def move_to_click(x, y):
t('绝对移动 点击')
move_to(x, y)
time.sleep(random.random() / 21)
km._left_button_down()
time.sleep(random.random() / 5)
km._left_button_up()
def loss_and_grad(Wf):
"""Returns cost, gradient for current parameter vector."""
global fs, X, global_cov_A, global_whitened_A
W = u.unflatten(Wf, fs[1:]) # perftodo: this creates transposes
W.insert(0, X)
A = [None] * (n + 2)
A[1] = W[0]
for i in range(1, n + 1):
A[i + 1] = nonlin(W[i] @ A[i])
err = (A[n + 1] - A[1])
B = [None] * (n + 1)
B[n] = 2 * err * d_nonlin(A[n + 1])
for i in range(n - 1, -1, -1):
backprop = t(W[i + 1]) @ B[i + 1]
B[i] = backprop * d_nonlin(A[i + 1])
dW = [None] * (n + 1)
for i in range(1, n + 1):
dW[i] = (B[i] @ t(A[i]))
loss = u.L2(err)
grad = u.flatten(dW[1:])
return loss, grad
def move_rel_click(x, y):
t('相对移动 点击')
move_rel(x, y)
time.sleep(random.random() / 20)
km._left_button_down()
time.sleep(random.random() / 5)
km._left_button_up()
def model_fit():
t('开始训练')
global model, epochs, train_data_gen, total_train, batch_size, val_data_gen, total_val
history = model.fit_generator(train_data_gen,
steps_per_epoch=total_train // batch_size,
epochs=epochs,
validation_data=val_data_gen,
validation_steps=total_val // batch_size)
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs_range = range(epochs)
plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
def correct(self, grad):
"""Accepts IndexedGrad object, produces corrected version."""
s = self
vars_ = []
grads_new = []
assert list(grad) == self.model.trainable_vars
dsize = get_batch_size(grad)
for var in grad:
vars_.append(var)
A = s.extract_A(grad, var) # extract activations
B = s.extract_B(grad, var)*dsize # extract backprops
if s.needs_correction(var):
# correct the gradient. Assume op is left matmul
A_svd = s[var].A.svd
B2_svd = s[var].B2.svd
if inverse_method == 'pseudo_inverse':
A_new = u.pseudo_inverse2(A_svd) @ A
B_new = u.pseudo_inverse2(B2_svd) @ B
elif inverse_method == 'inverse':
A_new = A_svd.inv @ A
B_new = B2_svd.inv @ B
else:
assert False
dW_new = (B_new @ t(A_new)) / dsize
grads_new.append(dW_new)
else:
dW = B@t(A)/dsize
grads_new.append(dW)
return IndexedGrad(grads=grads_new, vars_=vars_)
def model_predict(paths):
t('模型预测')
global model
imgs = [load_and_preprocess_image(path) for path in paths]
imgs = tf.convert_to_tensor(imgs)
predictions = model.predict(imgs)
predictions = [row[0] for row in predictions]
print(predictions)
min_index = predictions.index(min(predictions))
print(f' 预测结果为 第 > {min_index + 1} < 张图片')
return min_index
def count():
t('统计')
global total_train, total_val, epochs
total_train = sumNum(c.train_dir)
total_val = sumNum(c.validation_dir)
epochs = math.ceil(total_train / batch_size)
print('训练集标签 :', os.listdir(c.train_dir))
print('训练集图片个数 :', total_train)
print("验证集个数 :", total_val)
print(f'每批次训练个数: {batch_size}, 共要进行 {epochs} 轮训练')
if total_train == 0:
print('样本为0 无法训练')
return False
return True
def natural_kfac(lr0, num_samples=1):
init_dict[lr_holder] = lr0
np.random.seed(0)
tf.set_random_seed(0)
A = [0]*(n+2)
A2 = [0]*(n+2) # augmented forward props for natural gradient
A[0] = u.Identity(dsize)
A2[0] = u.Identity(dsize*num_samples)
for i in range(n+1):
# fs is off by 2 from common notation, ie W[0] has shape f[0],f[-1]
A[i+1] = tf.matmul(W[i], A[i], name="A"+str(i+1))
if i == 0:
A2[i+1] = tf.concat([W[0]]*num_samples, axis=1)
else:
A2[i+1] = tf.matmul(W[i], A2[i], name="A2"+str(i+1))
# create backprop matrices
# B[i] has backprop for matrix i
B = [0]*(n+1)
B2 = [0]*(n+1)
B[n] = -err/dsize
B2[n] = tf.random_normal((f(n), dsize*num_samples), 0, 1, seed=0,
dtype=dtype)
for i in range(n-1, -1, -1):
B[i] = tf.matmul(tf.transpose(W[i+1]), B[i+1], name="B"+str(i))
B2[i] = tf.matmul(tf.transpose(W[i+1]), B2[i+1], name="B2"+str(i))
# Kronecker factored covariance blocks
iblocks = u.empty_grid(n+1, n+1)
for i in range(1, n+1):
for j in range(1, n+1):
if i == j:
acov = A2[i] @ t(A2[j]) / (dsize*num_samples)
bcov = B2[i] @ t(B2[j]) / (dsize*num_samples);
term = kr(u.pseudo_inverse(acov), u.pseudo_inverse(bcov))
else:
term = tf.zeros(shape=(f(i)*f(i-1), f(j)*f(j-1)), dtype=dtype)
iblocks[i][j]=term
# remove leftmost blocks (those are with respect to W[0] which is input)
del iblocks[0]
for row in iblocks:
del row[0]
ifisher = u.concat_blocks(iblocks)
train_op = grad_update(Wf - lr * ifisher @ dWf)
return do_run(train_op)
def __init__(self, data, var, prefix, Lambda):
global numeric_inverse
dsize = get_batch_size(data)
# TODO: try adding regularizer later
cov_op = data @ t(data) / dsize
if regularize_covariances:
#ii = u.Identity(cov_op.shape[0])
#regularizer = Lambda*ii
regularizer = u.cachedGpuIdentityRegularizer(cov_op.shape[0],
Lambda=u.args.Lambda)
cov_op = cov_op + regularizer
cov_name = "%s_cov_%s" %(prefix, var.op.name)
svd_name = "%s_svd_%s" %(prefix, var.op.name)
# hack
# pp = u.args.kfac_polyak_factor
pp = 1
if pp<1:
self.cov = u.get_variable(name=cov_name, initializer=ii)
else:
self.cov = u.get_variable(name=cov_name, initializer=cov_op)
self.svd = u.SvdWrapper(target=self.cov, name=svd_name,
do_inverses=(inverse_method=='inverse'))
# self.cov_update_op = self.cov.initializer
if pp<1:
self.cov_update_op = self.cov.assign(cov_op*(1-pp)+self.cov*pp).op
else:
self.cov_update_op = self.cov.assign(cov_op).op
def hessian_quadratic(delta):
# update_covariances()
W = u.unflatten(delta, fs[1:])
W.insert(0, None)
total = 0
for l in range(1, n+1):
decrement = tf.trace(t(W[l])@cov_B2[l]@W[l]@cov_A[l])
total+=decrement
return (total/2).eval()
def loss_and_grad(Wf):
"""Returns cost, gradient for current parameter vector."""
global fs, X, global_cov_A, global_whitened_A
W = u.unflatten(Wf, fs[1:]) # perftodo: this creates transposes
W.insert(0, X)
A = [None] * (n + 2)
A[1] = W[0]
for i in range(1, n + 1):
A[i + 1] = tf.sigmoid(W[i] @ A[i])
err = (A[3] - A[1])
def d_sigmoid(y):
return y * (1 - y)
B = [None] * (n + 1)
B2 = [None] * (n + 1)
B[n] = err * d_sigmoid(A[n + 1])
sampled_labels = tf.random_normal((f(n), f(-1)), dtype=dtype, seed=0)
B2[n] = sampled_labels * d_sigmoid(A[n + 1])
for i in range(n - 1, -1, -1):
backprop = t(W[i + 1]) @ B[i + 1]
backprop2 = t(W[i + 1]) @ B2[i + 1]
B[i] = backprop * d_sigmoid(A[i + 1])
B2[i] = backprop2 * d_sigmoid(A[i + 1])
dW = [None] * (n + 1)
pre_dW = [None] * (n + 1) # preconditioned dW
cov_A = [None] * (n + 1) # covariance of activations[i]
whitened_A = [None] * (n + 1) # covariance of activations[i]
cov_B2 = [None] * (n + 1) # covariance of synthetic backprops[i]
vars_svd_A = [None] * (n + 1)
vars_svd_B2 = [None] * (n + 1)
if global_cov_A is None:
global_cov_A = A[1] @ t(A[1]) / dsize
global_whitened_A = regularized_inverse(global_cov_A, lambda_) @ A[1]
cov_A[1] = global_cov_A
whitened_A[1] = global_whitened_A
for i in range(1, n + 1):
if i > 1:
cov_A[i] = A[i] @ t(A[i]) / dsize
whitened_A[i] = regularized_inverse(cov_A[i], lambda_) @ A[i]
cov_B2[i] = B2[i] @ t(B2[i]) / dsize
whitened_B = regularized_inverse(cov_B2[i], lambda_) @ B[i]
pre_dW[i] = (whitened_B @ t(whitened_A[i])) / dsize
dW[i] = (B[i] @ t(A[i])) / dsize
reconstruction = u.L2(err) / (2 * dsize)
loss = reconstruction
grad = u.flatten(dW[1:])
kfac_grad = u.flatten(pre_dW[1:])
return loss, grad, kfac_grad
def data_generator():
t('数据生成器')
global train_data_gen, val_data_gen
train_image_generator = ImageDataGenerator(
rescale=1. / 255) # Generator for our training data
validation_image_generator = ImageDataGenerator(
rescale=1. / 255) # Generator for our validation data
train_data_gen = train_image_generator.flow_from_directory(
batch_size=batch_size,
directory=c.train_dir,
shuffle=True,
target_size=(IMG_HEIGHT, IMG_WIDTH),
class_mode='binary')
val_data_gen = validation_image_generator.flow_from_directory(
batch_size=batch_size,
directory=c.validation_dir,
target_size=(IMG_HEIGHT, IMG_WIDTH),
class_mode='binary')
def hessian_quadratic_inv(delta):
# update_covariances()
W = u.unflatten(delta, fs[1:])
W.insert(0, None)
total = 0
for l in range(1, n+1):
invB2 = u.pseudo_inverse2(vars_svd_B2[l])
invA = u.pseudo_inverse2(vars_svd_A[l])
decrement = tf.trace(t(W[l])@invB2@W[l]@invA)
total+=decrement
return (total/2).eval()
def newton_bd(lr0):
init_dict[lr_holder] = lr0
# Create B's
B = [0]*(n+1)
B[n] = -err/dsize
Bn = [0]*(n+1) # Newton-modified backprop
Bn[n] = u.Identity(f(n))
for i in range(n-1, -1, -1):
B[i] = t(W[i+1]) @ B[i+1]
Bn[i] = t(W[i+1]) @ Bn[i+1]
# Create U's
U = [list(range(n+1)) for _ in range(n+1)]
for bottom in range(n+1):
for top in range(n+1):
if bottom > top:
prod = u.Identity(f(top))
else:
prod = u.Identity(f(bottom-1))
for i in range(bottom, top+1):
prod = prod@t(W[i])
U[bottom][top] = prod
# Block i, j gives hessian block between layer i and layer j
blocks = [list(range(n+1)) for _ in range(n+1)]
for i in range(1, n+1):
for j in range(1, n+1):
term1 = kr(A[i] @ t(A[j]), Bn[i] @ t(Bn[j])) / dsize;
if i == j:
term2 = tf.zeros((f(i)*f(i-1), f(i)*f(i-1)), dtype=dtype)
elif i < j:
term2 = kr(A[i] @ t(B[j]), U[i+1][j-1])
else:
term2 = kr(t(U[j+1][i-1]), B[i] @ t(A[j]))
blocks[i][j]=term1 + term2 @ Kmat(f(j), f(j-1))
# remove leftmost blocks (those are with respect to W[0] which is input)
del blocks[0]
for row in blocks:
del row[0]
# todo -- figure out why this is not the same as block inversion
# grads = tf.concat([u.khatri_rao(A[i], Bn[i]) for i in range(1, n+1)], axis=0)
# hess = grads @ tf.transpose(grads) / dsize
# blocks = u.partition_matrix_evenly(hess, 10)
ihess = u.concat_blocks(u.block_diagonal_inverse(blocks))
train_op = grad_update(Wf - lr * ihess @ dWf)
return do_run(train_op)
def model_summary():
t('模型编译统计')
global model
model = Sequential([
Conv2D(16, (3, 3),
padding='same',
activation='relu',
input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)),
MaxPooling2D((2, 2)),
Conv2D(32, (3, 3), padding='same', activation='relu'),
MaxPooling2D((2, 2)),
Conv2D(64, (3, 3), padding='same', activation='relu'),
MaxPooling2D(),
Flatten(),
Dense(64, activation='relu'),
Dense(1)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=['accuracy'])
model.summary()
def newton_kfac(lr0):
init_dict[lr_holder] = lr0
# Create B's
B = [0]*(n+1)
B[n] = -err/dsize
Bn = [0]*(n+1) # Newton-modified backprop
Bn[n] = u.Identity(f(n))
for i in range(n-1, -1, -1):
B[i] = t(W[i+1]) @ B[i+1]
Bn[i] = t(W[i+1]) @ Bn[i+1]
# inverse Hessian blocks
iblocks = u.empty_grid(n+1, n+1)
for i in range(1, n+1):
for j in range(1, n+1):
# reuse Hess tensor calculation in order to get off-diag block sizes
dummy_term = kr(A[i] @ t(A[j]), Bn[i] @ t(Bn[j])) / dsize;
if i == j:
acov = A[i] @ t(A[j])
bcov = (Bn[i] @ t(Bn[j]))/dsize
term = kr(u.pseudo_inverse(acov), u.pseudo_inverse(bcov))
else:
term = tf.zeros(shape=dummy_term.get_shape(), dtype=dtype)
iblocks[i][j]=term
# remove leftmost blocks (those are with respect to W[0] which is input)
del iblocks[0]
for row in iblocks:
del row[0]
ihess = u.concat_blocks(iblocks)
train_op = grad_update(Wf - lr * ihess @ dWf)
return do_run(train_op)
def newton(lr0):
init_dict[lr_holder] = lr0
# todo, get rid of B's
# Create B's
B = [0]*(n+1)
B[n] = -err/dsize
Bn = [0]*(n+1) # Newton-modified backprop
Bn[n] = u.Identity(f(n))
for i in range(n-1, -1, -1):
B[i] = t(W[i+1]) @ B[i+1]
Bn[i] = t(W[i+1]) @ Bn[i+1]
# Create U's
U = [list(range(n+1)) for _ in range(n+1)]
for bottom in range(n+1):
for top in range(n+1):
if bottom > top:
prod = u.Identity(f(top))
else:
prod = u.Identity(f(bottom-1))
for i in range(bottom, top+1):
prod = prod@t(W[i])
U[bottom][top] = prod
# Block i, j gives hessian block between layer i and layer j
blocks = [list(range(n+1)) for _ in range(n+1)]
for i in range(1, n+1):
for j in range(1, n+1):
term1 = kr(A[i] @ t(A[j]), Bn[i] @ t(Bn[j])) / dsize;
if i == j:
term2 = tf.zeros((f(i)*f(i-1), f(i)*f(i-1)), dtype=dtype)
elif i < j:
term2 = kr(A[i] @ t(B[j]), U[i+1][j-1])
else:
term2 = kr(t(U[j+1][i-1]), B[i] @ t(A[j]))
blocks[i][j]=term1 + term2 @ Kmat(f(j), f(j-1))
# remove leftmost blocks (those are with respect to W[0] which is input)
del blocks[0]
for row in blocks:
del row[0]
hess = u.concat_blocks(blocks)
ihess = u.pseudo_inverse(hess)
train_op = grad_update(Wf - lr * ihess @ dWf)
return do_run(train_op)
def rotations2_natural_sampled_kfac(num_samples=1):
tf.reset_default_graph()
np.random.seed(0)
tf.set_random_seed(0)
# override kr with no-shape-inferring version
def kr(A, B):
return u.kronecker(A, B, do_shape_inference=False)
X0 = np.genfromtxt('data/large_rotations2_X0.csv', delimiter=",")
Y0 = np.genfromtxt('data/large_rotations2_Y0.csv', delimiter=",")
W0f = v2c_np(np.genfromtxt('data/large_rotations2_W0f.csv', delimiter=","))
fs = np.genfromtxt('data/large_rotations2_fs.csv',
delimiter=",").astype(np.int32)
n = len(fs) - 2 # number of layers
def f(i):
return fs[i + 1] # W[i] has shape f[i] x f[i-1]
dsize = X0.shape[1]
assert f(-1) == dsize
# load W0f and do shape checks (can remove)
W0s = u.unflatten_np(W0f,
fs[1:]) # Wf doesn't have first layer (data matrix)
W0s.insert(0, X0)
Wf_holder = tf.placeholder(dtype, shape=W0f.shape)
Wf = tf.Variable(Wf_holder, name="Wf")
Wf_copy = tf.Variable(Wf_holder, name="Wf_copy")
init_dict = {Wf_holder: W0f}
# Create W's
# initialize data + layers
# W[0] is input matrix (X), W[n] is last matrix
# A[1] has activations for W[1], equal to W[0]=X
# A[n+1] has predictions
# Create W's
W = u.unflatten(Wf, fs[1:])
X = tf.constant(X0)
Y = tf.constant(Y0)
W.insert(0, X)
A = [0] * (n + 2)
A2 = [0] * (n + 2) # augmented forward props for natural gradient
A[0] = u.Identity(dsize)
A2[0] = u.Identity(dsize * num_samples)
for i in range(n + 1):
# fs is off by 2 from common notation, ie W[0] has shape f[0],f[-1]
A[i + 1] = tf.matmul(W[i], A[i], name="A" + str(i + 1))
if i == 0:
# replicate dataset multiple times corresponding to number of samples
A2[i + 1] = tf.concat([W[0]] * num_samples, axis=1)
else:
A2[i + 1] = tf.matmul(W[i], A2[i], name="A2" + str(i + 1))
# input dimensions match
assert W[0].get_shape() == X0.shape
# output dimensions match
assert W[-1].get_shape()[0], W[0].get_shape()[1] == Y0.shape
assert A[n + 1].get_shape() == Y0.shape
err = Y - A[n + 1]
loss = tf.reduce_sum(tf.square(err)) / (2 * dsize)
# lower learning rate by 10x
lr = tf.Variable(0.01, dtype=dtype)
# create backprop matrices
# B[i] has backprop for matrix i
B = [0] * (n + 1)
B2 = [0] * (n + 1)
B[n] = -err / dsize
B2[n] = tf.random_normal((f(n), dsize * num_samples),
0,
1,
seed=0,
dtype=dtype)
for i in range(n - 1, -1, -1):
B[i] = tf.matmul(tf.transpose(W[i + 1]), B[i + 1], name="B" + str(i))
B2[i] = tf.matmul(tf.transpose(W[i + 1]),
B2[i + 1],
name="B2" + str(i))
# Create gradient update. Make copy of variables and split update into
# two run calls. Using single set of variables will gives updates that
# occasionally produce wrong results/NaN's because of data race
dW = [0] * (n + 1)
dW2 = [0] * (n + 1)
updates1 = [0] * (n + 1) # compute updated value into Wcopy
updates2 = [0] * (n + 1) # copy value back into W
Wcopy = [0] * (n + 1)
for i in range(n + 1):
Wi_name = "Wcopy" + str(i)
Wi_shape = (fs[i + 1], fs[i])
Wi_init = tf.zeros(dtype=dtype, shape=Wi_shape, name=Wi_name + "_init")
Wcopy[i] = tf.Variable(Wi_init, name=Wi_name, trainable=False)
dW[i] = tf.matmul(B[i], tf.transpose(A[i]), name="dW" + str(i))
dW2[i] = tf.matmul(B2[i], tf.transpose(A2[i]), name="dW2" + str(i))
del dW[0] # get rid of W[0] update
del dW2[0] # get rid of W[0] update
# construct flattened gradient update vector
dWf = tf.concat([vec(grad) for grad in dW], axis=0)
# todo: divide both activations and backprops by size for cov calc
# Kronecker factored covariance blocks
iblocks = u.empty_grid(n + 1, n + 1)
for i in range(1, n + 1):
for j in range(1, n + 1):
if i == j:
acov = A2[i] @ t(A2[j]) / (dsize * num_samples)
bcov = B2[i] @ t(B2[j]) / (dsize * num_samples)
term = kr(u.pseudo_inverse(acov), u.pseudo_inverse(bcov))
else:
term = tf.zeros(shape=(f(i) * f(i - 1), f(j) * f(j - 1)),
dtype=dtype)
iblocks[i][j] = term
# remove leftmost blocks (those are with respect to W[0] which is input)
del iblocks[0]
for row in iblocks:
del row[0]
ifisher = u.concat_blocks(iblocks)
Wf_copy = tf.Variable(tf.zeros(dtype=dtype,
shape=Wf.shape,
name="Wf_copy_init"),
name="Wf_copy")
new_val_matrix = Wf - lr * (ifisher @ dWf)
train_op1 = Wf_copy.assign(new_val_matrix)
train_op2 = Wf.assign(Wf_copy)
sess = tf.Session()
sess.run(tf.global_variables_initializer(), feed_dict=init_dict)
observed_losses = []
u.reset_time()
for i in range(20):
loss0 = sess.run(loss)
print(loss0)
observed_losses.append(loss0)
sess.run(train_op1)
sess.run(train_op2)
u.record_time()
u.summarize_time()
u.summarize_graph()
def loss_and_output_and_grad(Wf):
"""Returns cost, gradient for current parameter vector."""
global fs, X, global_cov_A, global_whitened_A
W = u.unflatten(Wf, fs[1:]) # perftodo: this creates transposes
W.insert(0, X)
A = [None] * (n + 2)
A[1] = W[0]
for i in range(1, n + 1):
A[i + 1] = nonlin(W[i] @ A[i])
# print(A[i+1])
# # print(A[i+1])
err = (A[n + 1] - A[1])
B = [None] * (n + 1)
B2 = [None] * (n + 1)
B[n] = err * d_nonlin(A[n + 1])
# sampled_labels = tf.random_normal((f(n), f(-1)), dtype=dtype, seed=0)
# print('random')
# print(np.random.randn(*X.shape).astype(dtype))
noise = tf.constant(np.random.randn(*err.shape).astype(dtype))
# print(noise)
B2[n] = noise * d_nonlin(A[n + 1])
# print("B2[n]", B2[n])
# print(B[n])
for i in range(n - 1, -1, -1):
backprop = t(W[i + 1]) @ B[i + 1]
backprop2 = t(W[i + 1]) @ B2[i + 1]
B[i] = backprop * d_nonlin(A[i + 1])
B2[i] = backprop2 * d_nonlin(A[i + 1])
dW = [None] * (n + 1)
pre_dW = [None] * (n + 1) # preconditioned dW
cov_A = [None] * (n + 1) # covariance of activations[i]
whitened_A = [None] * (n + 1) # covariance of activations[i]
cov_B2 = [None] * (n + 1) # covariance of synthetic backprops[i]
cov_B = [None] * (n + 1) # covariance of synthetic backprops[i]
vars_svd_A = [None] * (n + 1)
vars_svd_B2 = [None] * (n + 1)
if global_cov_A is None:
global_cov_A = A[1] @ t(A[1]) / dsize
global_whitened_A = regularized_inverse(global_cov_A) @ A[1]
cov_A[1] = global_cov_A
whitened_A[1] = global_whitened_A
for i in range(1, n + 1):
if i > 1:
cov_A[i] = A[i] @ t(A[i]) / dsize
whitened_A[i] = regularized_inverse(cov_A[i]) @ A[i]
cov_B2[i] = B2[i] @ t(B2[i]) / dsize
cov_B[i] = B[i] @ t(B[i]) / dsize
if SYNTHETIC_LABELS:
whitened_B = regularized_inverse(cov_B2[i]) @ B[i]
else:
whitened_B = regularized_inverse(cov_B[i]) @ B[i]
#regularized_inverse(cov_B[i])
# print("A", i, cov_A[i], regularized_inverse(cov_A[i]))
# print("B", i, cov_B[i], regularized_inverse(cov_B[i]))
# pre_dW[i] = (whitened_B @ t(whitened_A[i]))/dsize
# print(i, 'A', A[i].numpy())
# print(regularized_inverse(cov_A[i]).numpy())
pre_dW[i] = (whitened_B @ t(whitened_A[i])) / dsize
dW[i] = (B[i] @ t(A[i])) / dsize
loss = u.L2(err) / 2 / dsize
grad = u.flatten(dW[1:])
kfac_grad = u.flatten(pre_dW[1:])
return loss, A[n + 1], grad, kfac_grad
err = (A[3] - A[1])
rho_hat = tf.reduce_sum(A[2], axis=1, keep_dims=True)/dsize
# B[i] = backprops needed to compute gradient of W[i]
# B2[i] = backprops from sampled labels needed for natural gradient
B = [None]*(n+1)
B2 = [None]*(n+1)
B[n] = err*d_sigmoid(A[n+1])
sampled_labels_live = tf.random_normal((f(n), f(-1)), dtype=dtype, seed=0)
if use_fixed_labels:
sampled_labels_live = tf.ones(shape=(f(n), f(-1)), dtype=dtype)
sampled_labels = init_var(sampled_labels_live, "sampled_labels", noinit=True)
B2[n] = sampled_labels*d_sigmoid(A[n+1])
for i in range(n-1, -1, -1):
backprop = t(W[i+1]) @ B[i+1]
backprop2 = t(W[i+1]) @ B2[i+1]
if i == 1 and not drop_sparsity:
backprop += beta*d_kl(rho, rho_hat)
backprop2 += beta*d_kl(rho, rho_hat)
B[i] = backprop*d_sigmoid(A[i+1])
B2[i] = backprop2*d_sigmoid(A[i+1])
# dW[i] = gradient of W[i]
dW = [None]*(n+1)
pre_dW = [None]*(n+1) # preconditioned dW
pre_dW_stable = [None]*(n+1) # preconditioned stable dW
cov_A = [None]*(n+1) # covariance of activations[i]
cov_B2 = [None]*(n+1) # covariance of synthetic backprops[i]
vars_svd_A = [None]*(n+1)
def rotations1_gradient_test():
# https://www.wolframcloud.com/objects/ff6ecaf0-fccd-44e3-b26f-970d8fc2a57c
tf.reset_default_graph()
X0 = np.genfromtxt('data/large_rotations1_X0.csv', delimiter=",")
Y0 = np.genfromtxt('data/large_rotations1_Y0.csv', delimiter=",")
W0f = v2c_np(np.genfromtxt('data/large_rotations1_W0f.csv', delimiter=","))
fs = np.genfromtxt('data/large_rotations1_fs.csv',
delimiter=",").astype(np.int32)
n = len(fs) - 2 # number of layers
def f(i):
return fs[i + 1] # W[i] has shape f[i] x f[i-1]
dsize = X0.shape[1]
assert f(-1) == dsize
# load W0f and do shape checks (can remove)
W0s = u.unflatten_np(W0f,
fs[1:]) # Wf doesn't have first layer (data matrix)
W0s.insert(0, X0)
Wf_holder = tf.placeholder(dtype, shape=W0f.shape)
Wf = tf.Variable(Wf_holder, name="Wf")
Wf_copy = tf.Variable(Wf_holder, name="Wf_copy")
init_dict = {Wf_holder: W0f}
# Create W's
W = u.unflatten(Wf, fs[1:])
X = tf.constant(X0)
Y = tf.constant(Y0)
W.insert(0, X)
for (numpy_W, tf_W) in zip(W0s, W):
u.check_equal(numpy_W.shape, u.fix_shape(tf_W.shape))
# Create A's
# A[1] == X
A = [0] * (n + 2)
A[0] = u.Identity(dsize)
for i in range(n + 1):
A[i + 1] = tf.matmul(W[i], A[i], name="A" + str(i + 1))
assert W[0].get_shape() == X0.shape
assert A[n + 1].get_shape() == X0.shape
assert A[1].get_shape() == X0.shape
err = Y - A[n + 1]
loss = tf.reduce_sum(tf.square(err)) / (2 * dsize)
lr0 = np.genfromtxt('data/large_rotations1_gradient_lr.csv')
lr = tf.Variable(lr0, dtype=dtype)
# Create B's
B = [0] * (n + 1)
B[n] = -err / dsize
for i in range(n - 1, -1, -1):
B[i] = t(W[i + 1]) @ B[i + 1]
# create dW's
dW = [0] * (n + 1)
for i in range(n + 1):
dW[i] = tf.matmul(B[i], tf.transpose(A[i]), name="dW" + str(i))
del dW[0] # get rid of W[0] update
dWf = tf.concat([u.vec(dWi) for dWi in dW], axis=0)
Wf_new = Wf - lr * dWf
train_op1 = Wf_copy.assign(Wf_new)
train_op2 = Wf.assign(Wf_copy)
sess = tf.Session()
sess.run(tf.global_variables_initializer(), feed_dict=init_dict)
expected_losses = np.loadtxt("data/large_rotations1_gradient_losses.csv",
delimiter=",")
observed_losses = []
# from accompanying notebook
# {0.102522, 0.028124, 0.00907214, 0.00418929, 0.00293379,
for i in range(10):
observed_losses.append(sess.run([loss])[0])
sess.run(train_op1)
sess.run(train_op2)
u.check_equal(observed_losses, expected_losses)
def model_creator(batch_size, name="default", dtype=np.float32):
"""Create MNIST autoencoder model. Dataset is part of model."""
model = Model(name)
def get_batch_size(data):
if isinstance(data, IndexedGrad):
return int(data.live[0].shape[1])
else:
return int(data.shape[1])
init_dict = {}
global_vars = []
local_vars = []
# TODO: factor out to reuse between scripts
# TODO: change feed_dict logic to reuse value provided to VarStruct
# current situation makes reinitialization of global variable change
# it's value, counterinituitive
def init_var(val, name, is_global=False):
"""Helper to create variables with numpy or TF initial values."""
if isinstance(val, tf.Tensor):
var = u.get_variable(name=name, initializer=val, reuse=is_global)
else:
val = np.array(val)
assert u.is_numeric(val), "Non-numeric type."
var_struct = u.get_var(name=name, initializer=val, reuse=is_global)
holder = var_struct.val_
init_dict[holder] = val
var = var_struct.var
if is_global:
global_vars.append(var)
else:
local_vars.append(var)
return var
# TODO: get rid of purely_relu
def nonlin(x):
if purely_relu:
return tf.nn.relu(x)
elif purely_linear:
return tf.identity(x)
else:
return tf.sigmoid(x)
# TODO: rename into "nonlin_d"
def d_nonlin(y):
if purely_relu:
return u.relu_mask(y)
elif purely_linear:
return 1
else:
return y * (1 - y)
patches = train_images[:, :args.batch_size]
test_patches = test_images[:, :args.batch_size]
if args.dataset == 'cifar':
input_dim = 3 * 32 * 32
elif args.dataset == 'mnist':
input_dim = 28 * 28
else:
assert False
if release_name == 'kfac_tiny':
fs = [args.batch_size, input_dim, 196, input_dim]
else:
fs = [
args.batch_size, input_dim, 1024, 1024, 1024, 196, 1024, 1024,
1024, input_dim
]
def f(i):
return fs[i + 1] # W[i] has shape f[i] x f[i-1]
n = len(fs) - 2
# Full dataset from which new batches are sampled
X_full = init_var(train_images, "X_full", is_global=True)
X = init_var(patches, "X", is_global=False) # stores local batch per model
W = [None] * n
W.insert(0, X)
A = [None] * (n + 2)
A[1] = W[0]
for i in range(1, n + 1):
init_val = ng_init(f(i), f(i - 1)).astype(dtype)
W[i] = init_var(init_val, "W_%d" % (i, ), is_global=True)
A[i + 1] = nonlin(kfac_lib.matmul(W[i], A[i]))
err = A[n + 1] - A[1]
model.loss = u.L2(err) / (2 * get_batch_size(err))
# create test error eval
layer0 = init_var(test_patches, "X_test", is_global=True)
layer = layer0
for i in range(1, n + 1):
layer = nonlin(W[i] @ layer)
verr = (layer - layer0)
model.vloss = u.L2(verr) / (2 * get_batch_size(verr))
# manually compute backprop to use for sanity checking
B = [None] * (n + 1)
B2 = [None] * (n + 1)
B[n] = err * d_nonlin(A[n + 1])
_sampled_labels_live = tf.random_normal((f(n), f(-1)), dtype=dtype, seed=0)
if args.fixed_labels:
_sampled_labels_live = tf.ones(shape=(f(n), f(-1)), dtype=dtype)
_sampled_labels = init_var(_sampled_labels_live,
"to_be_deleted",
is_global=False)
B2[n] = _sampled_labels * d_nonlin(A[n + 1])
for i in range(n - 1, -1, -1):
backprop = t(W[i + 1]) @ B[i + 1]
B[i] = backprop * d_nonlin(A[i + 1])
backprop2 = t(W[i + 1]) @ B2[i + 1]
B2[i] = backprop2 * d_nonlin(A[i + 1])
# cov_A = [None]*(n+1) # covariance of activations[i]
# cov_B2 = [None]*(n+1) # covariance of synthetic backprops[i]
# vars_svd_A = [None]*(n+1)
# vars_svd_B2 = [None]*(n+1)
# dW = [None]*(n+1)
# pre_dW = [None]*(n+1) # preconditioned dW
# todo: decouple initial value from covariance update
# # maybe need start with identity and do running average
# for i in range(1,n+1):
# if regularized_svd:
# cov_A[i] = init_var(A[i]@t(A[i])/args.batch_size+args.Lambda*u.Identity(f(i-1)), "cov_A%d"%(i,))
# cov_B2[i] = init_var(B2[i]@t(B2[i])/args.batch_size+args.Lambda*u.Identity(f(i)), "cov_B2%d"%(i,))
# else:
# cov_A[i] = init_var(A[i]@t(A[i])/args.batch_size, "cov_A%d"%(i,))
# cov_B2[i] = init_var(B2[i]@t(B2[i])/args.batch_size, "cov_B2%d"%(i,))
# vars_svd_A[i] = u.SvdWrapper(cov_A[i],"svd_A_%d"%(i,), do_inverses=False)
# vars_svd_B2[i] = u.SvdWrapper(cov_B2[i],"svd_B2_%d"%(i,), do_inverses=False)
# whitened_A = u.cached_inverse(vars_svd_A[i], args.Lambda) @ A[i]
# whitened_B = u.cached_inverse(vars_svd_B2[i], args.Lambda) @ B[i]
# dW[i] = (B[i] @ t(A[i]))/args.batch_size
# pre_dW[i] = (whitened_B @ t(whitened_A))/args.batch_size
sampled_labels_live = A[n + 1] + tf.random_normal(
(f(n), f(-1)), dtype=dtype, seed=0)
if args.fixed_labels:
sampled_labels_live = A[n + 1] + tf.ones(shape=(f(n), f(-1)),
dtype=dtype)
sampled_labels = init_var(sampled_labels_live,
"sampled_labels",
is_global=False)
err2 = A[n + 1] - sampled_labels
model.loss2 = u.L2(err2) / (2 * args.batch_size)
model.global_vars = global_vars
model.local_vars = local_vars
model.trainable_vars = W[1:]
# todo, we have 3 places where model step is tracked, reduce
model.step = init_var(u.as_int32(0), "step", is_global=False)
advance_step_op = model.step.assign_add(1)
assert get_batch_size(X_full) % args.batch_size == 0
batches_per_dataset = (get_batch_size(X_full) // args.batch_size)
batch_idx = tf.mod(model.step, batches_per_dataset)
start_idx = batch_idx * args.batch_size
advance_batch_op = X.assign(X_full[:,
start_idx:start_idx + args.batch_size])
def advance_batch():
# print("Step for model(%s) is %s"%(model.name, u.eval(model.step)))
sess = u.get_default_session()
# TODO: get rid of _sampled_labels
sessrun([sampled_labels.initializer, _sampled_labels.initializer])
if args.advance_batch:
sessrun(advance_batch_op)
sessrun(advance_step_op)
model.advance_batch = advance_batch
# TODO: refactor this to take initial values out of Var struct
#global_init_op = tf.group(*[v.initializer for v in global_vars])
global_init_ops = [v.initializer for v in global_vars]
global_init_op = tf.group(*[v.initializer for v in global_vars])
global_init_query_ops = [
tf.logical_not(tf.is_variable_initialized(v)) for v in global_vars
]
def initialize_global_vars(verbose=False, reinitialize=False):
"""If reinitialize is false, will not reinitialize variables already
initialized."""
sess = u.get_default_session()
if not reinitialize:
uninited = sessrun(global_init_query_ops)
# use numpy boolean indexing to select list of initializers to run
to_initialize = list(np.asarray(global_init_ops)[uninited])
else:
to_initialize = global_init_ops
if verbose:
print("Initializing following:")
for v in to_initialize:
print(" " + v.name)
sessrun(to_initialize, feed_dict=init_dict)
model.initialize_global_vars = initialize_global_vars
# didn't quite work (can't initialize var in same run call as deps likely)
# enforce that batch is initialized before everything
# except fake labels opa
# for v in local_vars:
# if v != X and v != sampled_labels and v != _sampled_labels:
# print("Adding dep %s on %s"%(v.initializer.name, X.initializer.name))
# u.add_dep(v.initializer, on_op=X.initializer)
local_init_op = tf.group(*[v.initializer for v in local_vars],
name="%s_localinit" % (model.name))
print("Local vars:")
for v in local_vars:
print(v.name)
def initialize_local_vars():
sess = u.get_default_session()
sessrun(_sampled_labels.initializer, feed_dict=init_dict)
sessrun(X.initializer, feed_dict=init_dict)
sessrun(local_init_op, feed_dict=init_dict)
model.initialize_local_vars = initialize_local_vars
return model
def rotations2_newton_kfac():
tf.reset_default_graph()
# override kr with no-shape-inferring version
def kr(A, B):
return u.kronecker(A, B, do_shape_inference=False)
X0 = np.genfromtxt('data/large_rotations2_X0.csv', delimiter=",")
Y0 = np.genfromtxt('data/large_rotations2_Y0.csv', delimiter=",")
W0f = v2c_np(np.genfromtxt('data/large_rotations2_W0f.csv', delimiter=","))
fs = np.genfromtxt('data/large_rotations2_fs.csv',
delimiter=",").astype(np.int32)
n = len(fs) - 2 # number of layers
def f(i):
return fs[i + 1] # W[i] has shape f[i] x f[i-1]
dsize = X0.shape[1]
assert f(-1) == dsize
def f(i):
return fs[i + 1] # W[i] has shape f[i] x f[i-1]
dsize = X0.shape[1]
assert f(-1) == dsize
# load W0f and do shape checks (can remove)
W0s = u.unflatten_np(W0f,
fs[1:]) # Wf doesn't have first layer (data matrix)
W0s.insert(0, X0)
Wf_holder = tf.placeholder(dtype, shape=W0f.shape)
Wf = tf.Variable(Wf_holder, name="Wf")
Wf_copy = tf.Variable(Wf_holder, name="Wf_copy")
init_dict = {Wf_holder: W0f}
# Create W's
W = u.unflatten(Wf, fs[1:])
X = tf.constant(X0)
Y = tf.constant(Y0)
W.insert(0, X)
for (numpy_W, tf_W) in zip(W0s, W):
u.check_equal(numpy_W.shape, u.fix_shape(tf_W.shape))
# Create A's
# A[1] == X
A = [0] * (n + 2)
A[0] = u.Identity(dsize)
for i in range(n + 1):
A[i + 1] = tf.matmul(W[i], A[i], name="A" + str(i + 1))
assert W[0].get_shape() == X0.shape
assert A[n + 1].get_shape() == X0.shape
assert A[1].get_shape() == X0.shape
err = Y - A[n + 1]
loss = tf.reduce_sum(tf.square(err)) / (2 * dsize)
lr = tf.Variable(0.1, dtype=dtype, name="learning_rate")
# Create B's
B = [0] * (n + 1)
B[n] = -err / dsize
Bn = [0] * (n + 1) # Newton-modified backprop
Bn[n] = u.Identity(f(n))
for i in range(n - 1, -1, -1):
B[i] = t(W[i + 1]) @ B[i + 1]
Bn[i] = t(W[i + 1]) @ Bn[i + 1]
# inverse Hessian blocks
iblocks = u.empty_grid(n + 1, n + 1)
for i in range(1, n + 1):
for j in range(1, n + 1):
# reuse Hess tensor calculation in order to get off-diag block sizes
dummy_term = kr(A[i] @ t(A[j]), Bn[i] @ t(Bn[j])) / dsize
if i == j:
acov = A[i] @ t(A[j])
bcov = (Bn[i] @ t(Bn[j])) / dsize
term = kr(u.pseudo_inverse(acov), u.pseudo_inverse(bcov))
else:
term = tf.zeros(shape=dummy_term.get_shape(), dtype=dtype)
iblocks[i][j] = term
# remove leftmost blocks (those are with respect to W[0] which is input)
del iblocks[0]
for row in iblocks:
del row[0]
ihess = u.concat_blocks(iblocks)
sess = tf.Session()
sess.run(tf.global_variables_initializer(), feed_dict=init_dict)
# create dW's
dW = [0] * (n + 1)
for i in range(n + 1):
dW[i] = tf.matmul(B[i], tf.transpose(A[i]), name="dW" + str(i))
del dW[0] # get rid of W[0] update
dWf = tf.concat([u.vec(dWi) for dWi in dW], axis=0)
Wf_new = Wf - lr * ihess @ dWf
train_op1 = Wf_copy.assign(Wf_new)
train_op2 = Wf.assign(Wf_copy)
observed_losses = []
elapsed_times = []
u.reset_time()
for i in range(10):
loss0 = sess.run([loss])[0]
print(loss0)
observed_losses.append(loss0)
sess.run(train_op1)
sess.run(train_op2)
u.record_time()
u.summarize_time()
u.summarize_graph()
def rotations2_newton_bd():
# override kr with no-shape-inferring version
def kr(A, B):
return u.kronecker(A, B, do_shape_inference=False)
tf.reset_default_graph()
X0 = np.genfromtxt('data/large_rotations2_X0.csv', delimiter=",")
Y0 = np.genfromtxt('data/large_rotations2_Y0.csv', delimiter=",")
W0f = v2c_np(np.genfromtxt('data/large_rotations2_W0f.csv', delimiter=","))
fs = np.genfromtxt('data/large_rotations2_fs.csv',
delimiter=",").astype(np.int32)
n = len(fs) - 2 # number of layers
def f(i):
return fs[i + 1] # W[i] has shape f[i] x f[i-1]
dsize = X0.shape[1]
assert f(-1) == dsize
# load W0f and do shape checks (can remove)
W0s = u.unflatten_np(W0f,
fs[1:]) # Wf doesn't have first layer (data matrix)
W0s.insert(0, X0)
Wf_holder = tf.placeholder(dtype, shape=W0f.shape)
Wf = tf.Variable(Wf_holder, name="Wf")
Wf_copy = tf.Variable(Wf_holder, name="Wf_copy")
init_dict = {Wf_holder: W0f}
# Create W's
W = u.unflatten(Wf, fs[1:])
X = tf.constant(X0)
Y = tf.constant(Y0)
W.insert(0, X)
for (numpy_W, tf_W) in zip(W0s, W):
u.check_equal(numpy_W.shape, u.fix_shape(tf_W.shape))
# Create A's
# A[1] == X
A = [0] * (n + 2)
A[0] = u.Identity(dsize)
for i in range(n + 1):
A[i + 1] = tf.matmul(W[i], A[i], name="A" + str(i + 1))
assert W[0].get_shape() == X0.shape
assert A[n + 1].get_shape() == X0.shape
assert A[1].get_shape() == X0.shape
err = Y - A[n + 1]
loss = tf.reduce_sum(tf.square(err)) / (2 * dsize)
lr = tf.Variable(0.1, dtype=dtype, name="learning_rate")
# Create B's
B = [0] * (n + 1)
B[n] = -err / dsize
Bn = [0] * (n + 1) # Newton-modified backprop
Bn[n] = u.Identity(f(n))
for i in range(n - 1, -1, -1):
B[i] = t(W[i + 1]) @ B[i + 1]
Bn[i] = t(W[i + 1]) @ Bn[i + 1]
# Create U's
U = [list(range(n + 1)) for _ in range(n + 1)]
for bottom in range(n + 1):
for top in range(n + 1):
if bottom > top:
prod = u.Identity(f(top))
else:
prod = u.Identity(f(bottom - 1))
for i in range(bottom, top + 1):
prod = prod @ t(W[i])
U[bottom][top] = prod
# Block i, j gives hessian block between layer i and layer j
blocks = [list(range(n + 1)) for _ in range(n + 1)]
for i in range(1, n + 1):
for j in range(1, n + 1):
term1 = kr(A[i] @ t(A[j]), Bn[i] @ t(Bn[j])) / dsize
if i == j:
term2 = tf.zeros((f(i) * f(i - 1), f(i) * f(i - 1)),
dtype=dtype)
elif i < j:
term2 = kr(A[i] @ t(B[j]), U[i + 1][j - 1])
else:
term2 = kr(t(U[j + 1][i - 1]), B[i] @ t(A[j]))
blocks[i][j] = term1 + term2 @ Kmat(f(j), f(j - 1))
# remove leftmost blocks (those are with respect to W[0] which is input)
del blocks[0]
for row in blocks:
del row[0]
ihess = u.concat_blocks(u.block_diagonal_inverse(blocks))
sess = tf.Session()
sess.run(tf.global_variables_initializer(), feed_dict=init_dict)
# create dW's
dW = [0] * (n + 1)
for i in range(n + 1):
dW[i] = tf.matmul(B[i], tf.transpose(A[i]), name="dW" + str(i))
del dW[0] # get rid of W[0] update
dWf = tf.concat([u.vec(dWi) for dWi in dW], axis=0)
Wf_new = Wf - lr * ihess @ dWf
train_op1 = Wf_copy.assign(Wf_new)
train_op2 = Wf.assign(Wf_copy)
observed_losses = []
u.reset_time()
for i in range(20):
loss0 = sess.run([loss])[0]
print(loss0)
observed_losses.append(loss0)
sess.run(train_op1)
sess.run(train_op2)
u.record_time()
u.summarize_time()
u.summarize_graph()
def cost_and_grad(W0f=None,
fs=None,
lambda_=3e-3,
rho=0.1,
beta=3,
X0=None,
lr=0.1):
"""Construct sparse autoencoder loss and gradient.
Args:
W0f: initial value of weights (flattened representation)
fs: list of sizes [dsize, visible, hidden, visible]
sparsity_param: global feature sparsity target
beta: weight on sparsity penalty
X0: value of X (aka W[0])
Returns:
cost, train_step
"""
np.random.seed(0)
tf.set_random_seed(0)
dtype = np.float32
if not fs:
fs = [dsize, 28 * 28, 196, 28 * 28]
if not W0f:
W0f = W_uniform(fs[2], fs[3])
rho = tf.constant(rho, dtype=dtype)
def f(i):
return fs[i + 1] # W[i] has shape f[i] x f[i-1]
dsize = f(-1)
n = len(fs) - 2
init_dict = {}
def init_var(val, name, trainable=True):
holder = tf.placeholder(dtype, shape=val.shape, name=name + "_holder")
var = tf.Variable(holder, name=name + "_var", trainable=trainable)
init_dict[holder] = val
return var
Wf = init_var(W0f, "Wf")
Wf_copy = init_var(W0f, "Wf_copy")
W = u.unflatten(Wf, fs[1:])
X = init_var(X0, "X", False)
W.insert(0, X)
def sigmoid(x):
return tf.sigmoid(x)
def d_sigmoid(y):
return y * (1 - y)
def kl(x, y):
return x * tf.log(x / y) + (1 - x) * tf.log((1 - x) / (1 - y))
def d_kl(x, y):
return (1 - x) / (1 - y) - x / y
# A[i] = activations needed to compute gradient of W[i]
# A[n+1] = network output
A = [None] * (n + 2)
A[0] = u.Identity(dsize, dtype=dtype)
A[1] = W[0]
for i in range(1, n + 1):
A[i + 1] = sigmoid(W[i] @ A[i])
# reconstruction error and sparsity error
err = (A[3] - A[1])
rho_hat = tf.reduce_sum(A[2], axis=1, keep_dims=True) / dsize
# B[i] = backprops needed to compute gradient of W[i]
B = [None] * (n + 1)
B[n] = err * d_sigmoid(A[n + 1])
for i in range(n - 1, -1, -1):
backprop = t(W[i + 1]) @ B[i + 1]
if i == 1:
backprop += beta * d_kl(rho, rho_hat)
B[i] = backprop * d_sigmoid(A[i + 1])
# dW[i] = gradient of W[i]
dW = [None] * (n + 1)
for i in range(n + 1):
dW[i] = (B[i] @ t(A[i])) / dsize
# Cost function
reconstruction = u.L2(err) / (2 * dsize)
sparsity = beta * tf.reduce_sum(kl(rho, rho_hat))
L2 = (lambda_ / 2) * (u.L2(W[1]) + u.L2(W[1]))
cost = reconstruction + sparsity + L2
grad = u.flatten(dW[1:])
copy_op = Wf_copy.assign(Wf - lr * grad)
with tf.control_dependencies([copy_op]):
train_op = Wf.assign(Wf_copy)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer(), feed_dict=init_dict)
return cost, train_op
def model_creator(batch_size, name='defaultmodel', dtype=np.float32):
"""Create MNIST autoencoder model. Dataset is part of model."""
global hack_global_init_dict
model = Model(name)
# TODO: actually use batch_size
init_dict = {} # todo: rename to feed_dict?
global_vars = []
local_vars = []
# TODO: rename to make_var
def init_var(val, name, is_global=False):
"""Helper to create variables with numpy or TF initial values."""
if isinstance(val, tf.Tensor):
var = u.get_variable(name=name, initializer=val, reuse=is_global)
else:
val = np.array(val)
assert u.is_numeric(val), "Non-numeric type."
var_struct = u.get_var(name=name, initializer=val, reuse=is_global)
holder = var_struct.val_
init_dict[holder] = val
var = var_struct.var
if is_global:
global_vars.append(var)
else:
local_vars.append(var)
return var
# TODO: get rid of purely_relu
def nonlin(x):
if purely_relu:
return tf.nn.relu(x)
elif purely_linear:
return tf.identity(x)
else:
return tf.sigmoid(x)
# TODO: rename into "nonlin_d"
def d_nonlin(y):
if purely_relu:
return u.relu_mask(y)
elif purely_linear:
return 1
else:
return y * (1 - y)
train_images = load_MNIST.load_MNIST_images(
'data/train-images-idx3-ubyte').astype(dtype)
patches = train_images[:, :batch_size]
fs = [batch_size, 28 * 28, 196, 28 * 28]
def f(i):
return fs[i + 1] # W[i] has shape f[i] x f[i-1]
n = len(fs) - 2
X = init_var(patches, "X", is_global=False)
W = [None] * n
W.insert(0, X)
A = [None] * (n + 2)
A[1] = W[0]
W0f_old = W_uniform(fs[2],
fs[3]).astype(dtype) # to match previous generation
W0s_old = u.unflatten(W0f_old, fs[1:]) # perftodo: this creates transposes
for i in range(1, n + 1):
# temp = init_var(ng_init(f(i), f(i-1)), "W_%d"%(i,), is_global=True)
# init_val1 = W0s_old[i-1]
init_val = ng_init(f(i), f(i - 1)).astype(dtype)
W[i] = init_var(init_val, "W_%d" % (i, ), is_global=True)
A[i + 1] = nonlin(kfac_lib.matmul(W[i], A[i]))
err = A[n + 1] - A[1]
# manually compute backprop to use for sanity checking
B = [None] * (n + 1)
B2 = [None] * (n + 1)
B[n] = err * d_nonlin(A[n + 1])
_sampled_labels_live = tf.random_normal((f(n), f(-1)), dtype=dtype, seed=0)
if use_fixed_labels:
_sampled_labels_live = tf.ones(shape=(f(n), f(-1)), dtype=dtype)
_sampled_labels = init_var(_sampled_labels_live,
"to_be_deleted",
is_global=False)
B2[n] = _sampled_labels * d_nonlin(A[n + 1])
for i in range(n - 1, -1, -1):
backprop = t(W[i + 1]) @ B[i + 1]
B[i] = backprop * d_nonlin(A[i + 1])
backprop2 = t(W[i + 1]) @ B2[i + 1]
B2[i] = backprop2 * d_nonlin(A[i + 1])
cov_A = [None] * (n + 1) # covariance of activations[i]
cov_B2 = [None] * (n + 1) # covariance of synthetic backprops[i]
vars_svd_A = [None] * (n + 1)
vars_svd_B2 = [None] * (n + 1)
dW = [None] * (n + 1)
dW2 = [None] * (n + 1)
pre_dW = [None] * (n + 1) # preconditioned dW
for i in range(1, n + 1):
if regularized_svd:
cov_A[i] = init_var(
A[i] @ t(A[i]) / batch_size + LAMBDA * u.Identity(f(i - 1)),
"cov_A%d" % (i, ))
cov_B2[i] = init_var(
B2[i] @ t(B2[i]) / batch_size + LAMBDA * u.Identity(f(i)),
"cov_B2%d" % (i, ))
else:
cov_A[i] = init_var(A[i] @ t(A[i]) / batch_size, "cov_A%d" % (i, ))
cov_B2[i] = init_var(B2[i] @ t(B2[i]) / batch_size,
"cov_B2%d" % (i, ))
vars_svd_A[i] = u.SvdWrapper(cov_A[i], "svd_A_%d" % (i, ))
vars_svd_B2[i] = u.SvdWrapper(cov_B2[i], "svd_B2_%d" % (i, ))
if use_tikhonov:
whitened_A = u.regularized_inverse3(vars_svd_A[i], L=LAMBDA) @ A[i]
whitened_B2 = u.regularized_inverse3(vars_svd_B2[i],
L=LAMBDA) @ B[i]
else:
whitened_A = u.pseudo_inverse2(vars_svd_A[i]) @ A[i]
whitened_B2 = u.pseudo_inverse2(vars_svd_B2[i]) @ B[i]
dW[i] = (B[i] @ t(A[i])) / batch_size
dW2[i] = B[i] @ t(A[i])
pre_dW[i] = (whitened_B2 @ t(whitened_A)) / batch_size
# model.extra['A'] = A
# model.extra['B'] = B
# model.extra['B2'] = B2
# model.extra['cov_A'] = cov_A
# model.extra['cov_B2'] = cov_B2
# model.extra['vars_svd_A'] = vars_svd_A
# model.extra['vars_svd_B2'] = vars_svd_B2
# model.extra['W'] = W
# model.extra['dW'] = dW
# model.extra['dW2'] = dW2
# model.extra['pre_dW'] = pre_dW
model.loss = u.L2(err) / (2 * batch_size)
sampled_labels_live = A[n + 1] + tf.random_normal(
(f(n), f(-1)), dtype=dtype, seed=0)
if use_fixed_labels:
sampled_labels_live = A[n + 1] + tf.ones(shape=(f(n), f(-1)),
dtype=dtype)
sampled_labels = init_var(sampled_labels_live,
"sampled_labels",
is_global=False)
err2 = A[n + 1] - sampled_labels
model.loss2 = u.L2(err2) / (2 * batch_size)
model.global_vars = global_vars
model.local_vars = local_vars
model.trainable_vars = W[1:]
def advance_batch():
sess = tf.get_default_session()
# TODO: get rid of _sampled_labels
sess.run([sampled_labels.initializer, _sampled_labels.initializer])
model.advance_batch = advance_batch
global_init_op = tf.group(*[v.initializer for v in global_vars])
def initialize_global_vars():
sess = tf.get_default_session()
sess.run(global_init_op, feed_dict=init_dict)
model.initialize_global_vars = initialize_global_vars
local_init_op = tf.group(*[v.initializer for v in local_vars])
def initialize_local_vars():
sess = tf.get_default_session()
sess.run(X.initializer, feed_dict=init_dict) # A's depend on X
sess.run(_sampled_labels.initializer, feed_dict=init_dict)
sess.run(local_init_op, feed_dict=init_dict)
model.initialize_local_vars = initialize_local_vars
hack_global_init_dict = init_dict
return model
def simple_newton_kfac_test():
tf.reset_default_graph()
X0 = np.genfromtxt('data/rotations_simple_X0.csv',
delimiter= ",")
Y0 = np.genfromtxt('data/rotations_simple_Y0.csv',
delimiter= ",")
W0f = v2c_np(np.genfromtxt('data/rotations_simple_W0f.csv',
delimiter= ","))
assert W0f.shape == (8, 1)
fs = np.genfromtxt('data/rotations_simple_fs.csv',
delimiter= ",").astype(np.int32)
n = len(fs)-2 # number of layers
u.check_equal(fs, [10,2,2,2])
def f(i): return fs[i+1] # W[i] has shape f[i] x f[i-1]
dsize = X0.shape[1]
assert f(-1) == dsize
# load W0f and do shape checks (can remove)
W0s = u.unflatten_np(W0f, fs[1:]) # Wf doesn't have first layer (data matrix)
W0s.insert(0, X0)
Wf_holder = tf.placeholder(dtype, shape=W0f.shape)
Wf = tf.Variable(Wf_holder, name="Wf")
Wf_copy = tf.Variable(Wf_holder, name="Wf_copy")
init_dict = {Wf_holder: W0f}
# Create W's
W = u.unflatten(Wf, fs[1:])
X = tf.constant(X0)
Y = tf.constant(Y0)
W.insert(0, X)
for (numpy_W, tf_W) in zip(W0s, W):
u.check_equal(numpy_W.shape, u.fix_shape(tf_W.shape))
# Create A's
# A[1] == X
A = [0]*(n+2)
A[0] = u.Identity(dsize)
for i in range(n+1):
A[i+1] = tf.matmul(W[i], A[i], name="A"+str(i+1))
assert W[0].get_shape() == X0.shape
assert A[n+1].get_shape() == X0.shape
assert A[1].get_shape() == X0.shape
err = Y - A[n+1]
loss = tf.reduce_sum(tf.square(err))/(2*dsize)
lr = tf.Variable(0.5, dtype=dtype, name="learning_rate")
# Create B's
B = [0]*(n+1)
B[n] = -err/dsize
Bn = [0]*(n+1) # Newton-modified backprop
Bn[n] = u.Identity(f(n))
for i in range(n-1, -1, -1):
B[i] = t(W[i+1]) @ B[i+1]
Bn[i] = t(W[i+1]) @ Bn[i+1]
# inverse Hessian blocks
iblocks = u.empty_grid(n+1, n+1)
for i in range(1, n+1):
for j in range(1, n+1):
# reuse Hess tensor calculation in order to get off-diag block sizes
dummy_term = kr(A[i] @ t(A[j]), Bn[i] @ t(Bn[j])) / dsize;
if i == j:
acov = A[i] @ t(A[j])
bcov = Bn[i] @ t(Bn[j]) / dsize;
term = kr(u.pseudo_inverse(acov), u.pseudo_inverse(bcov))
else:
term = tf.zeros(shape=dummy_term.get_shape(), dtype=dtype)
iblocks[i][j]=term
# remove leftmost blocks (those are with respect to W[0] which is input)
del iblocks[0]
for row in iblocks:
del row[0]
ihess = u.concat_blocks(iblocks)
sess = tf.Session()
sess.run(tf.global_variables_initializer(), feed_dict=init_dict)
# create dW's
dW = [0]*(n+1)
for i in range(n+1):
dW[i] = tf.matmul(B[i], tf.transpose(A[i]), name="dW"+str(i))
del dW[0] # get rid of W[0] update
dWf = tf.concat([u.vec(dWi) for dWi in dW], axis=0)
Wf_new = Wf - lr * ihess @ dWf
train_op1 = Wf_copy.assign(Wf_new)
train_op2 = Wf.assign(Wf_copy)
expected_losses = np.loadtxt("data/rotations_simple_newtonkfac_losses.csv",
delimiter= ",")
observed_losses = []
# from accompanying notebook
# {0.0111498, 0.0000171591, 4.11445*10^-11, 2.33653*10^-22,
# 6.88354*10^-33,
for i in range(10):
observed_losses.append(sess.run([loss])[0])
sess.run(train_op1)
sess.run(train_op2)
u.check_equal(observed_losses, expected_losses)
def simple_newton_bd_test():
tf.reset_default_graph()
X0 = np.genfromtxt('data/rotations_simple_X0.csv',
delimiter= ",")
Y0 = np.genfromtxt('data/rotations_simple_Y0.csv',
delimiter= ",")
W0f = v2c_np(np.genfromtxt('data/rotations_simple_W0f.csv',
delimiter= ","))
assert W0f.shape == (8, 1)
fs = np.genfromtxt('data/rotations_simple_fs.csv',
delimiter= ",").astype(np.int32)
n = len(fs)-2 # number of layers
u.check_equal(fs, [10,2,2,2])
def f(i): return fs[i+1] # W[i] has shape f[i] x f[i-1]
dsize = X0.shape[1]
assert f(-1) == dsize
# load W0f and do shape checks (can remove)
W0s = u.unflatten_np(W0f, fs[1:]) # Wf doesn't have first layer (data matrix)
W0s.insert(0, X0)
Wf_holder = tf.placeholder(dtype, shape=W0f.shape)
Wf = tf.Variable(Wf_holder, name="Wf")
Wf_copy = tf.Variable(Wf_holder, name="Wf_copy")
init_dict = {Wf_holder: W0f}
# Create W's
W = u.unflatten(Wf, fs[1:])
X = tf.constant(X0)
Y = tf.constant(Y0)
W.insert(0, X)
for (numpy_W, tf_W) in zip(W0s, W):
u.check_equal(numpy_W.shape, u.fix_shape(tf_W.shape))
# Create A's
# A[1] == X
A = [0]*(n+2)
A[0] = u.Identity(dsize)
for i in range(n+1):
A[i+1] = tf.matmul(W[i], A[i], name="A"+str(i+1))
assert W[0].get_shape() == X0.shape
assert A[n+1].get_shape() == X0.shape
assert A[1].get_shape() == X0.shape
err = Y - A[n+1]
loss = tf.reduce_sum(tf.square(err))/(2*dsize)
lr = tf.Variable(0.5, dtype=dtype, name="learning_rate")
# Create B's
B = [0]*(n+1)
B[n] = -err/dsize
Bn = [0]*(n+1) # Newton-modified backprop
Bn[n] = u.Identity(f(n))
for i in range(n-1, -1, -1):
B[i] = t(W[i+1]) @ B[i+1]
Bn[i] = t(W[i+1]) @ Bn[i+1]
# Create U's
U = [list(range(n+1)) for _ in range(n+1)]
for bottom in range(n+1):
for top in range(n+1):
if bottom > top:
prod = u.Identity(f(top))
else:
prod = u.Identity(f(bottom-1))
for i in range(bottom, top+1):
prod = prod@t(W[i])
U[bottom][top] = prod
# Block i, j gives hessian block between layer i and layer j
blocks = [list(range(n+1)) for _ in range(n+1)]
for i in range(1, n+1):
for j in range(1, n+1):
term1 = kr(A[i] @ t(A[j]), Bn[i] @ t(Bn[j])) / dsize;
if i == j:
term2 = tf.zeros((f(i)*f(i-1), f(i)*f(i-1)), dtype=dtype)
elif i < j:
term2 = kr(A[i] @ t(B[j]), U[i+1][j-1])
else:
term2 = kr(t(U[j+1][i-1]), B[i] @ t(A[j]))
blocks[i][j]=term1 + term2 @ Kmat(f(j), f(j-1))
# remove leftmost blocks (those are with respect to W[0] which is input)
del blocks[0]
for row in blocks:
del row[0]
#hess = u.concat_blocks(blocks)
ihess = u.concat_blocks(u.block_diagonal_inverse(blocks))
# ihess = u.pseudo_inverse(hess)
sess = tf.Session()
sess.run(tf.global_variables_initializer(), feed_dict=init_dict)
# create dW's
dW = [0]*(n+1)
for i in range(n+1):
dW[i] = tf.matmul(B[i], tf.transpose(A[i]), name="dW"+str(i))
del dW[0] # get rid of W[0] update
dWf = tf.concat([u.vec(dWi) for dWi in dW], axis=0)
Wf_new = Wf - lr * ihess @ dWf
train_op1 = Wf_copy.assign(Wf_new)
train_op2 = Wf.assign(Wf_copy)
expected_losses = np.loadtxt("data/rotations_simple_newtonbd_losses.csv",
delimiter= ",")
observed_losses = []
# from accompanying notebook
# 0.0111498, 0.0000171591, 4.11445*10^-11, 2.33652*10^-22,
# 1.21455*10^-32,
for i in range(10):
observed_losses.append(sess.run([loss])[0])
sess.run(train_op1)
sess.run(train_op2)
u.check_equal(observed_losses, expected_losses)
def relu_gradient_test():
tf.reset_default_graph()
X0 = np.genfromtxt('data/rotations_X0.csv',
delimiter= ",")
Y0 = np.genfromtxt('data/rotations_Y0.csv',
delimiter= ",")
W0f = v2c_np(np.genfromtxt('data/rotations_W0f.csv',
delimiter= ","))
assert W0f.shape == (8, 1)
fs = np.genfromtxt('data/rotations_relu_fs.csv',
delimiter= ",").astype(np.int32)
n = len(fs)-2 # number of layers
u.check_equal(fs, [4,2,2,2])
def f(i): return fs[i+1] # W[i] has shape f[i] x f[i-1]
dsize = X0.shape[1]
assert f(-1) == dsize
# load W0f and do shape checks (can remove)
W0s = u.unflatten_np(W0f, fs[1:]) # Wf doesn't have first layer (data matrix)
W0s.insert(0, X0)
Wf_holder = tf.placeholder(dtype, shape=W0f.shape)
Wf = tf.Variable(Wf_holder, name="Wf")
Wf_copy = tf.Variable(Wf_holder, name="Wf_copy")
init_dict = {Wf_holder: W0f}
# Create W's
W = u.unflatten(Wf, fs[1:])
X = tf.constant(X0, name="X0")
Y = tf.constant(Y0, name="Y0")
W.insert(0, X)
for (numpy_W, tf_W) in zip(W0s, W):
u.check_equal(numpy_W.shape, u.fix_shape(tf_W.shape))
# Create A's
# A[1] == X
A = [0]*(n+2)
A[0] = u.Identity(dsize)
for i in range(n+1):
if i == 0:
A[i+1] = X
else:
A[i+1] = tf.nn.relu(tf.matmul(W[i], A[i], name="A"+str(i+1)))
assert W[0].get_shape() == X0.shape
assert A[n+1].get_shape() == X0.shape
assert A[1].get_shape() == X0.shape
err = Y - A[n+1]
loss = tf.reduce_sum(tf.square(err))/(2*dsize)
lr = tf.Variable(0.1, dtype=dtype)
# Create B's
B = [0]*(n+1)
B[n] = (-err/dsize)*u.relu_mask(A[n+1])
for i in range(n-1, -1, -1):
B[i] = t(W[i+1]) @ B[i+1]
if i > 0: # there's no relu on first matrix
B[i] = B[i]*u.relu_mask(A[i+1])
# create dW's
dW = [0]*(n+1)
for i in range(n+1):
dW[i] = tf.matmul(B[i], tf.transpose(A[i]), name="dW"+str(i))
del dW[0] # get rid of W[0] update
dWf = tf.concat([u.vec(dWi) for dWi in dW], axis=0)
Wf_new = Wf - lr * dWf
train_op1 = Wf_copy.assign(Wf_new)
train_op2 = Wf.assign(Wf_copy)
sess = tf.Session()
sess.run(tf.global_variables_initializer(), feed_dict=init_dict)
expected_losses = np.loadtxt("data/rotations_relu_gradient_losses.csv",
delimiter= ",")
observed_losses = []
# From accompanying notebook
# {0.407751, 0.0683822, 0.0138657, 0.0039221, 0.00203637, 0.00164892,
# 0.00156137, 0.00153857, 0.00153051, 0.00152593}
for i in range(10):
observed_losses.append(sess.run([loss])[0])
sess.run(train_op1)
sess.run(train_op2)
u.check_equal(observed_losses, expected_losses)
def model_creator(batch_size, name="default", dtype=np.float32):
"""Create MNIST autoencoder model. Dataset is part of model."""
model = Model(name)
def get_batch_size(data):
if isinstance(data, IndexedGrad):
return int(data.live[0].shape[1])
else:
return int(data.shape[1])
init_dict = {}
global_vars = []
local_vars = []
# TODO: factor out to reuse between scripts
# TODO: change feed_dict logic to reuse value provided to VarStruct
# current situation makes reinitialization of global variable change
# it's value, counterinituitive
def init_var(val, name, is_global=False):
"""Helper to create variables with numpy or TF initial values."""
if isinstance(val, tf.Tensor):
var = u.get_variable(name=name, initializer=val, reuse=is_global)
else:
val = np.array(val)
assert u.is_numeric(val), "Non-numeric type."
var_struct = u.get_var(name=name, initializer=val, reuse=is_global)
holder = var_struct.val_
init_dict[holder] = val
var = var_struct.var
if is_global:
global_vars.append(var)
else:
local_vars.append(var)
return var
# TODO: get rid of purely_relu
def nonlin(x):
if purely_relu:
return tf.nn.relu(x)
elif purely_linear:
return tf.identity(x)
else:
return tf.sigmoid(x)
# TODO: rename into "nonlin_d"
def d_nonlin(y):
if purely_relu:
return u.relu_mask(y)
elif purely_linear:
return 1
else:
return y*(1-y)
patches = train_images[:,:args.batch_size];
test_patches = test_images[:,:args.batch_size];
if args.dataset == 'cifar':
input_dim = 3*32*32
elif args.dataset == 'mnist':
input_dim = 28*28
else:
assert False
fs = [args.batch_size, input_dim, 1024, 1024, 1024, 196, 1024, 1024, 1024,
input_dim]
def f(i): return fs[i+1] # W[i] has shape f[i] x f[i-1]
n = len(fs) - 2
# Full dataset from which new batches are sampled
X_full = init_var(train_images, "X_full", is_global=True)
X = init_var(patches, "X", is_global=False) # stores local batch per model
W = [None]*n
W.insert(0, X)
A = [None]*(n+2)
A[1] = W[0]
for i in range(1, n+1):
init_val = ng_init(f(i), f(i-1)).astype(dtype)
W[i] = init_var(init_val, "W_%d"%(i,), is_global=True)
A[i+1] = nonlin(kfac_lib.matmul(W[i], A[i]))
err = A[n+1] - A[1]
model.loss = u.L2(err) / (2 * get_batch_size(err))
# create test error eval
layer0 = init_var(test_patches, "X_test", is_global=True)
layer = layer0
for i in range(1, n+1):
layer = nonlin(W[i] @ layer)
verr = (layer - layer0)
model.vloss = u.L2(verr) / (2 * get_batch_size(verr))
# manually compute backprop to use for sanity checking
B = [None]*(n+1)
B2 = [None]*(n+1)
B[n] = err*d_nonlin(A[n+1])
_sampled_labels_live = tf.random_normal((f(n), f(-1)), dtype=dtype, seed=0)
if args.fixed_labels:
_sampled_labels_live = tf.ones(shape=(f(n), f(-1)), dtype=dtype)
_sampled_labels = init_var(_sampled_labels_live, "to_be_deleted",
is_global=False)
B2[n] = _sampled_labels*d_nonlin(A[n+1])
for i in range(n-1, -1, -1):
backprop = t(W[i+1]) @ B[i+1]
B[i] = backprop*d_nonlin(A[i+1])
backprop2 = t(W[i+1]) @ B2[i+1]
B2[i] = backprop2*d_nonlin(A[i+1])
cov_A = [None]*(n+1) # covariance of activations[i]
cov_B2 = [None]*(n+1) # covariance of synthetic backprops[i]
vars_svd_A = [None]*(n+1)
vars_svd_B2 = [None]*(n+1)
dW = [None]*(n+1)
dW2 = [None]*(n+1)
pre_dW = [None]*(n+1) # preconditioned dW
# todo: decouple initial value from covariance update
# maybe need start with identity and do running average
for i in range(1,n+1):
if regularized_svd:
cov_A[i] = init_var(A[i]@t(A[i])/args.batch_size+args.Lambda*u.Identity(f(i-1)), "cov_A%d"%(i,))
cov_B2[i] = init_var(B2[i]@t(B2[i])/args.batch_size+args.Lambda*u.Identity(f(i)), "cov_B2%d"%(i,))
else:
cov_A[i] = init_var(A[i]@t(A[i])/args.batch_size, "cov_A%d"%(i,))
cov_B2[i] = init_var(B2[i]@t(B2[i])/args.batch_size, "cov_B2%d"%(i,))
vars_svd_A[i] = u.SvdWrapper(cov_A[i],"svd_A_%d"%(i,))
vars_svd_B2[i] = u.SvdWrapper(cov_B2[i],"svd_B2_%d"%(i,))
if use_tikhonov:
whitened_A = u.regularized_inverse3(vars_svd_A[i],L=args.Lambda) @ A[i]
whitened_B2 = u.regularized_inverse3(vars_svd_B2[i],L=args.Lambda) @ B[i]
else:
whitened_A = u.pseudo_inverse2(vars_svd_A[i]) @ A[i]
whitened_B2 = u.pseudo_inverse2(vars_svd_B2[i]) @ B[i]
dW[i] = (B[i] @ t(A[i]))/args.batch_size
dW2[i] = B[i] @ t(A[i])
pre_dW[i] = (whitened_B2 @ t(whitened_A))/args.batch_size
sampled_labels_live = A[n+1] + tf.random_normal((f(n), f(-1)),
dtype=dtype, seed=0)
if args.fixed_labels:
sampled_labels_live = A[n+1]+tf.ones(shape=(f(n), f(-1)), dtype=dtype)
sampled_labels = init_var(sampled_labels_live, "sampled_labels", is_global=False)
err2 = A[n+1] - sampled_labels
model.loss2 = u.L2(err2) / (2 * args.batch_size)
model.global_vars = global_vars
model.local_vars = local_vars
model.trainable_vars = W[1:]
# todo, we have 3 places where model step is tracked, reduce
model.step = init_var(u.as_int32(0), "step", is_global=False)
advance_step_op = model.step.assign_add(1)
assert get_batch_size(X_full) % args.batch_size == 0
batches_per_dataset = (get_batch_size(X_full) // args.batch_size)
batch_idx = tf.mod(model.step, batches_per_dataset)
start_idx = batch_idx * args.batch_size
advance_batch_op = X.assign(X_full[:,start_idx:start_idx + args.batch_size])
def advance_batch():
print("Step for model(%s) is %s"%(model.name, u.eval(model.step)))
sess = u.get_default_session()
# TODO: get rid of _sampled_labels
sessrun([sampled_labels.initializer, _sampled_labels.initializer])
if args.advance_batch:
with u.timeit("advance_batch"):
sessrun(advance_batch_op)
sessrun(advance_step_op)
model.advance_batch = advance_batch
# TODO: refactor this to take initial values out of Var struct
#global_init_op = tf.group(*[v.initializer for v in global_vars])
global_init_ops = [v.initializer for v in global_vars]
global_init_op = tf.group(*[v.initializer for v in global_vars])
global_init_query_ops = [tf.logical_not(tf.is_variable_initialized(v))
for v in global_vars]
def initialize_global_vars(verbose=False, reinitialize=False):
"""If reinitialize is false, will not reinitialize variables already
initialized."""
sess = u.get_default_session()
if not reinitialize:
uninited = sessrun(global_init_query_ops)
# use numpy boolean indexing to select list of initializers to run
to_initialize = list(np.asarray(global_init_ops)[uninited])
else:
to_initialize = global_init_ops
if verbose:
print("Initializing following:")
for v in to_initialize:
print(" " + v.name)
sessrun(to_initialize, feed_dict=init_dict)
model.initialize_global_vars = initialize_global_vars
# didn't quite work (can't initialize var in same run call as deps likely)
# enforce that batch is initialized before everything
# except fake labels opa
# for v in local_vars:
# if v != X and v != sampled_labels and v != _sampled_labels:
# print("Adding dep %s on %s"%(v.initializer.name, X.initializer.name))
# u.add_dep(v.initializer, on_op=X.initializer)
local_init_op = tf.group(*[v.initializer for v in local_vars],
name="%s_localinit"%(model.name))
print("Local vars:")
for v in local_vars:
print(v.name)
def initialize_local_vars():
sess = u.get_default_session()
sessrun(_sampled_labels.initializer, feed_dict=init_dict)
sessrun(X.initializer, feed_dict=init_dict)
sessrun(local_init_op, feed_dict=init_dict)
model.initialize_local_vars = initialize_local_vars
return model
A[0] = u.Identity(dsize)
for i in range(n+1):
A[i+1] = tf.matmul(W[i], A[i], name="A"+str(i+1))
assert W[0].get_shape() == X0.shape
assert A[n+1].get_shape() == X0.shape
assert A[1].get_shape() == X0.shape
err = Y - A[n+1]
loss = tf.reduce_sum(tf.square(err))/(2*dsize)
# Create B's
B = [0]*(n+1)
B[n] = -err/dsize
for i in range(n-1, -1, -1):
B[i] = t(W[i+1]) @ B[i+1]
# create dW's
dW = [0]*(n+1)
for i in range(n+1):
dW[i] = tf.matmul(B[i], tf.transpose(A[i]), name="dW"+str(i))
del dW[0] # get rid of W[0] update
dWf = tf.concat([u.vec(dWi) for dWi in dW], axis=0)
lr_holder = tf.placeholder(dtype=dtype, shape=())
lr = tf.Variable(lr_holder, dtype=dtype)
# run tests
do_run_iters = 5
result = newton(1.0)
expected_result = [8.9023744225439743e-05, 0.060120791316053412, 0.0059295249954177918, 1.9856240803246437e-05, 2.7125563957575423e-10]
# A[n+1] = network output
A = [None] * (n + 2)
A[0] = u.Identity(dsize, dtype=dtype)
A[1] = W[0]
for i in range(1, n + 1):
A[i + 1] = sigmoid(W[i] @ A[i])
# reconstruction error and sparsity error
err = (A[3] - A[1])
rho_hat = tf.reduce_sum(A[2], axis=1, keep_dims=True) / dsize
# B[i] = backprops needed to compute gradient of W[i]
B = [None] * (n + 1)
B[n] = err * d_sigmoid(A[n + 1])
for i in range(n - 1, -1, -1):
backprop = t(W[i + 1]) @ B[i + 1]
if i == 1:
backprop += beta * d_kl(rho, rho_hat)
B[i] = backprop * d_sigmoid(A[i + 1])
# dW[i] = gradient of W[i]
dW = [None] * (n + 1)
for i in range(n + 1):
dW[i] = (B[i] @ t(A[i])) / dsize
# Cost function
reconstruction = u.L2(err) / (2 * dsize)
sparsity = beta * tf.reduce_sum(kl(rho, rho_hat))
L2 = (lambda_ / 2) * (u.L2(W[1]) + u.L2(W[1]))
cost = reconstruction + sparsity + L2
A[1] = W[0]
for i in range(1, n + 1):
A[i + 1] = sigmoid(W[i] @ A[i])
# reconstruction error and sparsity error
err = (A[3] - A[1])
rho_hat = tf.reduce_sum(A[2], axis=1, keep_dims=True) / dsize
# B[i] = backprops needed to compute gradient of W[i]
# B2[i] = synthetic backprops for natural gradient
B = [None] * (n + 1)
B2 = [None] * (n + 1)
B[n] = err * d_sigmoid(A[n + 1])
B2[n] = tf.random_normal((f(n), f(-1)), dtype=dtype) * d_sigmoid(A[n + 1])
for i in range(n - 1, -1, -1):
backprop = t(W[i + 1]) @ B[i + 1]
backprop2 = t(W[i + 1]) @ B2[i + 1]
if i == 1:
backprop += beta * d_kl(rho, rho_hat)
backprop2 += beta * d_kl(rho, rho_hat)
B[i] = backprop * d_sigmoid(A[i + 1])
B2[i] = backprop2 * d_sigmoid(A[i + 1])
# dW[i] = gradient of W[i]
dW = [None] * (n + 1)
dW2 = [None] * (n + 1)
Acov = [None] * (n + 1)
Bcov = [None] * (n + 1) # empirical covariances
Bcov2 = [None] * (n + 1) # natural gradient sampled covariances
whitenA = [None] * (n + 1)
whitenB = [None] * (n + 1)
A[i+1] = sigmoid(W[i] @ A[i])
# reconstruction error and sparsity error
err = (A[3] - A[1])
rho_hat = tf.reduce_sum(A[2], axis=1, keep_dims=True)/dsize
# B[i] = backprops needed to compute gradient of W[i]
# B2[i] = backprops from sampled labels needed for natural gradient
B = [None]*(n+1)
B2 = [None]*(n+1)
B[n] = err*d_sigmoid(A[n+1])
sampled_labels_live = tf.random_normal((f(n), f(-1)), dtype=dtype, seed=0)
sampled_labels = init_var(sampled_labels_live, "sampled_labels", noinit=True)
B2[n] = sampled_labels*d_sigmoid(A[n+1])
for i in range(n-1, -1, -1):
backprop = t(W[i+1]) @ B[i+1]
backprop2 = t(W[i+1]) @ B2[i+1]
if i == 1 and not drop_sparsity:
backprop += beta*d_kl(rho, rho_hat)
backprop2 += beta*d_kl(rho, rho_hat)
B[i] = backprop*d_sigmoid(A[i+1])
B2[i] = backprop2*d_sigmoid(A[i+1])
# dW[i] = gradient of W[i]
dW = [None]*(n+1)
pre_dW = [None]*(n+1) # preconditioned dW
pre_dW_stable = [None]*(n+1) # preconditioned stable dW
cov_A = [None]*(n+1) # covariance of activations[i]
cov_B2 = [None]*(n+1) # covariance of synthetic backprops[i]
vars_svd_A = [None]*(n+1)
def kfac_optimizer(model_creator):
stats_batch_size = 10000
main_batch_size = 10000
stats_model, loss, labels = model_creator(stats_batch_size)
# replace labels_node with synthetic labels
main_model, _, _ = model_creator(main_batch_size)
opt = tf.GradientDescentOptimizer(0.2)
grads_and_vars = opt.compute_gradients(loss)
trainable_vars = tf.trainable_variables()
# create SVD and preconditioning variables for matmul vars
for var in trainable_vars:
if var not in matmul_registry:
continue
dW = u.extract_grad(grads_and_vars, var)
A[var] = get_activations(var)
B[var] = get_backprops(var)
B2[var] = get_backprops2(var) # get backprops with synthetic labels
dW[var] = B[var] @ t(A[var]) # todo: sort out dsize division
cov_A[var] = init_var(A[var] @ t(A[var]) / dsize,
"cov_A_%s" % (var.name, ))
cov_B2[var] = init_var(B2[var] @ t(B2[var]) / dsize,
"cov_B2_%s" % (var.name, ))
vars_svd_A[var] = SvdWrapper(cov_A[var], "svd_A_%d" % (var.name, ))
vars_svd_B2[var] = SvdWrapper(cov_B2[var], "svd_B2_%d" % (var.name, ))
whitened_A = u.pseudo_inverse2(vars_svd_A[var]) @ A[var]
whitened_B2 = u.pseudo_inverse2(vars_svd_B2[var]) @ B[var]
whitened_A_stable = u.pseudo_inverse_sqrt2(vars_svd_A[var]) @ A[var]
whitened_B2_stable = u.pseudo_inverse_sqrt2(vars_svd_B2[var]) @ B[var]
pre_dW[var] = (whitened_B2 @ t(whitened_A)) / dsize
pre_dW_stable[var] = (
whitened_B2_stable @ t(whitened_A_stable)) / dsize
dW[var] = (B[var] @ t(A[var])) / dsize
# create update params ops
# new_grads_and_vars = []
# for grad, var in grads_and_vars:
# if var in kfac_registry:
# pre_A, pre_B = kfac_registry[var]
# new_grad_live = pre_B @ grad @ t(pre_A)
# new_grads_and_vars.append((new_grad, var))
# print("Preconditioning %s"%(var.name))
# else:
# new_grads_and_vars.append((grad, var))
# train_op = opt.apply_gradients(new_grads_and_vars)
# Each variable has an associated gradient, pre_gradient, variable save op
def update_grad():
ops = [grad_update_ops[var] for var in trainable_vars]
sess.run(ops)
def update_pre_grad():
ops = [pre_grad_update_ops[var] for var in trainable_vars]
sess.run(ops)
def update_pre_grad2():
ops = [pre_grad2_update_ops[var] for var in trainable_vars]
sess.run(ops)
def save_params():
ops = [var_save_ops[var] for var in trainable_vars]
sess.run(ops)
for step in range(num_steps):
update_covariances()
if step % whitened_every_n_steps == 0:
update_svds()
update_grad()
update_pre_grad() # perf todo: update one of these
update_pre_grad2() # stable alternative
lr0, loss0 = sess.run([lr, loss])
save_params()
# when grad norm<1, Fisher is unstable, switch to Sqrt(Fisher)
# TODO: switch to per-matrix normalization
stabilized_mode = grad_norm.eval() < 1
if stabilized_mode:
update_params2()
else:
update_params()
loss1 = loss.eval()
advance_batch()
# line search stuff
target_slope = (-pre_grad_dot_grad.eval() if stabilized_mode else
-pre_grad_stable_dot_grad.eval())
target_delta = lr0 * target_slope
actual_delta = loss1 - loss0
actual_slope = actual_delta / lr0
slope_ratio = actual_slope / target_slope # between 0 and 1.01
losses.append(loss0)
step_lengths.append(lr0)
ratios.append(slope_ratio)
if step % report_frequency == 0:
print(
"Step %d loss %.2f, target decrease %.3f, actual decrease, %.3f ratio %.2f grad norm: %.2f pregrad norm: %.2f"
% (step, loss0, target_delta, actual_delta, slope_ratio,
grad_norm.eval(), pre_grad_norm.eval()))
u.record_time()
def simple_gradient_test():
tf.reset_default_graph()
X0 = np.genfromtxt('data/rotations_simple_X0.csv',
delimiter= ",")
Y0 = np.genfromtxt('data/rotations_simple_Y0.csv',
delimiter= ",")
W0f = v2c_np(np.genfromtxt('data/rotations_simple_W0f.csv',
delimiter= ","))
assert W0f.shape == (8, 1)
fs = np.genfromtxt('data/rotations_simple_fs.csv',
delimiter= ",").astype(np.int32)
n = len(fs)-2 # number of layers
u.check_equal(fs, [10,2,2,2])
def f(i): return fs[i+1] # W[i] has shape f[i] x f[i-1]
dsize = X0.shape[1]
assert f(-1) == dsize
# load W0f and do shape checks (can remove)
W0s = u.unflatten_np(W0f, fs[1:]) # Wf doesn't have first layer (data matrix)
W0s.insert(0, X0)
Wf_holder = tf.placeholder(dtype, shape=W0f.shape)
Wf = tf.Variable(Wf_holder, name="Wf")
Wf_copy = tf.Variable(Wf_holder, name="Wf_copy")
init_dict = {Wf_holder: W0f}
# Create W's
W = u.unflatten(Wf, fs[1:])
X = tf.constant(X0)
Y = tf.constant(Y0)
W.insert(0, X)
for (numpy_W, tf_W) in zip(W0s, W):
u.check_equal(numpy_W.shape, u.fix_shape(tf_W.shape))
# Create A's
# A[1] == X
A = [0]*(n+2)
A[0] = u.Identity(dsize)
for i in range(n+1):
A[i+1] = tf.matmul(W[i], A[i], name="A"+str(i+1))
assert W[0].get_shape() == X0.shape
assert A[n+1].get_shape() == X0.shape
assert A[1].get_shape() == X0.shape
err = Y - A[n+1]
loss = tf.reduce_sum(tf.square(err))/(2*dsize)
lr = tf.Variable(1.0, dtype=dtype)
# Create B's
B = [0]*(n+1)
B[n] = -err/dsize
for i in range(n-1, -1, -1):
B[i] = t(W[i+1]) @ B[i+1]
# create dW's
dW = [0]*(n+1)
for i in range(n+1):
dW[i] = tf.matmul(B[i], tf.transpose(A[i]), name="dW"+str(i))
del dW[0] # get rid of W[0] update
dWf = tf.concat([u.vec(dWi) for dWi in dW], axis=0)
Wf_new = Wf - lr * dWf
train_op1 = Wf_copy.assign(Wf_new)
train_op2 = Wf.assign(Wf_copy)
sess = tf.Session()
sess.run(tf.global_variables_initializer(), feed_dict=init_dict)
expected_losses = np.loadtxt("data/rotations_simple_gradient_losses.csv",
delimiter= ",")
observed_losses = []
# from accompanying notebook
# {0.0111498, 0.00694816, 0.00429464, 0.00248228, 0.00159361,
# 0.000957424, 0.000651653, 0.000423802, 0.000306749, 0.00021772,
for i in range(20):
observed_losses.append(sess.run([loss])[0])
sess.run(train_op1)
sess.run(train_op2)
u.check_equal(observed_losses, expected_losses)
A = [None]*(n+2)
A[0] = u.Identity(dsize, dtype=dtype)
A[1] = W[0]
for i in range(1, n+1):
A[i+1] = sigmoid(W[i] @ A[i])
# reconstruction error and sparsity error
err = (A[3] - A[1])
rho_hat = tf.reduce_sum(A[2], axis=1, keep_dims=True)/dsize
# B[i] = backprops needed to compute gradient of W[i]
B = [None]*(n+1)
B[n] = err*d_sigmoid(A[n+1])
for i in range(n-1, -1, -1):
backprop = t(W[i+1]) @ B[i+1]
if i == 1:
backprop += beta*d_kl(rho, rho_hat)
B[i] = backprop*d_sigmoid(A[i+1])
# dW[i] = gradient of W[i]
dW = [None]*(n+1)
for i in range(n+1):
dW[i] = (B[i] @ t(A[i]))/dsize
# Cost function
reconstruction = u.L2(err) / (2 * dsize)
sparsity = beta * tf.reduce_sum(kl(rho, rho_hat))
L2 = (lambda_ / 2) * (u.L2(W[1]) + u.L2(W[1]))
cost = reconstruction + sparsity + L2
