_p)
all_reg0 = tf.reduce_sum(tf.abs(lasso_p))
loss_val = loss + lambda_param * all_reg0 + lambda_param2 * l2_norm_val
if hess_approx_flag:
# j1 equal jacobian_classif(y_hat_model, p, nm_set_points)
# jt = tf.transpose(j1)
# jtj = tf.matmul(jt, j1)
# jtr = tf.matmul(jt, r)
jcb = jacobian(y_hat_model, params0)
t_jcb = tf.matmul(tf.transpose(jcb), jcb)
j1 = jacobian_mse(y_hat_model, params0, nm_set_points,
wb_sizes_classif, wb_shapes)
jt = tf.transpose(j1)
partitioned = tf.dynamic_partition(j1,
nm_set_points,
1,
name='dynamic_unstack')
print(len(partitioned))
l2_grad = tf.gradients(l2_norm_val, l2_p)[0]
dxdt = tf.expand_dims(tf.gradients(all_reg0, lasso_p)[0], 1)
hess_l2_ps = tf.hessians(l2_norm_val, l2_p)[0]
print('The shape is;', j1.shape)
jtj1 = tf.matmul(jt, j1)
jtr1 = 2 * tf.matmul(jt, r1)
l2grad = tf.expand_dims(l2_grad, 1)
s_l2grad = tf.matmul(l2grad, tf.transpose(l2grad))
# compute gradient of l2 params
reshaped_gradl2 = jtr1[0:shaped_new]
reshaped_l20 = reshaped_gradl2 + lambda_param2 * l2grad # l2_p_grads, 1)
# build another hessian
jt_hess = jt[0:shaped_new] + lambda_param2 * l2grad # l2_p_grads, 1)
jt_hess_end = tf.concat([jt_hess, jt[shaped_new:, :]], axis=0)
j1_t = tf.transpose(jt_hess_end)
# calculate gradient for lasso params group
reshaped_gradl1 = jtr1[shaped_new:]
reshaped_gradl0 = reshaped_gradl1 + lambda_param * dxdt # tf.expand_dims(dxdt, 1) #tf.sign(lasso_p), 1)
# Assemble the lasso group
jtj = tf.matmul(jt_hess_end, j1_t)
jtr = tf.concat([reshaped_l20, reshaped_gradl0], axis=0)
jtr = tf.reshape(jtr, shape=(neurons_cnt_x1, 1))
# The other hess using hessian for in --> hid1
hess_part2 = jtj1[0:shaped_new,
0:shaped_new] + s_l2grad #hess_l2_ps# + h_mat_l2
hess_partsconc = tf.concat(
[hess_part2, jtj1[0:shaped_new, shaped_new:]], axis=1)
jtj3 = tf.concat([hess_partsconc, jtj1[shaped_new:, :]], axis=0)
else:
# remove it
# stop_grads = tf.where(tf.math.equal(p, 0))
# jtj = hessian_multivar(loss_val, [params0])
jtj = tf.hessians(loss_val, params0)[0]
jtr = -tf.gradients(loss_val, params0)[
0] # stop_gradients=stop_grads, unconnected_gradients='zero')[0]
jtr = tf.reshape(jtr, shape=(neurons_cnt_x1, 1))
jtj_store = tf.Variable(
tf.zeros((neurons_cnt_x1, neurons_cnt_x1), dtype=tf.float64))
jtr_store = tf.Variable(tf.zeros((neurons_cnt_x1, 1), dtype=tf.float64))
save_jtj_jtr = [tf.assign(jtj_store, jtj), tf.assign(jtr_store, jtr)]
input_mat = jtj_store + tf.multiply(mu, I_mat)
try:
dx = tf.matmul(tf.linalg.inv(input_mat, adjoint=None), jtr_store)
except:
c = tf.constant(0.1, dtype=tf.float64)
input_mat += np.identity(input_mat.shape) * c
dx = tf.matmul(tf.linalg.inv(input_mat, adjoint=None), jtr_store)
dx = tf.squeeze(dx)
lm = opt_obj.apply_gradients([(-dx, params0)])
# p2 equal p.assign(p + dx)
sess_values = kwargspred['sess']
# print(sess_values.run(lasso_p))
feed_dict[mu] = np.array([0.1], dtype=np.float64)
i_cnt = 0
step = 0
mat_values = []
sess_values.run(tf.global_variables_initializer())
current_loss = sess_values.run(loss_val, feed_dict)
while feed_dict[mu] > 1e-10 and step < 200:
p0 = sess_values.run(params0)
values_vec = np.where(params0 == 0)
p0[values_vec] = 0.0
step += 1
sess.run(save_params_p)
# sess.run(restore_params_p)
if math.log(step, 2).is_integer():
print('step', 'mu: ', 'current loss: ')
print(step, feed_dict[mu][0], current_loss)
success = False
sess_values.run(jtj_store, feed_dict)
sess_values.run(p_store)
for _ in range(400):
sess_values.run(save_jtj_jtr, feed_dict)
sess_values.run(jtj_store, feed_dict)
# p0 equals session object with run of p2 and feed dict
sess_values.run(lm, feed_dict)
p0 = sess_values.run(params0)
# p0 equals tf.where(p == 0, tf.zeros_like(p), p)
values_vec = np.where(p0 == 0.0)
p0[values_vec] = 0.0
new_loss = sess_values.run(loss_val, feed_dict)
if new_loss < current_loss:
# divide parameters to 2 groups: 1 for l1 and the other for structured l2
shaped_new = np.int(wb_sizes_classif[0]) + np.int(
wb_sizes_classif[1])
lasso_p0 = p0[shaped_new:]
in2_hidden_params = p0[0:shaped_new]
mat_values.append(lasso_p0)
i_cnt += 1
if len(mat_values) == 3:
sgn1 = mat_values[0] * mat_values[1]
sgn2 = mat_values[1] * mat_values[2] # store parameters
# checking if parameters are locally close to zero
px = mat_values[2]
values_vec = np.where((sgn1 < 0) & (sgn2 < 0))
px[values_vec] = 0.0
print(len(mat_values))
# join both sets of parameter lists here joined_params = np.concatenate(l2_params_set, new_p0)
px0 = tf.concat([in2_hidden_params, px], 0)
if lambda_param2 > 0.0 and np.mod(step, 2) == 0:
px0 = sess_values.run(px0)
new_all_params, ws_bs_in1_hid1, _ = func_compute_cond(
px0, lambda_param2, kwargspred)
else:
new_all_params = np.array(sess_values.run(px0))
# sess_px0 equal np.array(sess_values.run(px0))
p_values_send = func_collect_allparams(
new_all_params, wb_sizes_classif, wb_shapes)
print(p_values_send.shape)
params0.assign(p_values_send)
i_cnt = 0
mat_values = []
mat_values = [px]
else:
params0.assign(p0)
feed_dict[mu] /= 10
current_loss = new_loss
success = True
break
else:
feed_dict[mu] *= 10
params0.assign(p0)
# sess.run(save_params_p)
sess_values.run(restore_params_p)
if not success:
print('Failed to improve')
break
p_new = sess_values.run(restore_params_p)
abs_p = np.abs(p_new)
idx_absp = np.where(abs_p < 0.1)
p_new[idx_absp] = 0.0
p_new[values_vec] = 0.0
correct_prediction, feed_dict2, y_hat_classif_logits = predclassif(
wb_sizes_classif, xydat, hidden, p_new, activation, wb_shapes,
nclasses)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print('ENDED ON STEP: ')
print(step)
print(' FINAL LOSS:')
print(current_loss)
print('Parameters: ')
print(sess_values.run(restore_params_p))
print('Parameters: ')
print(p_new)
print("Accuracy:", sess.run(accuracy, feed_dict2))
correct_predictions = sess.run(y_hat_classif_logits, feed_dict2)
correct_prediction, feed_dict21, y_hat_classif_logits = predclassif(
wb_sizes_classif, ydatrain, hidden, p_new, activation, wb_shapes,
nclasses)
correct_predictions_train = sess.run(y_hat_classif_logits, feed_dict21)
return p_new, correct_predictions, correct_predictions_train
def loop_fn(i, use_pfor):
image = array_ops.gather(images, i)
logits = array_ops.reshape(model(image, training=training), [-1])
return gradients.jacobian(logits,
variables.trainable_variables(),
use_pfor=use_pfor)
def empirical_NTK(model, train_images):
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
print(rank)
model.compile("sgd", loss=lambda target, pred: pred)
import tensorflow.keras.backend as K
num_layers = len(model.trainable_weights)
trainable_weights = np.array(model.trainable_weights)
# fs = []
# params_per_chunk = []
num_chunks = min(size, num_layers)
layers_per_chunk = num_layers // num_chunks
if rank < num_chunks:
chunks = list(
range(int(rank * layers_per_chunk),
int((rank + 1) * layers_per_chunk)))
if rank < num_layers % num_chunks:
chunks.append(num_chunks * layers_per_chunk + rank)
params_per_layer = np.array(
[np.prod(x.shape) for x in trainable_weights])
params_per_chunk = sum(params_per_layer[chunks])
# grads = model.optimizer.get_gradients(model.output, list(trainable_weights[chunks]))
# grads = tf.keras.backend.gradients(model.output, list(trainable_weights[chunks]))
# grads = tf.gradients(model.output, list(trainable_weights[chunks]))
# symb_inputs = (model._feed_inputs + model._feed_targets)
symb_inputs = model._feed_inputs
grads = jacobian(model.output, list(trainable_weights[chunks]))
f = K.function(symb_inputs, grads)
def get_weight_grad(model, inputs, outputs):
""" Gets gradient of model for given inputs and outputs for all weights"""
x, y, _ = model._standardize_user_data(inputs, outputs)
batch_size = inputs.shape[0]
# output_grad = f(x + y)
output_grad = f(x)
print(output_grad[0].shape)
# output_grad = np.concatenate([x.flatten() for x in output_grad])
output_grad = np.concatenate(
[x.reshape((batch_size, -1)) for x in output_grad])
return output_grad
X = train_images
m = len(X)
Y = np.zeros((len(X), 1))
NTK = np.zeros((len(X), len(X)))
chunk1 = 25
chunk2 = chunk1
# it's benefitial to chunk in j2 too, in orden to reduce the python for loop. Even though we do more on numpy/pytorch (by reducing the chunking on j1, we do more grad computaiotns), python is much slower than those, and so tradeoff is worth it I think
# print("tot_parameters",tot_parameters)
# jac1 = np.zeros((chunk1,params_per_chunk))
# jac2 = np.zeros((chunk2,params_per_chunk))
num_chunk1s = m // chunk1
if m % chunk1 > 0:
num_chunk1s += 1
num_chunk2s = m // chunk2
if m % chunk2 > 0:
num_chunk2s += 1
for j1 in range(num_chunk1s):
if m % chunk1 > 0 and j1 == num_chunk1s - 1:
num_inputs1 = m % chunk1
# jac1 = np.zeros((num_inputs1,params_per_chunk))
else:
num_inputs1 = chunk1
print("chunk", j1, "out of", num_chunk1s)
sys.stdout.flush()
# for i in range(num_inputs1):
# gradient = get_weight_grad(model, train_images[j1*chunk1+i:j1*chunk1+i+1], Y[j1*chunk1+i:j1*chunk1+i+1])
# jac1[i,:] = gradient
jac1 = get_weight_grad(
model, train_images[j1 * chunk1:j1 * chunk1 + num_inputs1],
Y[j1 * chunk1:j1 * chunk1 + num_inputs1])
print(jac1.shape)
for j2 in range(j1, num_chunk2s):
if m % chunk2 > 0 and j2 == num_chunk2s - 1:
num_inputs2 = m % chunk2
# jac2 = np.zeros((num_inputs2,params_per_chunk))
else:
num_inputs2 = chunk2
print(j1, j2)
# for i in range(num_inputs2):
# gradient = get_weight_grad(model, train_images[j2*chunk2+i:j2*chunk2+i+1], Y[j2*chunk2+i:j2*chunk2+i+1])
# jac2[i,:] = gradient
jac2 = get_weight_grad(
model, train_images[j2 * chunk2:j2 * chunk2 + num_inputs2],
Y[j2 * chunk2:j2 * chunk2 + num_inputs2])
NTK[j1 * chunk1:j1 * chunk1 + num_inputs1,
j2 * chunk2:j2 * chunk2 + num_inputs2] += np.matmul(
jac1, jac2.T)
ntk_recv = None
if rank == 0:
ntk_recv = np.zeros_like(NTK)
comm.Reduce(NTK, ntk_recv, op=MPI.SUM, root=0)
if rank == 0:
NTK = (ntk_recv + ntk_recv.T) / 2
return NTK
def getGrad2(image):
with tf.GradientTape(persistent=True) as tape:
x, concepts, _ = model.helper(image)
gradients = jacobian(concepts, x)
return gradients
def loop_fn(i, use_pfor):
image = array_ops.gather(images, i)
logits = array_ops.reshape(model(image, training=training), [-1])
return gradients.jacobian(
logits, variables.trainable_variables(), use_pfor=use_pfor)
def loop_fn(i, use_pfor):
inp_i = array_ops.expand_dims(array_ops.gather(inp, i), 0)
output = array_ops.reshape(model(inp_i), [-1])
return gradients.jacobian(
output, variables.trainable_variables(), use_pfor=use_pfor)
