def build_predict(self, Xnew, full_cov=False):
"""
Compute the mean and variance of the latent function at some new points
Xnew. For a derivation of the terms in here, see the associated SGPR
notebook.
"""
num_inducing = tf.shape(self.Z)[0]
err = self.Y - self.mean_function(self.X)
Kuf = self.kern.K(self.Z, self.X)
Kuu = self.kern.K(self.Z) + eye(num_inducing) * 1e-6
Kus = self.kern.K(self.Z, Xnew)
sigma = tf.sqrt(self.likelihood.variance)
L = tf.cholesky(Kuu)
A = tf.matrix_triangular_solve(L, Kuf, lower=True) / sigma
B = tf.matmul(A, tf.transpose(A)) + eye(num_inducing)
LB = tf.cholesky(B)
Aerr = tf.matmul(A, err)
c = tf.matrix_triangular_solve(LB, Aerr, lower=True) / sigma
tmp1 = tf.matrix_triangular_solve(L, Kus, lower=True)
tmp2 = tf.matrix_triangular_solve(LB, tmp1, lower=True)
mean = tf.matmul(tf.transpose(tmp2), c)
if full_cov:
var = self.kern.K(Xnew) + tf.matmul(tf.transpose(tmp2), tmp2)\
- tf.matmul(tf.transpose(tmp1), tmp1)
shape = tf.pack([1, 1, tf.shape(self.Y)[1]])
var = tf.tile(tf.expand_dims(var, 2), shape)
else:
var = self.kern.Kdiag(Xnew) + tf.reduce_sum(tf.square(tmp2), 0)\
- tf.reduce_sum(tf.square(tmp1), 0)
shape = tf.pack([1, tf.shape(self.Y)[1]])
var = tf.tile(tf.expand_dims(var, 1), shape)
return mean + self.mean_function(Xnew), var
def sq_dist(boxlist1, boxlist2, scope=None):
"""Computes the pairwise squared distances between box corners.
This op treats each box as if it were a point in a 4d Euclidean space and
computes pairwise squared distances.
Mathematically, we are given two matrices of box coordinates X and Y,
where X(i,:) is the i'th row of X, containing the 4 numbers defining the
corners of the i'th box in boxlist1. Similarly Y(j,:) corresponds to
boxlist2. We compute
Z(i,j) = ||X(i,:) - Y(j,:)||^2
= ||X(i,:)||^2 + ||Y(j,:)||^2 - 2 X(i,:)' * Y(j,:),
Args:
boxlist1: BoxList holding N boxes
boxlist2: BoxList holding M boxes
scope: name scope.
Returns:
a tensor with shape [N, M] representing pairwise distances
"""
with tf.name_scope(scope, 'SqDist'):
sqnorm1 = tf.reduce_sum(tf.square(boxlist1.get()), 1, keep_dims=True)
sqnorm2 = tf.reduce_sum(tf.square(boxlist2.get()), 1, keep_dims=True)
innerprod = tf.matmul(boxlist1.get(), boxlist2.get(),
transpose_a=False, transpose_b=True)
return sqnorm1 + tf.transpose(sqnorm2) - 2.0 * innerprod
def soft_triplet_loss(anchor, positive, negative, extra=True, scope="soft_triplet_loss"):
r"""Loss for triplet networks as described in the paper:
`Deep Metric Learning using Triplet Network
<https://arxiv.org/abs/1412.6622>`_ by Hoffer et al.
It is a softmax loss using :math:`(anchor-positive)^2` and
:math:`(anchor-negative)^2` as logits.
Args:
anchor (tf.Tensor): anchor feature vectors of shape [Batch, N].
positive (tf.Tensor): features of positive match of the same shape.
negative (tf.Tensor): features of negative match of the same shape.
extra (bool): also return distances for pos and neg.
Returns:
tf.Tensor: triplet-loss as scalar (and optionally average_pos_dist, average_neg_dist)
"""
eps = 1e-10
with tf.name_scope(scope):
d_pos = tf.sqrt(tf.reduce_sum(tf.square(anchor - positive), 1) + eps)
d_neg = tf.sqrt(tf.reduce_sum(tf.square(anchor - negative), 1) + eps)
logits = tf.stack([d_pos, d_neg], axis=1)
ones = tf.ones_like(tf.squeeze(d_pos), dtype="int32")
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=ones))
if extra:
pos_dist = tf.reduce_mean(d_pos, name='pos-dist')
neg_dist = tf.reduce_mean(d_neg, name='neg-dist')
return loss, pos_dist, neg_dist
else:
return loss
def loss(self):
# 1. The margin loss
# [batch_size, 10, 1, 1]
# max_l = max(0, m_plus-||v_c||)^2
max_l = tf.square(tf.maximum(0., cfg.m_plus - self.v_length))
# max_r = max(0, ||v_c||-m_minus)^2
max_r = tf.square(tf.maximum(0., self.v_length - cfg.m_minus))
assert max_l.get_shape() == [cfg.batch_size, 10, 1, 1]
# reshape: [batch_size, 10, 1, 1] => [batch_size, 10]
max_l = tf.reshape(max_l, shape=(cfg.batch_size, -1))
max_r = tf.reshape(max_r, shape=(cfg.batch_size, -1))
# calc T_c: [batch_size, 10]
# T_c = Y, is my understanding correct? Try it.
T_c = self.Y
# [batch_size, 10], element-wise multiply
L_c = T_c * max_l + cfg.lambda_val * (1 - T_c) * max_r
self.margin_loss = tf.reduce_mean(tf.reduce_sum(L_c, axis=1))
# 2. The reconstruction loss
orgin = tf.reshape(self.X, shape=(cfg.batch_size, -1))
squared = tf.square(self.decoded - orgin)
self.reconstruction_err = tf.reduce_mean(squared)
# 3. Total loss
# The paper uses sum of squared error as reconstruction error, but we
# have used reduce_mean in `# 2 The reconstruction loss` to calculate
# mean squared error. In order to keep in line with the paper,the
# regularization scale should be 0.0005*784=0.392
self.total_loss = self.margin_loss + cfg.regularization_scale * self.reconstruction_err
def standard_reg():
reg = tf.constant(0.0, dtype=tf.float32)
reg = reg + standard_w_weight_reg * tf.reduce_mean(tf.square(net_params['sDW1']))
#reg = reg + standard_w_weight_reg * tf.reduce_mean(tf.square(net_params['sDW2']))
reg = reg + regressor_w_weight_reg * tf.reduce_mean(tf.square(net_params['sRW']))
return reg
def kl_multivariate_normal(loc_one, scale_one, loc_two=0.0, scale_two=1.0):
"""Calculate the KL of multivariate normal distributions with
diagonal covariances.
Parameters
----------
loc_one : tf.Tensor
A 0-D tensor, 1-D tensor of length n, or 2-D tensor of shape M
x n where each row represents the mean of a n-dimensional
Gaussian.
scale_one : tf.Tensor
A tensor of same shape as ``loc_one``, representing the
standard deviation.
loc_two : tf.Tensor, optional
A tensor of same shape as ``loc_one``, representing the
mean of another Gaussian.
scale_two : tf.Tensor, optional
A tensor of same shape as ``loc_one``, representing the
standard deviation of another Gaussian.
Returns
-------
tf.Tensor
For 0-D or 1-D tensor inputs, outputs the 0-D tensor
``KL( N(z; loc_one, scale_one) || N(z; loc_two, scale_two) )``
For 2-D tensor inputs, outputs the 1-D tensor
``[KL( N(z; loc_one[m,:], scale_one[m,:]) || N(z; loc_two[m,:], scale_two[m,:]) )]_{m=1}^M``
Raises
------
InvalidArgumentError
If the location variables have Inf or NaN values, or if the scale
variables are not positive.
"""
dependencies = [tf.verify_tensor_all_finite(loc_one, msg=''),
tf.verify_tensor_all_finite(loc_two, msg=''),
tf.assert_positive(scale_one),
tf.assert_positive(scale_two)]
loc_one = control_flow_ops.with_dependencies(dependencies, loc_one)
scale_one = control_flow_ops.with_dependencies(dependencies, scale_one)
loc_one = tf.cast(loc_one, tf.float32)
scale_one = tf.cast(scale_one, tf.float32)
if loc_two == 0.0 and scale_two == 1.0:
# With default arguments, we can avoid some intermediate computation.
out = tf.square(scale_one) + tf.square(loc_one) - \
1.0 - 2.0 * tf.log(scale_one)
else:
loc_two = control_flow_ops.with_dependencies(dependencies, loc_two)
scale_two = control_flow_ops.with_dependencies(dependencies, scale_two)
loc_two = tf.cast(loc_two, tf.float32)
scale_two = tf.cast(scale_two, tf.float32)
out = tf.square(scale_one/scale_two) + \
tf.square((loc_two - loc_one)/scale_two) - \
1.0 + 2.0 * tf.log(scale_two) - 2.0 * tf.log(scale_one)
if len(out.get_shape()) <= 1: # scalar or vector
return 0.5 * tf.reduce_sum(out)
else: # matrix
return 0.5 * tf.reduce_sum(out, 1)
def multilinear_square_product(emb, tuples, l2=0):
"""
Compute the square-product of real vectors at selected embeddings.
This is the sum over all dimensions of the square of summed embedding vectors.
:param emb: embedding matrix of size [n_emb, rank] containing float numbers
:param tuples: tuple matrix of size [n_t, arity] containing integers
:param l2: optional l2 regularization strength that is added to the score. If it is different from 0, the function
returns a pair (pred, l2norm) where pred is the sample prediction, but l2norm is the l2 norm of the selected
embeddings
:return: the multilinear square product between selected embeddings
S[i] = sum_k ( sum_j E[I[i,k],j] )^2
>>> emb = [[12., 0, 0], [0, 1, 0], [-1, 1, 1]]
>>> idx = tf.Variable([[1,0,0],[1,1,0]])
>>> g = multilinear_square_product(emb, idx)
>>> print(tf_eval(g))
[ 577. 148.]
"""
emb_sel = tf.gather(emb, tuples)
pred = tf.reduce_sum(tf.square(tf.reduce_sum(emb_sel, 1)), 1)
if l2 == 0: # unregularized prediction ==> returns only the predictions
return pred
else: # l2 regularization of the selected embeddings
reg = l2 * tf.reduce_sum(tf.square(emb_sel))
return pred, reg
def cord_cls_loss(
detectors_mask,
matching_true_boxes,
num_classes,
pred_class_prob,
pred_boxes,
loc_scale,
):
"""
:param detectors_mask: [batch, 13, 13, 3, 1]
:param matching_true_boxes: [batch, 13, 13, 3, 5] [σ(tx), σ(ty), tw, th, cls]
:param num_classes: 20
:param pred_class_prob: [batch, 13, 13, 3, 20]
:param pred_boxes: [batch, 13, 13, 3, 4]
:param loc_scale: [batch, 13, 13, 3, 1]
:return:
mean_loss: float
mean localization loss across minibatch
"""
# Classification loss for matching detections.
# NOTE: YOLO does not use categorical cross-entropy loss here.
matching_classes = tf.cast(matching_true_boxes[..., 4], tf.int32) # [batch, 13, 13, 3]
matching_classes = tf.one_hot(matching_classes, num_classes) # [batch, 13, 13, 3, 20]
classification_loss = (detectors_mask *
tf.square(matching_classes - pred_class_prob)) # [batch, 13, 13, 3, 20]
# Coordinate loss for matching detection boxes. [σ(tx), σ(ty), tw, th]
matching_boxes = matching_true_boxes[..., 0:4]
coordinates_loss = (detectors_mask * loc_scale * tf.square(matching_boxes - pred_boxes))
classification_loss_sum = tf.reduce_sum(classification_loss)
coordinates_loss_sum = tf.reduce_sum(coordinates_loss)
return classification_loss_sum + coordinates_loss_sum
def e_step(o_mean, o_stdv, o_activations, votes):
"""The E-Step in EM Routing.
:param o_mean: (24, 6, 6, 1, 32, 16)
:param o_stdv: (24, 6, 6, 1, 32, 16)
:param o_activations: (24, 6, 6, 1, 32, 1)
:param votes: (24, 6, 6, 288, 32, 16)
:return: rr
"""
o_p_unit0 = - tf.reduce_sum(
tf.square(votes - o_mean) / (2 * tf.square(o_stdv)), axis=-1, keep_dims=True
)
o_p_unit2 = - tf.reduce_sum(
tf.log(o_stdv + epsilon), axis=-1, keep_dims=True
)
# o_p is the probability density of the h-th component of the vote from i to j
# (24, 6, 6, 1, 32, 16)
o_p = o_p_unit0 + o_p_unit2
# rr: (24, 6, 6, 288, 32, 1)
zz = tf.log(o_activations + epsilon) + o_p
rr = tf.nn.softmax(
zz, dim=len(zz.get_shape().as_list())-2
)
return rr
def _build_predict(self, Xnew, full_cov=False):
"""
Compute the mean and variance of the latent function at some new points
Xnew. For a derivation of the terms in here, see the associated SGPR
notebook.
"""
num_inducing = len(self.feature)
err = self.Y - self.mean_function(self.X)
Kuf = self.feature.Kuf(self.kern, self.X)
Kuu = self.feature.Kuu(self.kern, jitter=settings.numerics.jitter_level)
Kus = self.feature.Kuf(self.kern, Xnew)
sigma = tf.sqrt(self.likelihood.variance)
L = tf.cholesky(Kuu)
A = tf.matrix_triangular_solve(L, Kuf, lower=True) / sigma
B = tf.matmul(A, A, transpose_b=True) + tf.eye(num_inducing, dtype=settings.float_type)
LB = tf.cholesky(B)
Aerr = tf.matmul(A, err)
c = tf.matrix_triangular_solve(LB, Aerr, lower=True) / sigma
tmp1 = tf.matrix_triangular_solve(L, Kus, lower=True)
tmp2 = tf.matrix_triangular_solve(LB, tmp1, lower=True)
mean = tf.matmul(tmp2, c, transpose_a=True)
if full_cov:
var = self.kern.K(Xnew) + tf.matmul(tmp2, tmp2, transpose_a=True) \
- tf.matmul(tmp1, tmp1, transpose_a=True)
shape = tf.stack([1, 1, tf.shape(self.Y)[1]])
var = tf.tile(tf.expand_dims(var, 2), shape)
else:
var = self.kern.Kdiag(Xnew) + tf.reduce_sum(tf.square(tmp2), 0) \
- tf.reduce_sum(tf.square(tmp1), 0)
shape = tf.stack([1, tf.shape(self.Y)[1]])
var = tf.tile(tf.expand_dims(var, 1), shape)
return mean + self.mean_function(Xnew), var
def triplet_loss(y_true, y_pred, alpha = 0.2):
"""
Implementation of the triplet loss as defined by formula
Arguments:
y_true -- true labels, required when you define a loss in Keras, you don't need it in this function.
y_pred -- python list containing three objects:
anchor -- the encodings for the anchor images, of shape (None, 128)
positive -- the encodings for the positive images, of shape (None, 128)
negative -- the encodings for the negative images, of shape (None, 128)
Returns:
loss -- real number, value of the loss
"""
anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]
### START CODE HERE ### (≈ 4 lines)
# Step 1: Compute the (encoding) distance between the anchor and the positive, you will need to sum over axis=-1
pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)))
# Step 2: Compute the (encoding) distance between the anchor and the negative, you will need to sum over axis=-1
neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)))
# Step 3: subtract the two previous distances and add alpha.
basic_loss = tf.add(tf.subtract(pos_dist,neg_dist), alpha)
# Step 4: Take the maximum of basic_loss and 0.0. Sum over the training examples.
loss = tf.reduce_sum(tf.maximum(basic_loss, 0.))
### END CODE HERE ###
return loss
def build_graph(self, image_pos):
image_pos = image_pos / 128.0 - 1
z = tf.random_normal([self.batch, self.zdim], name='z_train')
z = tf.placeholder_with_default(z, [None, self.zdim], name='z')
with argscope([Conv2D, Conv2DTranspose, FullyConnected],
kernel_initializer=tf.truncated_normal_initializer(stddev=0.02)):
with tf.variable_scope('gen'):
image_gen = self.generator(z)
tf.summary.image('generated-samples', image_gen, max_outputs=30)
alpha = tf.random_uniform(shape=[self.batch, 1, 1, 1],
minval=0., maxval=1., name='alpha')
interp = image_pos + alpha * (image_gen - image_pos)
with tf.variable_scope('discrim'):
vecpos = self.discriminator(image_pos)
vecneg = self.discriminator(image_gen)
vec_interp = self.discriminator(interp)
# the Wasserstein-GAN losses
self.d_loss = tf.reduce_mean(vecneg - vecpos, name='d_loss')
self.g_loss = tf.negative(tf.reduce_mean(vecneg), name='g_loss')
# the gradient penalty loss
gradients = tf.gradients(vec_interp, [interp])[0]
gradients = tf.sqrt(tf.reduce_sum(tf.square(gradients), [1, 2, 3]))
gradients_rms = symbolic_functions.rms(gradients, 'gradient_rms')
gradient_penalty = tf.reduce_mean(tf.square(gradients - 1), name='gradient_penalty')
add_moving_summary(self.d_loss, self.g_loss, gradient_penalty, gradients_rms)
self.d_loss = tf.add(self.d_loss, 10 * gradient_penalty)
self.collect_variables()
def build_psi_stats_rbf_plus_linear(Z, kern, mu, S):
# TODO: make sure the acvite dimensions are overlapping completely
# use only active dimensions
mu, S = kern._slice(mu, S) # only use the active dimensions.
Z, _ = kern._slice(Z, None)
psi0_lin, psi1_lin, psi2_lin = build_psi_stats_linear(Z, kern.linear, mu, S)
psi0_rbf, psi1_rbf, psi2_rbf = build_psi_stats_rbf(Z, kern.rbf, mu, S)
psi0, psi1, psi2 = psi0_lin + psi0_rbf, psi1_lin + psi1_rbf, psi2_lin + psi2_rbf
# extra terms for the 'interaction' of linear and rbf
l2 = tf.square(kern.rbf.lengthscales)
A = tf.expand_dims(1./S + 1./l2, 1) # N x 1 x Q
m = (tf.expand_dims(mu/S, 1) + tf.expand_dims(Z/l2, 0)) / A # N x M x Q
mTAZ = tf.reduce_sum(tf.expand_dims(m * kern.linear.variance, 1) *
tf.expand_dims(tf.expand_dims(Z, 0), 0), 3) # N x M x M
Z2 = tf.reduce_sum(tf.square(Z) / l2, 1) # M,
mu2 = tf.reduce_sum(tf.square(mu) / S, 1) # N
mAm = tf.reduce_sum(tf.square(m) * A, 2) # N x M
exp_term = tf.exp(-(tf.reshape(Z2, (1, -1)) + tf.reshape(mu2, (-1, 1))-mAm) / 2.) # N x M
psi2_extra = tf.reduce_sum(kern.rbf.variance *
tf.expand_dims(exp_term, 2) *
tf.expand_dims(tf.expand_dims(tf.reduce_prod(S, 1), 1), 2) *
tf.expand_dims(tf.reduce_prod(A, 2), 1) *
mTAZ, 0)
psi2 = psi2 + psi2_extra + tf.transpose(psi2_extra)
return psi0, psi1, psi2
def drawGraph(self, n_row, n_latent, n_col):
with tf.name_scope('matDecomp'):
self._p = tf.placeholder(tf.float32, shape=[None, n_col])
self._c = tf.placeholder(tf.float32, shape=[None, n_col])
self._lambda = tf.placeholder(tf.float32)
self._index = tf.placeholder(tf.float32, shape=[None, n_row])
self._A = tf.Variable(tf.truncated_normal([n_row, n_latent]))
self._B = tf.Variable(tf.truncated_normal([n_latent, n_col]))
self._h = tf.matmul(tf.matmul(self._index, self._A), self._B)
weighted_loss = tf.reduce_mean(tf.mul(self._c, tf.squared_difference(self._p, self._h)))
self._weighted_loss = weighted_loss
l2_A = tf.reduce_sum(tf.square(self._A))
l2_B = tf.reduce_sum(tf.square(self._B))
n_w = tf.constant(n_row * n_latent + n_latent * n_col, tf.float32)
l2 = tf.truediv(tf.add(l2_A, l2_B), n_w)
reg_term = tf.mul(self._lambda, l2)
self._loss = tf.add(weighted_loss, reg_term)
self._mask = tf.placeholder(tf.float32, shape=[n_row, n_col])
one = tf.constant(1, tf.float32)
pred = tf.cast(tf.greater_equal(tf.matmul(self._A, self._B), one), tf.float32)
cor = tf.mul(tf.cast(tf.equal(pred, self._p), tf.float32), self._c)
self._vali_err = tf.reduce_sum(tf.mul(cor, self._mask))
self._saver = tf.train.Saver([v for v in tf.all_variables() if v.name.find('matDecomp') != -1])
tf.scalar_summary('training_weighted_loss_l2', self._loss)
tf.scalar_summary('validation_weighted_loss', self._weighted_loss)
merged = tf.merge_all_summaries()
def _ssim_helper(var_x, var_y, max_val, kernel, compensation=1.0):
"""
Helper function for computing SSIM.
SSIM estimates covariances with weighted sums. The default parameters
use a biased estimate of the covariance:
Suppose `reducer` is a weighted sum, then the mean estimators are
mu_x = sum_i w_i x_i,
mu_y = sum_i w_i y_i,
where w_i's are the weighted-sum weights, and covariance estimator is
cov_{xy} = sum_i w_i (x_i - mu_x) (y_i - mu_y)
with assumption sum_i w_i = 1. This covariance estimator is biased, since
E[cov_{xy}] = (1 - sum_i w_i ^ 2) Cov(X, Y).
For SSIM measure with unbiased covariance estimators, pass as `compensation`
argument (1 - sum_i w_i ^ 2).
Arguments:
x: First set of images.
y: Second set of images.
reducer: Function that computes 'local' averages from set of images.
For non-covolutional version, this is usually tf.reduce_mean(x, [1, 2]),
and for convolutional version, this is usually tf.nn.avg_pool or
tf.nn.conv2d with weighted-sum kernel.
max_val: The dynamic range (i.e., the difference between the maximum
possible allowed value and the minimum allowed value).
compensation: Compensation factor. See above.
Returns:
A pair containing the luminance measure, and the contrast-structure measure.
"""
def reducer(var_x, kernel):
shape = tf.shape(var_x)
var_x = tf.reshape(var_x, shape=tf.concat([[-1], shape[-3:]], 0))
var_y = tf.nn.depthwise_conv2d(var_x, kernel, strides=[1, 1, 1, 1], padding='VALID')
return tf.reshape(var_y, tf.concat([shape[:-3], tf.shape(var_y)[1:]], 0))
_ssim_k1 = 0.01
_ssim_k2 = 0.03
c_1 = (_ssim_k1 * max_val) ** 2
c_2 = (_ssim_k2 * max_val) ** 2
# SSIM luminance measure is
# (2 * mu_x * mu_y + c_1) / (mu_x ** 2 + mu_y ** 2 + c_1).
mean0 = reducer(var_x, kernel)
mean1 = reducer(var_y, kernel)
num0 = mean0 * mean1 * 2.0
den0 = tf.square(mean0) + tf.square(mean1)
luminance = (num0 + c_1) / (den0 + c_1)
# SSIM contrast-structure measure is
# (2 * cov_{xy} + c_2) / (cov_{xx} + cov_{yy} + c_2).
# Note that `reducer` is a weighted sum with weight w_k, \sum_i w_i = 1, then
# cov_{xy} = \sum_i w_i (x_i - \mu_x) (y_i - \mu_y)
# = \sum_i w_i x_i y_i - (\sum_i w_i x_i) (\sum_j w_j y_j).
num1 = reducer(var_x * var_y, kernel) * 2.0
den1 = reducer(tf.square(var_x) + tf.square(var_y), kernel)
c_2 *= compensation
c_s = (num1 - num0 + c_2) / (den1 - den0 + c_2)
# SSIM score is the product of the luminance and contrast-structure measures.
return luminance, c_s
def cosine_distances(test, support):
"""Computes pairwise cosine distances between provided tensors
Parameters
----------
test: tf.Tensor
Of shape (n_test, n_feat)
support: tf.Tensor
Of shape (n_support, n_feat)
Returns
-------
tf.Tensor:
Of shape (n_test, n_support)
"""
rnorm_test = tf.rsqrt(
tf.reduce_sum(tf.square(test), 1, keep_dims=True)) + 1e-7
rnorm_support = tf.rsqrt(
tf.reduce_sum(tf.square(support), 1, keep_dims=True)) + 1e-7
test_normalized = test * rnorm_test
support_normalized = support * rnorm_support
# Transpose for mul
support_normalized_t = tf.transpose(support_normalized, perm=[1, 0])
g = tf.matmul(test_normalized, support_normalized_t) # Gram matrix
return g
def batchnormalize(X, eps=1e-8, g=None, b=None):
if X.get_shape().ndims == 4:
mean = tf.reduce_mean(X, [0,1,2])
std = tf.reduce_mean( tf.square(X-mean), [0,1,2] )
X = (X-mean) / tf.sqrt(std+eps)
if g is not None and b is not None:
g = tf.reshape(g, [1,1,1,-1])
b = tf.reshape(b, [1,1,1,-1])
X = X*g + b
elif X.get_shape().ndims == 2:
mean = tf.reduce_mean(X, 0)
std = tf.reduce_mean(tf.square(X-mean), 0)
X = (X-mean) / tf.sqrt(std+eps)#std
if g is not None and b is not None:
g = tf.reshape(g, [1,-1])
b = tf.reshape(b, [1,-1])
X = X*g + b
else:
raise NotImplementedError
return X
def calc_bound_loss(x_tf, bound_min, bound_max):
# penalty for violating bounds
violation_min = tf.minimum(x_tf - bound_min, 0)
violation_max = tf.maximum(x_tf - bound_max, 0)
violation = tf.reduce_sum(tf.square(violation_min), axis=-1) + tf.reduce_sum(tf.square(violation_max), axis=-1)
loss = 0.5 * tf.reduce_mean(violation)
return loss
def build_predict(self,Xnew,task_ind):
"""
We need to assume the task_ind starts from 0
"""
Fmean,Fvar = 0,0
for i in np.arange(self.rank):
for j in np.arange(self.num_latent_list[i]):
lat_id = np.sum(self.num_latent_list[:i],dtype = np.int64) + j
if self.whiten_list[lat_id]: # need to compute fmean and fvar by the weights
fmean, fvar = conditionals.gaussian_gp_predict_whitened(Xnew, self.Z[lat_id],
self.kern_list[i], self.q_mu_list[lat_id],
self.q_sqrt_list[lat_id], 1)
else:
fmean, fvar = conditionals.gaussian_gp_predict(Xnew, self.Z[lat_id],
self.kern_list[i], self.q_mu_list[lat_id],
self.q_sqrt_list[lat_id],1)
W_ij = tf.gather(self.W,task_ind)[lat_id]
Fmean += (fmean + self.mean_function_list[lat_id](Xnew))*W_ij
Fvar += fvar * tf.square(W_ij)
if self.tsk:
for i in np.arange(self.num_tasks):
lat_id = np.sum(self.num_latent_list,dtype = np.int64) + i
if self.whiten_list[lat_id]: # need to compute fmean and fvar by the weights
fmean, fvar = conditionals.gaussian_gp_predict_whitened(Xnew, self.Z[lat_id],
self.tskern_list[i], self.q_mu_list[lat_id],
self.q_sqrt_list[lat_id], 1)
else:
fmean, fvar = conditionals.gaussian_gp_predict(Xnew, self.Z[lat_id],
self.tskern_list[i], self.q_mu_list[lat_id],
self.q_sqrt_list[lat_id], 1)
switch = tf.cast(tf.equal(tf.to_int64(i), task_ind),tf.float64)
W_ij = tf.gather(self.Kappa,i)[0]*switch
Fmean += (fmean + self.mean_function_list[lat_id](Xnew))*W_ij
Fvar += fvar * tf.square(W_ij)
return Fmean, Fvar
def gauss_kl_white(q_mu, q_sqrt, num_latent):
"""
Compute the KL divergence from
q(x) = N(q_mu, q_sqrt^2)
to
p(x) = N(0, I)
We assume num_latent independent distributions, given by the columns of
q_mu and the last dimension of q_sqrt.
q_mu is a matrix, each column contains a mean
q_sqrt is a 3D tensor, each matrix within is a lower triangular square-root
matrix of the covariance.
num_latent is an integer: the number of independent distributions (equal to
the columns of q_mu and the last dim of q_sqrt).
"""
KL = 0.5 * tf.reduce_sum(tf.square(q_mu)) # Mahalanobis term
KL += -0.5 * tf.cast(tf.shape(q_sqrt)[0] * num_latent, tf.float64)
for d in range(num_latent):
Lq = tf.batch_matrix_band_part(q_sqrt[:, :, d], -1, 0)
# Log determinant of q covariance:
KL -= 0.5 * tf.reduce_sum(tf.log(tf.square(tf.diag_part(Lq))))
KL += 0.5 * tf.reduce_sum(tf.square(Lq)) # Trace term.
return KL
def gauss_kl_diag(q_mu, q_sqrt, K, num_latent):
"""
Compute the KL divergence from
q(x) = N(q_mu, q_sqrt^2)
to
p(x) = N(0, K)
We assume num_latent independent distributions, given by the columns of
q_mu and q_sqrt.
q_mu is a matrix, each column contains a mean
q_sqrt is a matrix, each column represents the diagonal of a square-root
matrix of the covariance of q.
K is a positive definite matrix: the covariance of p.
num_latent is an integer: the number of independent distributions (equal to
the columns of q_mu and q_sqrt).
"""
L = tf.cholesky(K)
alpha = tf.matrix_triangular_solve(L, q_mu, lower=True)
KL = 0.5 * tf.reduce_sum(tf.square(alpha)) # Mahalanobis term.
KL += num_latent * 0.5 * tf.reduce_sum(
tf.log(tf.square(tf.diag_part(L)))) # Prior log-det term.
KL += -0.5 * tf.cast(tf.shape(q_sqrt)[0] * num_latent, tf.float64)
KL += -0.5 * tf.reduce_sum(tf.log(tf.square(q_sqrt))) # Log-det of q-cov
L_inv = tf.matrix_triangular_solve(L, eye(tf.shape(L)[0]), lower=True)
K_inv = tf.matrix_triangular_solve(tf.transpose(L), L_inv, lower=False)
KL += 0.5 * tf.reduce_sum(tf.expand_dims(tf.diag_part(K_inv), 1)
* tf.square(q_sqrt)) # Trace term.
return KL
def cross_entropy(u, label_u, alpha=0.5, normed=False):
label_ip = tf.cast(
tf.matmul(label_u, tf.transpose(label_u)), tf.float32)
s = tf.clip_by_value(label_ip, 0.0, 1.0)
# compute balance param
# s_t \in {-1, 1}
s_t = tf.multiply(tf.add(s, tf.constant(-0.5)), tf.constant(2.0))
sum_1 = tf.reduce_sum(s)
sum_all = tf.reduce_sum(tf.abs(s_t))
balance_param = tf.add(tf.abs(tf.add(s, tf.constant(-1.0))),
tf.multiply(tf.div(sum_all, sum_1), s))
if normed:
# ip = tf.clip_by_value(tf.matmul(u, tf.transpose(u)), -1.5e1, 1.5e1)
ip_1 = tf.matmul(u, tf.transpose(u))
def reduce_shaper(t):
return tf.reshape(tf.reduce_sum(t, 1), [tf.shape(t)[0], 1])
mod_1 = tf.sqrt(tf.matmul(reduce_shaper(tf.square(u)),
reduce_shaper(tf.square(u)), transpose_b=True))
ip = tf.div(ip_1, mod_1)
else:
ip = tf.clip_by_value(tf.matmul(u, tf.transpose(u)), -1.5e1, 1.5e1)
ones = tf.ones([tf.shape(u)[0], tf.shape(u)[0]])
return tf.reduce_mean(tf.multiply(tf.log(ones + tf.exp(alpha * ip)) - s * alpha * ip, balance_param))
def gaussian(y, mu_k, sigma_k): y = tf.reshape(y, [batchDim,1,L_out]) norm = tf.reduce_sum(tf.square(y-mu_k),axis=2) # sums over the L dimensions -> we get shape (N,K) again phi_k = -tf.div(norm, 2*tf.square(sigma_k)) phi_k = tf.exp(phi_k) phi_k = tf.divide(phi_k, sigma_k) return phi_k
def build_rmsprop_optimizer(self, learning_rate, rmsprop_decay, rmsprop_constant, gradient_clip, version):
with tf.name_scope('rmsprop'):
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
grads_and_vars = optimizer.compute_gradients(self.loss)
grads = [gv[0] for gv in grads_and_vars]
params = [gv[1] for gv in grads_and_vars]
if gradient_clip > 0:
grads = tf.clip_by_global_norm(grads, gradient_clip)
if version == 'rmsprop':
return optimizer.apply_gradients(zip(grads, params))
elif version == 'graves_rmsprop':
square_grads = [tf.square(grad) for grad in grads]
avg_grads = [tf.Variable(tf.ones(var.get_shape())) for var in params]
avg_square_grads = [tf.Variable(tf.ones(var.get_shape())) for var in params]
update_avg_grads = [grad_pair[0].assign((rmsprop_decay * grad_pair[0]) + ((1 - rmsprop_decay) * grad_pair[1]))
for grad_pair in zip(avg_grads, grads)]
update_avg_square_grads = [grad_pair[0].assign((rmsprop_decay * grad_pair[0]) + ((1 - rmsprop_decay) * tf.square(grad_pair[1])))
for grad_pair in zip(avg_square_grads, grads)]
avg_grad_updates = update_avg_grads + update_avg_square_grads
rms = [tf.sqrt(avg_grad_pair[1] - tf.square(avg_grad_pair[0]) + rmsprop_constant)
for avg_grad_pair in zip(avg_grads, avg_square_grads)]
rms_updates = [grad_rms_pair[0] / grad_rms_pair[1] for grad_rms_pair in zip(grads, rms)]
train = optimizer.apply_gradients(zip(rms_updates, params))
return tf.group(train, tf.group(*avg_grad_updates))
def gauss_kl(q_mu, q_sqrt, K, num_latent):
"""
Compute the KL divergence from
q(x) = N(q_mu, q_sqrt^2)
to
p(x) = N(0, K)
We assume num_latent independent distributions, given by the columns of
q_mu and the last dimension of q_sqrt.
q_mu is a matrix, each column contains a mean.
q_sqrt is a 3D tensor, each matrix within is a lower triangular square-root
matrix of the covariance of q.
K is a positive definite matrix: the covariance of p.
num_latent is an integer: the number of independent distributions (equal to
the columns of q_mu and the last dim of q_sqrt).
"""
L = tf.cholesky(K)
alpha = tf.matrix_triangular_solve(L, q_mu, lower=True)
KL = 0.5 * tf.reduce_sum(tf.square(alpha)) # Mahalanobis term.
KL += num_latent * 0.5 * tf.reduce_sum(
tf.log(tf.square(tf.diag_part(L)))) # Prior log-det term.
KL += -0.5 * tf.cast(tf.shape(q_sqrt)[0] * num_latent, tf.float64)
for d in range(num_latent):
Lq = tf.batch_matrix_band_part(q_sqrt[:, :, d], -1, 0)
# Log determinant of q covariance:
KL += -0.5*tf.reduce_sum(tf.log(tf.square(tf.diag_part(Lq))))
LiLq = tf.matrix_triangular_solve(L, Lq, lower=True)
KL += 0.5 * tf.reduce_sum(tf.square(LiLq)) # Trace term
return KL
def __graph__():
"""Building the inference graph"""
with tf.name_scope('input'):
# [BATCH_SIZE, NUM_FEATURES]
x_input = tf.placeholder(dtype=tf.float32, shape=[None, self.num_features], name='x_input')
# [BATCH_SIZE]
y_input = tf.placeholder(dtype=tf.uint8, shape=[None], name='y_input')
# [BATCH_SIZE, NUM_CLASSES]
y_onehot = tf.one_hot(indices=y_input, depth=self.num_classes, on_value=1, off_value=-1,
name='y_onehot')
learning_rate = tf.placeholder(dtype=tf.float32, name='learning_rate')
with tf.name_scope('training_ops'):
with tf.name_scope('weights'):
weight = tf.get_variable(name='weights',
initializer=tf.random_normal([self.num_features, self.num_classes],
stddev=0.01))
self.variable_summaries(weight)
with tf.name_scope('biases'):
bias = tf.get_variable(name='biases', initializer=tf.constant([0.1], shape=[self.num_classes]))
self.variable_summaries(bias)
with tf.name_scope('Wx_plus_b'):
output = tf.matmul(x_input, weight) + bias
tf.summary.histogram('pre-activations', output)
with tf.name_scope('svm'):
regularization = tf.reduce_mean(tf.square(weight))
hinge_loss = tf.reduce_mean(tf.square(tf.maximum(tf.zeros([self.batch_size, self.num_classes]),
1 - tf.cast(y_onehot, tf.float32) * output)))
with tf.name_scope('loss'):
loss = regularization + self.svm_c * hinge_loss
tf.summary.scalar('loss', loss)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
with tf.name_scope('accuracy'):
predicted_class = tf.sign(output)
predicted_class = tf.identity(predicted_class, name='prediction')
with tf.name_scope('correct_prediction'):
correct = tf.equal(tf.argmax(predicted_class, 1), tf.argmax(y_onehot, 1))
with tf.name_scope('accuracy'):
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
tf.summary.scalar('accuracy', accuracy)
merged = tf.summary.merge_all()
self.x_input = x_input
self.y_input = y_input
self.y_onehot = y_onehot
self.learning_rate = learning_rate
self.loss = loss
self.optimizer = optimizer
self.output = output
self.predicted_class = predicted_class
self.accuracy = accuracy
self.merged = merged
def build_loss(self, error_clip, num_actions, double_dqn):
''' build loss graph '''
with tf.name_scope("loss"):
predictions = tf.reduce_sum(tf.mul(self.gpu_q_layer, self.actions), 1)
max_action_values = None
if double_dqn: # Double Q-Learning:
max_actions = tf.to_int32(tf.argmax(self.gpu_q_layer, 1))
# tf.gather doesn't support multidimensional indexing yet, so we flatten output activations for indexing
indices = tf.range(0, tf.size(max_actions) * num_actions, num_actions) + max_actions
max_action_values = tf.gather(tf.reshape(self.target_q_layer, shape=[-1]), indices)
else:
max_action_values = tf.reduce_max(self.target_q_layer, 1)
targets = tf.stop_gradient(self.rewards + (self.discount_factor * max_action_values * self.terminals))
difference = tf.abs(predictions - targets)
if error_clip >= 0:
quadratic_part = tf.clip_by_value(difference, 0.0, error_clip)
linear_part = difference - quadratic_part
errors = (0.5 * tf.square(quadratic_part)) + (error_clip * linear_part)
else:
errors = (0.5 * tf.square(difference))
return tf.reduce_sum(errors)
def gauss_kl(q_mu, q_sqrt, K):
"""
Compute the KL divergence from
q(x) = N(q_mu, q_sqrt^2)
to
p(x) = N(0, K)
We assume multiple independent distributions, given by the columns of
q_mu and the last dimension of q_sqrt.
q_mu is a matrix, each column contains a mean.
q_sqrt is a 3D tensor, each matrix within is a lower triangular square-root
matrix of the covariance of q.
K is a positive definite matrix: the covariance of p.
"""
L = tf.cholesky(K)
alpha = tf.matrix_triangular_solve(L, q_mu, lower=True)
KL = 0.5 * tf.reduce_sum(tf.square(alpha)) # Mahalanobis term.
num_latent = tf.cast(tf.shape(q_sqrt)[2], float_type)
KL += num_latent * 0.5 * tf.reduce_sum(tf.log(tf.square(tf.diag_part(L)))) # Prior log-det term.
KL += -0.5 * tf.cast(tf.reduce_prod(tf.shape(q_sqrt)[1:]), float_type) # constant term
Lq = tf.matrix_band_part(tf.transpose(q_sqrt, (2, 0, 1)), -1, 0) # force lower triangle
KL += -0.5*tf.reduce_sum(tf.log(tf.square(tf.matrix_diag_part(Lq)))) # logdet
L_tiled = tf.tile(tf.expand_dims(L, 0), tf.pack([tf.shape(Lq)[0], 1, 1]))
LiLq = tf.matrix_triangular_solve(L_tiled, Lq, lower=True)
KL += 0.5 * tf.reduce_sum(tf.square(LiLq)) # Trace term
return KL
def _build_loss(self):
with tf.variable_scope("loss"):
# Compute y_j = r_j * discount*best_qvalue
self.tf_discount = tf.constant(self.discount)
self.qtarget = tf.add(self.pl_rewards, tf.mul(1.0-self.pl_terminals, tf.mul(self.tf_discount, self.pl_qtargets)))
# Select Q-values for given actions
self.actions_one_hot = tf.one_hot(self.pl_actions, self.num_actions, 1.0, 0.0)
self.qvalue_pred = tf.reduce_sum(tf.mul(self.qvalues, self.actions_one_hot), reduction_indices=1)
# Difference between target and predicted Q-network output
self.delta = tf.sub(self.qtarget, self.qvalue_pred)
if self.clip_delta > 0:
# Perform clipping of the error term, default clipping is to (-1, +1) range
self.quadratic_part = tf.minimum(tf.abs(self.delta), tf.constant(self.clip_delta))
self.linear_part = tf.sub(tf.abs(self.delta), self.quadratic_part)
self.delta_square = tf.mul(tf.constant(0.5), tf.square(self.quadratic_part)) + (self.clip_delta*self.linear_part)
#self.delta_clipped = tf.clip_by_value(self.delta, -1.0*self.clip_delta, self.clip_delta)
#self.delta_square = tf.square(self.delta_clipped)
else:
# No error clipping
self.delta_square = tf.square(self.delta)
# Actual loss
if self.batch_accumulator == "sum":
self.loss = tf.reduce_sum(self.delta_square)
else:
self.loss = tf.reduce_mean(self.delta_square)
# Running average of the loss for TensorBoard
self.loss_moving_avg = tf.train.ExponentialMovingAverage(decay=0.999)
self.loss_moving_avg_op = self.loss_moving_avg.apply([self.loss])
def gauss_kl_white_diag(q_mu, q_sqrt, num_latent):
"""
Compute the KL divergence from
q(x) = N(q_mu, q_sqrt^2)
to
p(x) = N(0, I)
We assume num_latent independent distributions, given by the columns of
q_mu and q_sqrt
q_mu is a matrix, each column contains a mean
q_sqrt is a matrix, each column represents the diagonal of a square-root
matrix of the covariance.
num_latent is an integer: the number of independent distributions (equal to
the columns of q_mu and q_sqrt).
"""
KL = 0.5 * tf.reduce_sum(tf.square(q_mu)) # Mahalanobis term
KL += -0.5 * tf.cast(tf.shape(q_sqrt)[0] * num_latent, tf.float64)
KL += -0.5 * tf.reduce_sum(tf.log(tf.square(q_sqrt))) # Log-det of q-cov
KL += 0.5 * tf.reduce_sum(tf.square(q_sqrt)) # Trace term
return KL
y_data = [v[1] for v in vectors_set]
#plt.figure(figsize=(30,40),dpi=20)
# plt.scatter(x_data,y_data,color='r')
# plt.show()
# to make one dimension matrix that the value is between -1 and 1
W = tf.Variable(tf.random_uniform([1],-1.0,1.0),name='W')
# to define the b matrix one dimension, the initial value is 0
b = tf.Variable(tf.zeros([1]),name='b')
# get the predicted value by calculating
y = W * x_data + b
# define the loss that the mean square error between the y and y_data
loss = tf.reduce_mean(tf.square(y-y_data),name='loss')
# to optimize the parameter by using gradient descent
optimizer = tf.train.GradientDescentOptimizer(0.5)
# the progress training is minimum the error value
train = optimizer.minimize(loss,name='train')
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# value of the initializtion w and b
print('W=',sess.run(W),'b=',sess.run(b),'loss=',sess.run(loss))
# training 20 times
for step in range(20):
sess.run(train)
print('W=', sess.run(W), 'b=', sess.run(b), 'loss=', sess.run(loss))
# 回归问题只有1个输出结点
y_ = tf.placeholder(tf.float32, shape=(None, 1), name='y-input')
# 定义一个单层的神经网络前向传播过程，只是简单加权和
w1 = tf.Variable(tf.random_normal([2, 1], stddev=1, seed=1))
y = tf.matmul(x, w1)
# 定义预测多了和预测少了的成本
loss_less = 1
loss_more = 10
loss = tf.reduce_sum(
tf.select(tf.greater(y, y_), loss_more*(y-y_), loss_less*(y_-y)))
train_step = tf.train.AdamOptimizer(0.001).minimize(loss)
# 定义损失函数为均方误差
loss_2 = tf.reduce_mean(tf.square(y_ - y))
train_step_2 = tf.train.AdamOptimizer(0.001).minimize(loss_2)
# 通过随机数生成一个模拟数据集
rdm = RandomState(1)
dataset_size = 128
X = rdm.rand(dataset_size, 2)
# 设置回归的正确值为两个输入加上一个随机量
Y = [[x1 + x2 + rdm.rand()/10.0-0.05] for (x1, x2) in X]
# 训练神经网络
with tf.Session() as sess:
init_op = tf.initialize_all_variables()
sess.run(init_op)
STEPS = 5000
for i in range(STEPS):
def _create_one_cell():
lstm_cell = tf.contrib.rnn.LSTMCell(config.lstm_size,
state_is_tuple=True)
if config.keep_prob < 1.0:
return tf.contrib.rnn.DropoutWrapper(
lstm_cell, output_keep_prob=config.keep_prob)
cell = tf.contrib.rnn.MultiRNNCell(
[_create_one_cell() for _ in range(config.num_layers)],
state_is_tuple=True) if config.num_layers > 1 else _create_one_cell()
val, _ = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)
weight = tf.Variable(
tf.truncated_normal([config.lstm_size, config.input_size]))
bias = tf.Variable(tf.constant(0.1, shape=[config.input_size]))
prediction = tf.matmul(last, weight) + bias
loss = tf.reduce_mean(tf.square(prediction - targets))
optimizer = tf.train.RMSPropOptimizer(learning_rate)
minimize = optimizer.minimize(loss)
with tf.Session(graph=lstm_graph) as sess:
tf.global_variables_initializer().run()
learning_rates_to_use = [
config.init_learning_rate *
(config.learning_rate_decay**max(float(i + 1 - config.init_epoch), 0.0))
for i in range(config.max_epoch)
]
for epoch_step in range(config.max_epoch):
current_lr = learning_rates_to_use[epoch_step]
# Check https://github.com/lilianweng/stock-rnn/blob/master/data_wrapper.py
def loss(self, y, label):
with tf.variable_scope('losses'):
loss = tf.reduce_mean(tf.square(y-label), name = 'mse')
return loss
import numpy as np
import tensorflow as tf
# Model parameters
W = tf.Variable([.3], dtype=tf.float32, name='weights')
b = tf.Variable([-.3], dtype=tf.float32, name='biases')
tf.summary.histogram('W', W)
tf.summary.histogram('b', b)
# Model input and output
x = tf.placeholder(tf.float32, name='x')
linear_model = W * x + b
y = tf.placeholder(tf.float32, name='y')
# loss
loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
tf.summary.scalar('loss', loss)
# optimizer
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
# training data
x_train = [1, 2, 3, 4]
y_train = [0, -1, -2, -3]
# training loop
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init) # reset values to wrong
# input place holders
X = tf.placeholder(tf.float32, [None, seq_length, data_dim])
Y = tf.placeholder(tf.float32, [None, 1])
# build a LSTM network
cell = tf.contrib.rnn.BasicLSTMCell(num_units=hidden_dim,
state_is_tuple=True,
activation=tf.tanh)
outputs, _states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)
Y_pred = tf.contrib.layers.fully_connected(
outputs[:, -1], output_dim,
activation_fn=None) # We use the last cell's output
# cost/loss
loss = tf.reduce_sum(tf.square(Y_pred - Y)) # sum of the squares
# optimizer
optimizer = tf.train.AdamOptimizer(learning_rate)
train = optimizer.minimize(loss)
# RMSE
targets = tf.placeholder(tf.float32, [None, 1])
predictions = tf.placeholder(tf.float32, [None, 1])
rmse = tf.sqrt(tf.reduce_mean(tf.square(targets - predictions)))
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
# Training
for i in range(iterations):
print("{},{}".format(m, n))
housing_data_plus_bias = np.c_[np.ones((m, 1)), housing.data]
print(housing_data_plus_bias.shape)
scaler = StandardScaler()
scaled_housing_data = scaler.fit_transform(housing.data)
scaled_housing_data_plus_bias = np.c_[np.ones((m, 1)), scaled_housing_data]
print(scaled_housing_data_plus_bias.shape)
learning_rate = 0.01
X = tf.placeholder(tf.float32, shape=(None, n + 1), name="X")
y = tf.placeholder(tf.float32, shape=(None, 1), name="y")
theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name='theta')
y_pred = tf.matmul(X, theta, name="predictions")
error = y_pred - y
mse = tf.reduce_mean(tf.square(error), name="mse")
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
training_op = optimizer.minimize(mse)
init = tf.global_variables_initializer()
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
n_epochs = 10
batch_size = 100
n_batches = int(np.ceil(m / batch_size))
def fetch_batch(epoch, batch_index, batch_size):
know = np.random.seed(epoch * n_batches + batch_index)
indices = np.random.randint(m, size=batch_size)
def loss(self):
"""Compute function loss."""
return tf.reduce_sum(
tf.square(tf.linalg.matvec(self.A, self.x) - self.b))
def build_model(self):
print("Setting up model...")
# input_images = First frame of video
self.input_images = tf.placeholder(tf.float32, [self.batch_size, self.crop_size, self.crop_size, self.channels])
self.videos_fake, self.gen_reg, self.generator_variables = self.generator(self.input_images)
self.fake_min = tf.reduce_min(self.videos_fake)
self.fake_max = tf.reduce_max(self.videos_fake)
print('Shapes of videos:')
print('Original:')
print(self.videos.shape)
print('Generated:')
print(self.videos_fake.shape)
self.d_real, self.discriminator_variables = self.discriminator(self.videos, reuse=False)
# merging initial frame and generated to create full forecast "video"
self.videos_fake = tf.stack([self.input_images, self.videos_fake], axis=1)
self.d_fake, _ = self.discriminator(self.videos_fake, reuse=True)
self.g_cost_pure = -tf.reduce_mean(self.d_fake)
# self.g_cost = self.g_cost_pure + 1000 * self.gen_reg
self.d_cost = tf.reduce_mean(self.d_fake) - tf.reduce_mean(self.d_real)
self.videos = tf.reshape(self.videos, [self.batch_size, self.frame_size, self.crop_size, self.crop_size, self.channels])
self.videos_fake = tf.reshape(self.videos_fake, [self.batch_size, self.frame_size, self.crop_size, self.crop_size, self.channels])
help_v = [0,0,0,0,0]
par = 0
for c,k in zip(self.wvars, range(5)):
if c == '1':
help_v[k] = tf.sqrt(tf.reduce_mean(tf.square(tf.subtract(self.videos[:,:,:,:,par], self.videos_fake[:,:,:,:,par]))))
par += 1
else:
help_v[k] = tf.constant(0.0)
self.rmse_temp = help_v[0]
self.rmse_cc = help_v[1]
self.rmse_sh = help_v[2]
self.rmse_sp = help_v[3]
self.rmse_geo = help_v[4]
tf.summary.scalar('rmse_temp', self.rmse_temp)
tf.summary.scalar('rmse_cc', self.rmse_cc)
tf.summary.scalar('rmse_sh', self.rmse_sh)
tf.summary.scalar('rmse_sp', self.rmse_sp)
tf.summary.scalar('rmse_geo', self.rmse_geo)
self.rmse = tf.sqrt(tf.reduce_mean(tf.square(tf.subtract(self.videos, self.videos_fake))))
# self.mae = tf.metrics.mean_absolute_error(self.videos_fake, self.videos)
# error of discriminator failing to evaluate generated sample as fake - good job generator
tf.summary.scalar("g_cost_pure", self.g_cost_pure)
# diff between original image and created image/sequence in generator
tf.summary.scalar("g_cost_regularizer", self.gen_reg)
# error of - saying fake is fake and original is original (when fake == orig and orig == fake)
tf.summary.scalar("d_cost", self.d_cost)
tf.summary.scalar("RMSE_overal", self.rmse)
# tf.summary.tensor_summary("MAE", self.mae)
alpha = tf.random_uniform(
shape=[self.batch_size, 1],
minval=0.,
maxval=1.
)
dim = self.frame_size * self.crop_size * self.crop_size * self.channels
vid = tf.reshape(self.videos, [self.batch_size, dim])
fake = tf.reshape(self.videos_fake, [self.batch_size, dim])
differences = fake - vid
interpolates = vid + (alpha * differences)
d_hat, _ = self.discriminator(tf.reshape(interpolates, [self.batch_size, self.frame_size, self.crop_size,
self.crop_size, self.channels]), reuse=True)
gradients = tf.gradients(d_hat, [interpolates])[0]
slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients), reduction_indices=[1]))
gradient_penalty = tf.reduce_mean((slopes - 1.) ** 2)
self.d_penalty = 10 * gradient_penalty
tf.summary.scalar('d_penalty', self.d_penalty)
self.d_cost_final = self.d_cost + self.d_penalty
tf.summary.scalar("d_cost_penalized", self.d_cost_final)
self.d_adam, self.g_adam = None, None
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
self.d_adam = tf.train.AdamOptimizer(learning_rate=self.learning_rate, beta1=self.beta1, beta2=0.999) \
.minimize(self.d_cost_final, var_list=self.discriminator_variables)
self.g_adam_gan = tf.train.AdamOptimizer(learning_rate=self.learning_rate, beta1=self.beta1, beta2=0.999) \
.minimize(self.g_cost_pure, var_list=self.generator_variables)
self.g_adam_first = tf.train.AdamOptimizer(learning_rate=self.learning_rate, beta1=self.beta1, beta2=0.999) \
.minimize(self.gen_reg, var_list=self.generator_variables)
self.sample = self.videos_fake
self.summary_op = tf.summary.merge_all()
def _pixel_norm(self, epsilon=1e-08):
"""
Pixelwise normalization
"""
return layers.Lambda(lambda x: x * tf.math.rsqrt(
tf.reduce_mean(tf.square(x), axis=-1, keepdims=True) + epsilon))
multi_cells = tf.contrib.rnn.MultiRNNCell(stackedRNNs, state_is_tuple=True)
# RNN Cell(여기서는 LSTM셀임)들을 연결
outputs, _states = tf.nn.dynamic_rnn(multi_cells, X, dtype=tf.float32)
print("outputs: ", outputs)
# [:, -1]를 잘 살펴보자. LSTM RNN의 마지막 (hidden)출력만을 사용했다.
# 과거 여러 거래일의 주가를 이용해서 다음날의 주가 1개를 예측하기때문에 MANY-TO-ONE형태이다
Y_pred = tf.contrib.layers.fully_connected(outputs[:, -1],
1,
activation_fn=None)
#%%
# Loss function define
loss_p = tf.reduce_sum(tf.square(Y_pred - Y))
#loss_dpl = tf.reduce_sum(tf.abs(tf.sign(Y_pred[1:,:] - Y[:-1,:]) - tf.sign(Y[1:,:] - Y[:-1,:]) ) )
#loss_dpl = tf.reduce_sum(Y_pred - Y)
loss = loss_p
# Optimizer
train = tf.train.AdamOptimizer(learning_rate).minimize(loss)
# RMSE(Root Mean Square Error)
rmse = tf.sqrt(tf.reduce_mean(tf.squared_difference(targets, predictions)))
#%%
# Session 초기화
with tf.Session() as sess:
def generator(self, img_batch):
with tf.variable_scope('g_') as vs:
""" -----------------------------------------------------------------------------------
ENCODER
----------------------------------------------------------------------------------- """
print('ENCODER')
self.en_h0 = conv2d(img_batch, self.channels, 128, k_h=4, k_w=4, d_w=2, d_h=2, name="enc_conv1")
self.en_h0 = tf.nn.relu(tf.contrib.layers.batch_norm(self.en_h0))
add_activation_summary(self.en_h0)
print(self.en_h0.get_shape().as_list())
self.en_h1 = conv2d(self.en_h0, 128, 256, k_h=4, k_w=4, d_w=2, d_h=2, name="enc_conv2")
self.en_h1 = tf.contrib.layers.batch_norm(self.en_h1, scope="enc_bn2")
self.en_h1 = tf.nn.relu(self.en_h1)
add_activation_summary(self.en_h1)
print(self.en_h1.get_shape().as_list())
self.en_h2 = conv2d(self.en_h1, 256, 512, k_h=4, k_w=4, d_w=2, d_h=2, name="enc_conv3")
self.en_h2 = tf.contrib.layers.batch_norm(self.en_h2, scope="enc_bn3")
self.en_h2 = tf.nn.relu(self.en_h2)
add_activation_summary(self.en_h2)
print(self.en_h2.get_shape().as_list())
self.en_h3 = conv2d(self.en_h2, 512, 1024, k_h=4, k_w=4, d_w=2, d_h=2, name="enc_conv4")
self.en_h3 = tf.contrib.layers.batch_norm(self.en_h3, scope="enc_bn4")
self.en_h3 = tf.nn.relu(self.en_h3)
add_activation_summary(self.en_h3)
print(self.en_h3.get_shape().as_list())
""" -----------------------------------------------------------------------------------
GENERATOR
----------------------------------------------------------------------------------- """
print('GENERATOR')
self.z_ = tf.reshape(self.en_h3, [self.batch_size, 2, 2, 1024])
print(self.z_.get_shape().as_list())
self.fg_h1 = tf.image.resize_images(self.z_, [4,4], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
self.fg_h1 = conv2d(self.fg_h1, 1024, 512, d_h=1, d_w=1, name="gen_conv1")
self.fg_h1 = tf.nn.relu(tf.contrib.layers.batch_norm(self.fg_h1, scope='g_f_bn1'), name='g_f_relu1')
add_activation_summary(self.fg_h1)
print(self.fg_h1.get_shape().as_list())
self.fg_h2 = tf.image.resize_images(self.fg_h1, [8,8], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
self.fg_h2 = conv2d(self.fg_h2, 512, 256, d_h=1, d_w=1, name="gen_conv2")
self.fg_h2 = tf.nn.relu(tf.contrib.layers.batch_norm(self.fg_h2, scope='g_f_bn2'), name='g_f_relu2')
add_activation_summary(self.fg_h2)
print(self.fg_h2.get_shape().as_list())
self.fg_h3 = tf.image.resize_images(self.fg_h2, [16,16], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
self.fg_h3 = conv2d(self.fg_h3, 256, 128, d_h=1, d_w=1, name="gen_conv3")
self.fg_h3 = tf.nn.relu(tf.contrib.layers.batch_norm(self.fg_h3, scope='g_f_bn3'), name='g_f_relu3')
add_activation_summary(self.fg_h3)
print(self.fg_h3.get_shape().as_list())
self.fg_h4 = tf.image.resize_images(self.fg_h3, [self.crop_size,self.crop_size], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
self.fg_h4 = conv2d(self.fg_h4, 128, self.channels, d_h=1, d_w=1, name="gen_conv4")
self.fg_fg = tf.nn.tanh(self.fg_h4, name='g_f_actication')
print(self.fg_fg.get_shape().as_list())
gen_reg = tf.reduce_mean(tf.square(img_batch - self.fg_fg))
variables = tf.contrib.framework.get_variables(vs)
return self.fg_fg, gen_reg, variables
def create_model(inputsX, inputsY, a):
# Modify values if images are reduced
IMAGE_SIZE = 256
OUTPUT_DIM = IMAGE_SIZE*IMAGE_SIZE*3 # 256x256x3
# Target for inputsX is inputsY and vice versa
targetsX = inputsY
targetsY = inputsX
######### IMAGE_TRANSLATORS
with tf.variable_scope("generatorX2Y_encoder"):
sR_X2Y, eR_X2Y = create_generator_encoder(inputsX, a)
with tf.variable_scope("generatorY2X_encoder"):
sR_Y2X, eR_Y2X = create_generator_encoder(inputsY, a)
# Generate random noise to substitute exclusive rep
z = tf.random_normal(eR_X2Y.shape)
z2 = tf.random_normal(eR_X2Y.shape)
# One copy of the decoder for the noise input, the second copy for the correct the cross-domain autoencoder
with tf.name_scope("generatorX2Y_decoder_noise"):
with tf.variable_scope("generatorX2Y_decoder"):
out_channels = int(targetsX.get_shape()[-1])
outputsX2Y = create_generator_decoder(sR_X2Y, z, out_channels, a)
with tf.variable_scope("generatorX2Y_decoder", reuse=True):
outputsX2Yp = create_generator_decoder(sR_X2Y, z2, out_channels, a)
with tf.name_scope("generatorX2Y_reconstructor"):
with tf.variable_scope("generatorY2X_encoder", reuse=True):
sR_X2Y_recon, eR_X2Y_recon = create_generator_encoder(outputsX2Y, a)
#CYCLE-CONSISTENCY
with tf.name_scope("generatorX2Y_cyc"):
with tf.variable_scope("generatorX2Y_decoder", reuse=True):
outputX2Y_cyc = create_generator_decoder(sR_X2Y_recon, eR_X2Y, out_channels, a)
with tf.name_scope("generatorY2X_decoder_noise"):
with tf.variable_scope("generatorY2X_decoder"):
out_channels = int(targetsY.get_shape()[-1])
outputsY2X = create_generator_decoder(sR_Y2X, z, out_channels, a)
with tf.variable_scope("generatorY2X_decoder", reuse=True):
outputsY2Xp = create_generator_decoder(sR_Y2X, z2, out_channels, a)
with tf.name_scope("generatorY2X_reconstructor"):
with tf.variable_scope("generatorX2Y_encoder", reuse=True):
sR_Y2X_recon, eR_Y2X_recon = create_generator_encoder(outputsY2X, a)
# CYCLE-CONSISTENCY
with tf.name_scope("generatorY2X_cyc"):
with tf.variable_scope("generatorY2X_decoder", reuse=True):
outputY2X_cyc = create_generator_decoder(sR_Y2X_recon, eR_Y2X, out_channels, a)
# create two copies of discriminator, one for real pairs and one for fake pairs
# they share the same underlying variables
# We will now have 2 different discriminators, one per direction, and two
# copies of each for real/fake pairs
with tf.name_scope("real_discriminatorX2Y"):
with tf.variable_scope("discriminatorX2Y"):
predict_realX2Y = create_discriminator(inputsX, targetsX, a)
with tf.name_scope("real_discriminatorY2X"):
with tf.variable_scope("discriminatorY2X"):
predict_realY2X = create_discriminator(inputsY, targetsY, a)
with tf.name_scope("fake_discriminatorX2Y"):
with tf.variable_scope("discriminatorX2Y", reuse=True):
predict_fakeX2Y = create_discriminator(inputsX, outputsX2Y, a)
with tf.name_scope("fake_discriminatorY2X"):
with tf.variable_scope("discriminatorY2X", reuse=True):
predict_fakeY2X = create_discriminator(inputsY, outputsY2X, a)
######### VISUAL ANALOGIES
# This is only for visualization (visual analogies), not used in training loss
with tf.name_scope("image_swapper_X"):
im_swapped_X,sel_auto_X = create_visual_analogy(sR_X2Y, eR_X2Y,
outputX2Y_cyc,inputsX,'Y2X', a)
with tf.name_scope("image_swapper_Y"):
im_swapped_Y,sel_auto_Y = create_visual_analogy(sR_Y2X, eR_Y2X,
outputY2X_cyc,inputsY,'X2Y', a)
######### EXCLUSIVE REPRESENTATION
# Create generators/discriminators for exclusive representation
with tf.variable_scope("generator_exclusiveX2Y_decoder"):
outputs_exclusiveX2Y = create_generator_decoder_exclusive(eR_X2Y, out_channels, a)
with tf.name_scope("real_discriminator_exclusiveX2Y"):
with tf.variable_scope("discriminator_exclusiveX2Y"):
predict_real_exclusiveX2Y = create_discriminator(inputsX, targetsX, a)
with tf.name_scope("fake_discriminator_exclusiveX2Y"):
with tf.variable_scope("discriminator_exclusiveX2Y", reuse=True):
predict_fake_exclusiveX2Y = create_discriminator(inputsX, outputs_exclusiveX2Y, a)
with tf.variable_scope("generator_exclusiveY2X_decoder"):
outputs_exclusiveY2X = create_generator_decoder_exclusive(eR_Y2X, out_channels, a)
with tf.name_scope("real_discriminator_exclusiveY2X"):
with tf.variable_scope("discriminator_exclusiveY2X"):
predict_real_exclusiveY2X = create_discriminator(inputsY, targetsY, a)
with tf.name_scope("fake_discriminator_exclusiveY2Y"):
with tf.variable_scope("discriminator_exclusiveY2X", reuse=True):
predict_fake_exclusiveY2X = create_discriminator(inputsY, outputs_exclusiveY2X, a)
######### SHARED REPRESENTATION
# Create generators/discriminators for exclusive representation
with tf.name_scope("discriminator_sharedX2Y"):
with tf.variable_scope("discriminator_sharedX2Y"):
predict_fake_sharedX2Y = create_domain_classifier(sR_X2Y, a)
with tf.name_scope("discriminator_sharedY2X"):
with tf.variable_scope("discriminator_sharedY2X"):
predict_fake_sharedY2X = create_domain_classifier(sR_Y2X, a)
######### LOSSES
with tf.name_scope("generatorX2Y_loss"):
genX2Y_loss_GAN = -tf.reduce_mean(predict_fakeX2Y)
genX2Y_loss = genX2Y_loss_GAN * a.gan_weight
with tf.name_scope("discriminatorX2Y_loss"):
discrimX2Y_loss = tf.reduce_mean(predict_fakeX2Y) - tf.reduce_mean(predict_realX2Y)
alpha = tf.random_uniform(shape=[a.batch_size,1], minval=0., maxval=1.)
differences = tf.reshape(outputsX2Y,[-1,OUTPUT_DIM])-tf.reshape(targetsX,[-1,OUTPUT_DIM])
interpolates = tf.reshape(targetsX, [-1,OUTPUT_DIM]) + (alpha*differences)
with tf.variable_scope("discriminatorX2Y", reuse=True):
gradients = tf.gradients(create_discriminator(inputsX,tf.reshape(interpolates,[-1,IMAGE_SIZE,IMAGE_SIZE,3]),a),
[interpolates])[0]
slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients),
reduction_indices=[1]))
gradient_penalty = tf.reduce_mean((slopes-1.)**2)
tf.summary.histogram("X2Y/fake_score", predict_fakeX2Y)
tf.summary.histogram("X2Y/real_score", predict_realX2Y)
tf.summary.histogram("X2Y/disc_loss", discrimX2Y_loss )
tf.summary.histogram("X2Y/gradient_penalty", gradient_penalty)
discrimX2Y_loss += LAMBDA*gradient_penalty
with tf.name_scope("generatorY2X_loss"):
genY2X_loss_GAN = -tf.reduce_mean(predict_fakeY2X)
genY2X_loss = genY2X_loss_GAN * a.gan_weight
with tf.name_scope("discriminatorY2X_loss"):
discrimY2X_loss = tf.reduce_mean(predict_fakeY2X) - tf.reduce_mean(predict_realY2X)
alpha = tf.random_uniform(shape=[a.batch_size,1], minval=0., maxval=1.)
differences = tf.reshape(outputsY2X,[-1,OUTPUT_DIM])-tf.reshape(targetsY,[-1,OUTPUT_DIM])
interpolates = tf.reshape(targetsY,[-1,OUTPUT_DIM]) + (alpha*differences)
with tf.variable_scope("discriminatorY2X", reuse=True):
gradients = tf.gradients(create_discriminator(inputsY,tf.reshape(interpolates,[-1,IMAGE_SIZE,IMAGE_SIZE,3]),a),
[interpolates])[0]
slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients),
reduction_indices=[1]))
gradient_penalty = tf.reduce_mean((slopes-1.)**2)
discrimY2X_loss += LAMBDA*gradient_penalty
with tf.name_scope("generator_exclusiveX2Y_loss"):
gen_exclusiveX2Y_loss_GAN = -tf.reduce_mean(predict_fake_exclusiveX2Y)
gen_exclusiveX2Y_loss = gen_exclusiveX2Y_loss_GAN * a.gan_exclusive_weight
with tf.name_scope("discriminator_exclusiveX2Y_loss"):
discrim_exclusiveX2Y_loss = tf.reduce_mean(predict_fake_exclusiveX2Y) - tf.reduce_mean(predict_real_exclusiveX2Y)
alpha = tf.random_uniform(shape=[a.batch_size,1], minval=0., maxval=1.)
differences = tf.reshape(outputs_exclusiveX2Y,[-1,OUTPUT_DIM])-tf.reshape(targetsX,[-1,OUTPUT_DIM])
interpolates = tf.reshape(targetsX,[-1,OUTPUT_DIM]) + (alpha*differences)
with tf.variable_scope("discriminator_exclusiveX2Y", reuse=True):
gradients = tf.gradients(create_discriminator(inputsX,tf.reshape(interpolates,[-1,IMAGE_SIZE,IMAGE_SIZE,3]),a),
[interpolates])[0]
slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients),
reduction_indices=[1]))
gradient_penalty = tf.reduce_mean((slopes-1.)**2)
discrim_exclusiveX2Y_loss += LAMBDA*gradient_penalty
with tf.name_scope("generator_exclusiveY2X_loss"):
gen_exclusiveY2X_loss_GAN = -tf.reduce_mean(predict_fake_exclusiveY2X)
gen_exclusiveY2X_loss = gen_exclusiveY2X_loss_GAN * a.gan_exclusive_weight
with tf.name_scope("discriminator_exclusiveY2X_loss"):
discrim_exclusiveY2X_loss = tf.reduce_mean(predict_fake_exclusiveY2X) - tf.reduce_mean(predict_real_exclusiveY2X)
alpha = tf.random_uniform(shape=[a.batch_size,1], minval=0., maxval=1.)
differences = tf.reshape(outputs_exclusiveY2X,[-1,OUTPUT_DIM])-tf.reshape(targetsX,[-1,OUTPUT_DIM])
interpolates = tf.reshape(targetsX,[-1,OUTPUT_DIM]) + (alpha*differences)
with tf.variable_scope("discriminator_exclusiveY2X", reuse=True):
gradients = tf.gradients(create_discriminator(inputsX,tf.reshape(interpolates,[-1,IMAGE_SIZE,IMAGE_SIZE,3]),a),
[interpolates])[0]
slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients),
reduction_indices=[1]))
gradient_penalty = tf.reduce_mean((slopes-1.)**2)
discrim_exclusiveY2X_loss += LAMBDA*gradient_penalty
#SHARED GRL LOSS
with tf.name_scope("discriminator_sharedX2Y_loss"):
labels_X2Y = tf.zeros([a.batch_size, 1], dtype=tf.float32)
cross_entropyX2Y = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_X2Y, logits=predict_fake_sharedX2Y)
discrim_sharedX2Y_loss = tf.reduce_mean(cross_entropyX2Y)
discrim_sharedX2Y_loss = discrim_sharedX2Y_loss * a.classifier_shared_weight
with tf.name_scope("discriminator_sharedY2X_loss"):
labels_Y2X = tf.ones([a.batch_size, 1], dtype=tf.float32)
cross_entropyY2X = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_Y2X, logits=predict_fake_sharedY2X)
discrim_sharedY2X_loss = tf.reduce_mean(cross_entropyY2X)
discrim_sharedY2X_loss = discrim_sharedY2X_loss * a.classifier_shared_weight
with tf.name_scope("code_recon_loss"):
code_sR_X2Y_recon_loss = tf.reduce_mean(tf.abs(sR_X2Y_recon-sR_X2Y))
code_sR_Y2X_recon_loss = tf.reduce_mean(tf.abs(sR_Y2X_recon-sR_Y2X))
code_eR_X2Y_recon_loss = tf.reduce_mean(tf.abs(eR_X2Y_recon-z))
code_eR_Y2X_recon_loss = tf.reduce_mean(tf.abs(eR_Y2X_recon-z))
code_recon_loss = a.l1_weight*(code_sR_X2Y_recon_loss + code_sR_Y2X_recon_loss
+code_eR_X2Y_recon_loss + code_eR_Y2X_recon_loss)
#CYCLE-CONSISTENCY LOSS
with tf.name_scope("cycX_loss"):
cycX_loss = a.cyc_weight*tf.reduce_mean(tf.abs(outputX2Y_cyc-inputsX))
with tf.name_scope("cycY_loss"):
cycY_loss = a.cyc_weight*tf.reduce_mean(tf.abs(outputY2X_cyc-inputsY))
######### OPTIMIZERS
with tf.name_scope("discriminatorX2Y_train"):
discrimX2Y_tvars = [var for var in tf.trainable_variables() if var.name.startswith("discriminatorX2Y")]
discrimX2Y_optim = tf.train.AdamOptimizer(a.lr, a.beta1)
discrimX2Y_grads_and_vars = discrimX2Y_optim.compute_gradients(discrimX2Y_loss, var_list=discrimX2Y_tvars)
discrimX2Y_train = discrimX2Y_optim.apply_gradients(discrimX2Y_grads_and_vars)
with tf.name_scope("discriminatorY2X_train"):
discrimY2X_tvars = [var for var in tf.trainable_variables() if var.name.startswith("discriminatorY2X")]
discrimY2X_optim = tf.train.AdamOptimizer(a.lr, a.beta1)
discrimY2X_grads_and_vars = discrimY2X_optim.compute_gradients(discrimY2X_loss, var_list=discrimY2X_tvars)
discrimY2X_train = discrimY2X_optim.apply_gradients(discrimY2X_grads_and_vars)
with tf.name_scope("generatorX2Y_train"):
with tf.control_dependencies([discrimX2Y_train]):
genX2Y_tvars = [var for var in tf.trainable_variables() if var.name.startswith("generatorX2Y")]
genX2Y_optim = tf.train.AdamOptimizer(a.lr, a.beta1)
genX2Y_grads_and_vars = genX2Y_optim.compute_gradients(genX2Y_loss, var_list=genX2Y_tvars)
genX2Y_train = genX2Y_optim.apply_gradients(genX2Y_grads_and_vars)
with tf.name_scope("generatorY2X_train"):
with tf.control_dependencies([discrimY2X_train]):
genY2X_tvars = [var for var in tf.trainable_variables() if var.name.startswith("generatorY2X")]
genY2X_optim = tf.train.AdamOptimizer(a.lr, a.beta1)
genY2X_grads_and_vars = genY2X_optim.compute_gradients(genY2X_loss, var_list=genY2X_tvars)
genY2X_train = genY2X_optim.apply_gradients(genY2X_grads_and_vars)
with tf.name_scope("discriminator_exclusiveX2Y_train"):
discrim_exclusiveX2Y_tvars = [var for var in tf.trainable_variables() if var.name.startswith("discriminator_exclusiveX2Y")]
discrim_exclusiveX2Y_optim = tf.train.AdamOptimizer(a.lr, a.beta1)
discrim_exclusiveX2Y_grads_and_vars = discrim_exclusiveX2Y_optim.compute_gradients(discrim_exclusiveX2Y_loss, var_list=discrim_exclusiveX2Y_tvars)
discrim_exclusiveX2Y_train = discrim_exclusiveX2Y_optim.apply_gradients(discrim_exclusiveX2Y_grads_and_vars)
with tf.name_scope("generator_exclusiveX2Y_train"):
with tf.control_dependencies([discrim_exclusiveX2Y_train]):
gen_exclusiveX2Y_tvars = [var for var in tf.trainable_variables()
if var.name.startswith("generator_exclusiveX2Y")
or var.name.startswith("generatorX2Y_encoder")]
gen_exclusiveX2Y_optim = tf.train.AdamOptimizer(a.lr, a.beta1)
gen_exclusiveX2Y_grads_and_vars = gen_exclusiveX2Y_optim.compute_gradients(gen_exclusiveX2Y_loss, var_list=gen_exclusiveX2Y_tvars)
gen_exclusiveX2Y_train = gen_exclusiveX2Y_optim.apply_gradients(gen_exclusiveX2Y_grads_and_vars)
with tf.name_scope("discriminator_exclusiveY2X_train"):
discrim_exclusiveY2X_tvars = [var for var in tf.trainable_variables() if var.name.startswith("discriminator_exclusiveY2X")]
discrim_exclusiveY2X_optim = tf.train.AdamOptimizer(a.lr, a.beta1)
discrim_exclusiveY2X_grads_and_vars = discrim_exclusiveY2X_optim.compute_gradients(discrim_exclusiveY2X_loss, var_list=discrim_exclusiveY2X_tvars)
discrim_exclusiveY2X_train = discrim_exclusiveY2X_optim.apply_gradients(discrim_exclusiveY2X_grads_and_vars)
with tf.name_scope("generator_exclusiveY2X_train"):
with tf.control_dependencies([discrim_exclusiveY2X_train]):
gen_exclusiveY2X_tvars = [var for var in tf.trainable_variables()
if var.name.startswith("generator_exclusiveY2X")
or var.name.startswith("generatorY2X_encoder")]
gen_exclusiveY2X_optim = tf.train.AdamOptimizer(a.lr, a.beta1)
gen_exclusiveY2X_grads_and_vars = gen_exclusiveY2X_optim.compute_gradients(gen_exclusiveY2X_loss, var_list=gen_exclusiveY2X_tvars)
gen_exclusiveY2X_train = gen_exclusiveY2X_optim.apply_gradients(gen_exclusiveY2X_grads_and_vars)
#SHARED GRL OPTIMIZATION
with tf.name_scope("discriminator_sharedX2Y_train"):
discrim_sharedX2Y_tvars = [var for var in tf.trainable_variables() if
var.name.startswith("discriminator_sharedX2Y")]
discrim_sharedX2Y_optim = tf.train.AdamOptimizer(a.lr, a.beta1)
discrim_sharedX2Y_grads_and_vars = discrim_sharedX2Y_optim.compute_gradients(discrim_sharedX2Y_loss,
var_list=discrim_sharedX2Y_tvars)
discrim_sharedX2Y_train = discrim_sharedX2Y_optim.apply_gradients(discrim_sharedX2Y_grads_and_vars)
with tf.name_scope("discriminator_sharedY2X_train"):
discrim_sharedY2X_tvars = [var for var in tf.trainable_variables() if
var.name.startswith("discriminator_sharedY2X")]
discrim_sharedY2X_optim = tf.train.AdamOptimizer(a.lr, a.beta1)
discrim_sharedY2X_grads_and_vars = discrim_sharedY2X_optim.compute_gradients(discrim_sharedY2X_loss,
var_list=discrim_sharedY2X_tvars)
discrim_sharedY2X_train = discrim_sharedY2X_optim.apply_gradients(discrim_sharedY2X_grads_and_vars)
with tf.name_scope("code_recon_train"):
code_recon_tvars = [var for var in tf.trainable_variables() if
var.name.startswith("generatorX2Y") or
var.name.startswith("generatorY2X")]
code_recon_optim = tf.train.AdamOptimizer(a.lr, a.beta1)
code_recon_grads_and_vars = code_recon_optim.compute_gradients(code_recon_loss, var_list=code_recon_tvars)
code_recon_train = code_recon_optim.apply_gradients(code_recon_grads_and_vars)
#CYCLE-CONSISTENCY OPTIMIZATION
with tf.name_scope("generatorX2Y_cyc_train"):
cycX_tvars = [var for var in tf.trainable_variables() if
var.name.startswith("generatorX2Y_encoder") or
var.name.startswith("generatorX2Y_decoder") or
var.name.startswith("generatorY2X_encoder") or
var.name.startswith("generatorY2X_decoder")]
cycX_optim = tf.train.AdamOptimizer(a.lr, a.beta1)
cycX_grads_and_vars = cycX_optim.compute_gradients(cycX_loss, var_list=cycX_tvars)
cycX_train = cycX_optim.apply_gradients(cycX_grads_and_vars)
with tf.name_scope("generatorY2X_cyc_train"):
cycY_tvars = [var for var in tf.trainable_variables() if
var.name.startswith("generatorX2Y_encoder") or
var.name.startswith("generatorX2Y_decoder") or
var.name.startswith("generatorY2X_encoder") or
var.name.startswith("generatorY2X_decoder")]
cycY_optim = tf.train.AdamOptimizer(a.lr, a.beta1)
cycY_grads_and_vars = cycY_optim.compute_gradients(cycY_loss, var_list=cycY_tvars)
cycY_train = cycY_optim.apply_gradients(cycY_grads_and_vars)
ema = tf.train.ExponentialMovingAverage(decay=0.99)
update_losses = ema.apply([discrimX2Y_loss, discrimY2X_loss,
genX2Y_loss, genY2X_loss,
code_recon_loss,
code_sR_X2Y_recon_loss, code_sR_Y2X_recon_loss,
code_eR_X2Y_recon_loss, code_eR_Y2X_recon_loss,
discrim_exclusiveX2Y_loss, discrim_exclusiveY2X_loss,
gen_exclusiveX2Y_loss, gen_exclusiveY2X_loss,
discrim_sharedX2Y_loss, discrim_sharedY2X_loss,
cycX_loss, cycY_loss])
global_step = tf.train.get_or_create_global_step()
incr_global_step = tf.assign(global_step, global_step+1)
return Model(
predict_realX2Y=predict_realX2Y,
predict_realY2X=predict_realY2X,
predict_fakeX2Y=predict_fakeX2Y,
predict_fakeY2X=predict_fakeY2X,
im_swapped_X=im_swapped_X,
im_swapped_Y=im_swapped_Y,
sel_auto_X=sel_auto_X,
sel_auto_Y=sel_auto_Y,
sR_X2Y=sR_X2Y,
sR_Y2X=sR_Y2X,
eR_X2Y=eR_X2Y,
eR_Y2X=eR_Y2X,
discrimX2Y_loss=ema.average(discrimX2Y_loss),
discrimY2X_loss=ema.average(discrimY2X_loss),
genX2Y_loss=ema.average(genX2Y_loss),
genY2X_loss=ema.average(genY2X_loss),
discrim_exclusiveX2Y_loss=ema.average(discrim_exclusiveX2Y_loss),
discrim_exclusiveY2X_loss=ema.average(discrim_exclusiveY2X_loss),
gen_exclusiveX2Y_loss=ema.average(gen_exclusiveX2Y_loss),
gen_exclusiveY2X_loss=ema.average(gen_exclusiveY2X_loss),
discrim_sharedX2Y_loss=ema.average(discrim_sharedX2Y_loss),
discrim_sharedY2X_loss=ema.average(discrim_sharedY2X_loss),
outputsX2Y=outputsX2Y,
outputsY2X=outputsY2X,
outputsX2Yp=outputsX2Yp,
outputsY2Xp=outputsY2Xp,
outputs_exclusiveX2Y=outputs_exclusiveX2Y,
outputs_exclusiveY2X=outputs_exclusiveY2X,
cycX_output=outputX2Y_cyc,
cycX_loss=ema.average(cycX_loss),
cycY_output=outputY2X_cyc,
cycY_loss=ema.average(cycY_loss),
code_recon_loss=ema.average(code_recon_loss),
code_sR_X2Y_recon_loss=ema.average(code_sR_X2Y_recon_loss),
code_sR_Y2X_recon_loss=ema.average(code_sR_Y2X_recon_loss),
code_eR_X2Y_recon_loss=ema.average(code_eR_X2Y_recon_loss),
code_eR_Y2X_recon_loss=ema.average(code_eR_Y2X_recon_loss),
train=tf.group(update_losses, incr_global_step, genX2Y_train,
genY2X_train, code_recon_train,
gen_exclusiveX2Y_train,gen_exclusiveY2X_train,
discrim_sharedX2Y_train,discrim_sharedY2X_train,
cycX_train, cycY_train),
)
model_output = tf.add(tf.matmul(x_data, A), b)
###
# Loss Functions
###
# Select appropriate loss function based on regression type
if regression_type == 'LASSO':
# Declare Lasso loss function
# Lasso Loss = L2_Loss + heavyside_step,
# Where heavyside_step ~ 0 if A < constant, otherwise ~ 99
lasso_param = tf.constant(0.9)
heavyside_step = tf.truediv(1., tf.add(1., tf.exp(tf.multiply(-50., tf.subtract(A, lasso_param)))))
regularization_param = tf.multiply(heavyside_step, 99.)
loss = tf.add(tf.reduce_mean(input_tensor=tf.square(y_target - model_output)), regularization_param)
elif regression_type == 'Ridge':
# Declare the Ridge loss function
# Ridge loss = L2_loss + L2 norm of slope
ridge_param = tf.constant(1.)
ridge_loss = tf.reduce_mean(input_tensor=tf.square(A))
loss = tf.expand_dims(tf.add(tf.reduce_mean(input_tensor=tf.square(y_target - model_output)), tf.multiply(ridge_param, ridge_loss)), 0)
else:
print('Invalid regression_type parameter value',file=sys.stderr)
###
# Optimizer
###
def generate(self, x, **kwargs):
"""
Generate symbolic graph for adversarial examples and return.
:param x: The model's symbolic inputs.
:param eps: (required float) maximum distortion of adversarial example
compared to original input
:param eps_iter: (required float) step size for each attack iteration
:param nb_iter: (required int) Number of attack iterations.
:param y: (optional) A tensor with the model labels.
:param y_target: (optional) A tensor with the labels to target. Leave
y_target=None if y is also set. Labels should be
one-hot-encoded.
:param ord: (optional) Order of the norm (mimics Numpy).
Possible values: np.inf, 1 or 2.
:param clip_min: (optional float) Minimum input component value
:param clip_max: (optional float) Maximum input component value
"""
import tensorflow as tf
# Parse and save attack-specific parameters
assert self.parse_params(**kwargs)
# Initialize loop variables
eta = 0
# Fix labels to the first model predictions for loss computation
model_preds = self.model.get_probs(x)
preds_max = tf.reduce_max(model_preds, 1, keep_dims=True)
if self.y_target is not None:
y = self.y_target
targeted = True
elif self.y is not None:
y = self.y
targeted = False
else:
y = tf.to_float(tf.equal(model_preds, preds_max))
y = tf.stop_gradient(y)
targeted = False
y_kwarg = 'y_target' if targeted else 'y'
fgm_params = {'eps': self.eps_iter, y_kwarg: y, 'ord': self.ord,
'clip_min': self.clip_min, 'clip_max': self.clip_max}
for i in range(self.nb_iter):
FGM = FastGradientMethod(self.model, back=self.back,
sess=self.sess)
# Compute this step's perturbation
eta = FGM.generate(x + eta, **fgm_params) - x
# Clipping perturbation eta to self.ord norm ball
if self.ord == np.inf:
eta = tf.clip_by_value(eta, -self.eps, self.eps)
elif self.ord in [1, 2]:
reduc_ind = list(xrange(1, len(eta.get_shape())))
if self.ord == 1:
norm = tf.reduce_sum(tf.abs(eta),
reduction_indices=reduc_ind,
keep_dims=True)
elif self.ord == 2:
norm = tf.sqrt(tf.reduce_sum(tf.square(eta),
reduction_indices=reduc_ind,
keep_dims=True))
eta = eta * self.eps / norm
# Define adversarial example (and clip if necessary)
adv_x = x + eta
if self.clip_min is not None and self.clip_max is not None:
adv_x = tf.clip_by_value(adv_x, self.clip_min, self.clip_max)
return adv_x
def main():
traincsvdata = np.loadtxt('trainset.csv', unpack=True, delimiter=',', skiprows=1)
num_points = len(traincsvdata[0])
print("training points : ", num_points)
x_data = traincsvdata[0] # [230. 280. 241....
y_data = traincsvdata[1] # [1349.9 1809. 1590.8 1571.8 1768.3
#학습용 데이터셋을 녹색(g)에 둥근점(o)로 시각화
plt.suptitle('Training Data Set', fontsize=16)
plt.plot(x_data, y_data, 'go')
plt.xlabel('weight')
plt.ylabel('distance')
plt.show()
#데이터 정규화 진행. 데이터범위를 0~1사이 값으로 변환.
x_data = minmax_normalize(x_data)
y_data = minmax_normalize(y_data)
#배치단위로 학습
BATCH_SIZE = 5
BATCH_NUM = int(len(x_data)/BATCH_SIZE)
#데이터를 세로로(한개씩)나열한 형태로 reshape
x_data = np.reshape(x_data, [len(x_data),1]) # [[230.] [280.] ...
y_data = np.reshape(y_data, [len(y_data),1])
#총 개수는 정해지지 않았고 1개씩 들어가는 Placeholder 생성
input_data = tf.placeholder(tf.float32, shape=[None,1])
output_data = tf.placeholder(tf.float32, shape=[None,1])
#레이어간 Weight 정의후 랜덤값으로 초기화. 그림에서는 선으로 표시.
W1 = tf.Variable(tf.random_uniform([1,5], 0.0, 300.0))
W2 = tf.Variable(tf.random_uniform([5,3], 0.0, 1.0))
W_out = tf.Variable(tf.random_uniform([3,1], 0.0, 300.0))
#레이어의 노드가 하는 계산. 이전노드와 현재노드의 곱셈. 비선형함수로 sigmoid 추가.
hidden1 = tf.nn.sigmoid(tf.matmul(input_data,W1))
hidden2 = tf.nn.sigmoid(tf.matmul(hidden1,W2))
output = tf.matmul(hidden2, W_out)
#비용함수, 최적화함수, train 정의
loss = tf.reduce_mean(tf.square(output-output_data))
optimizer = tf.train.AdamOptimizer(0.1)
train = optimizer.minimize(loss)
#변수(Variable) 사용준비
init = tf.global_variables_initializer()
#세션 열고 init 실행
sess= tf.Session()
sess.run(init)
#학습을 반복하며 값 업데이트
for step in range(4000):
index = 0
#매번 데이터셋을 섞음
x_data, y_data = shuffle_data(x_data, y_data)
#배치크기만큼 학습을 진행
for batch_iter in range(BATCH_NUM-1):
feed_dict = {input_data: x_data[index:index+BATCH_SIZE], output_data: y_data[index:index+BATCH_SIZE]}
sess.run(train, feed_dict = feed_dict)
index += BATCH_SIZE
#화면에 학습진행상태 출력(최초100회까지는 10마다 한번씩, 이후는 100회에 한번씩)
if (step%200==0):
print("Step=%5d, Loss Value=%f" %(step, sess.run(loss, feed_dict = feed_dict)))
#학습이 끝난후 그래프로 결과확인
print("## 학습결과 그래프 ##")
#학습용 데이터셋을 녹색(g)에 둥근점(o)로 시각화
plt.plot(x_data, y_data, 'go')
#예측모델 출력은 검은색(k) 별표(*)로 시각화
feed_dict = {input_data: x_data}
plt.plot(x_data, sess.run(output, feed_dict=feed_dict), 'k*')
#그래프 그리기
plt.suptitle('Training Result', fontsize=16)
plt.xlabel('weight')
plt.ylabel('distance')
plt.show()
print("# 학습결과 이륙거리계산 #")
ask_x = 270
ask_norm_x = [[minmax_get_norm(ask_x, traincsvdata[0])]]
answer_norm_y = sess.run(output, feed_dict={input_data: ask_norm_x})
answer_y = minmax_get_denorm(answer_norm_y, traincsvdata[1])
print("> 무게(X):", ask_x, "ton => 이륙거리Y:", answer_y[0][0], "m \n\n\n")
#테스트셋을 활용한 결과확인
print("## Test Set 검증결과 그래프 ##")
#테스트셋 파일읽음
test_csv_x_data = np.loadtxt('testset_x.csv', unpack=True, delimiter=',', skiprows=1)
test_csv_y_data = np.loadtxt('testset_y.csv', unpack=True, delimiter=',', skiprows=1)
#테스트셋 정규화 진행
test_norm_x_data = minmax_normalize(test_csv_x_data)
test_norm_y_data = minmax_normalize(test_csv_y_data)
#CVS 데이터(테스트셋, 정답데이터) : 빨간색(m)에 둥근점(o)로 시각화
plt.plot(test_csv_x_data, test_csv_y_data, 'mo')
#예측데이터 : 검은색(k) 별표(*)로 시각화
feed_dict = {input_data: np.reshape(test_norm_x_data, (len(test_norm_x_data), 1)) } #[[0.41573034] [0.20224719] ...
test_pred_y_data = minmax_get_denorm(sess.run(output, feed_dict=feed_dict), traincsvdata[1])
plt.plot(test_csv_x_data, test_pred_y_data, 'k*')
#그래프 그리기
plt.suptitle('Test Result', fontsize=16)
plt.xlabel('weight')
plt.ylabel('distance')
plt.show()
# 必要なライブラリのインポート
import tensorflow as tf
import numpy as np
# 変数の定義
dim = 5
x = tf.placeholder(tf.float32, [None, dim + 1])
w = tf.Variable(tf.zeros([dim + 1, 1]))
y = tf.matmul(x, w)
t = tf.placeholder(tf.float32, [None, 1])
sess = tf.Session()
# 損失関数と学習メソッドの定義
loss = tf.reduce_sum(tf.square(y - t))
train_step = tf.train.AdamOptimizer().minimize(loss)
# TensorBoardで追跡する変数を定義
with tf.name_scope('summary'):
tf.summary.scalar('loss', loss)
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter('./logs', sess.graph)
# セッションの初期化と入力データの準備
sess.run(tf.global_variables_initializer())
train_t = np.array(
[5.2, 5.7, 8.6, 14.9, 18.2, 20.4, 25.5, 26.4, 22.8, 17.5, 11.1, 6.6])
train_t = train_t.reshape([12, 1])
train_x = np.zeros([12, dim + 1])
for row, month in enumerate(range(1, 13)):
for col, n in enumerate(range(0, dim + 1)):
def __init__(self,
mixing_weights,
component_params,
component_dist,
validate_args=False,
allow_nan_stats=True,
name="ParamMixture"):
"""Initialize a batch of mixture random variables.
Args:
mixing_weights: tf.Tensor.
(Normalized) weights whose inner (right-most) dimension matches
the number of components.
component_params: dict.
Parameters of the per-component distributions.
component_dist: RandomVariable.
Distribution of each component. The outer (left-most) dimension
of its batch shape when instantiated determines the number of
components.
"""
parameters = locals()
parameters.pop("self")
values = [mixing_weights] + list(six.itervalues(component_params))
with tf.name_scope(name, values=values):
if validate_args:
if not isinstance(component_params, dict):
raise TypeError("component_params must be a dict.")
elif not issubclass(component_dist, RandomVariable):
raise TypeError(
"component_dist must be a ed.RandomVariable object.")
# get sample_shape from inherited RandomVariable specifically
if hasattr(self, '_kwargs'):
sample_shape = self._kwargs.get('sample_shape', ())
else:
sample_shape = ()
self._mixing_weights = tf.identity(mixing_weights,
name="mixing_weights")
self._cat = Categorical(probs=self._mixing_weights,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
sample_shape=sample_shape)
self._component_params = component_params
self._components = component_dist(validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
sample_shape=sample_shape,
collections=[],
**component_params)
if validate_args:
if not self._mixing_weights.shape[-1].is_compatible_with(
self._components.batch_shape[0]):
raise TypeError(
"Last dimension of mixing_weights must match with "
"the first dimension of components.")
elif not self._mixing_weights.shape[:-1].is_compatible_with(
self._components.batch_shape[1:]):
raise TypeError(
"Dimensions of mixing_weights are not compatible "
"with the dimensions of components.")
try:
self._num_components = self._cat.probs.shape.as_list()[-1]
except: # if p has TensorShape None
raise NotImplementedError(
"Number of components must be statically "
"determined.")
self._mean_val = None
self._variance_val = None
self._stddev_val = None
if self._cat.probs.shape.ndims <= 1:
with tf.name_scope('means'):
try:
comp_means = self._components.mean()
comp_vars = self._components.variance()
comp_mean_sq = tf.square(comp_means) + comp_vars
# weights has shape batch_shape + [num_components]; change
# to broadcast with [num_components] + batch_shape + event_shape.
# The below reshaping only works for empty batch_shape.
weights = self._cat.probs
event_rank = self._components.event_shape.ndims
for _ in range(event_rank):
weights = tf.expand_dims(weights, -1)
self._mean_val = tf.reduce_sum(comp_means * weights,
0,
name='mean')
mean_sq_val = tf.reduce_sum(comp_mean_sq * weights,
0,
name='mean_squared')
self._variance_val = tf.subtract(mean_sq_val,
tf.square(
self._mean_val),
name='variance')
self._stddev_val = tf.sqrt(self._variance_val,
name='stddev')
except:
# This fails if _components.{mean,variance}() fails.
pass
super(distributions_ParamMixture, self).__init__(
dtype=self._components.dtype,
reparameterization_type=self._components.reparameterization_type,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._cat.value(),
self._components.value()],
name=name)
def _init_graph(self):
'''
Init a tensorflow Graph containing: input data, variables, model, loss, optimizer
'''
self.graph = tf.Graph()
with self.graph.as_default(): # , tf.device('/cpu:0'):
# Set graph level random seed
tf.set_random_seed(self.random_seed)
# Input data.
self.train_features = tf.placeholder(tf.int32, shape=[None, None]) # None * features_M
self.train_labels = tf.placeholder(tf.float32, shape=[None, 1]) # None * 1
self.dropout_keep = tf.placeholder(tf.float32, shape=[None])
self.train_phase = tf.placeholder(tf.bool)
# Variables.
self.weights = self._initialize_weights()
# Model.
# _________ sum_square part _____________
# get the summed up embeddings of features.
nonzero_embeddings = tf.nn.embedding_lookup(self.weights['feature_embeddings'], self.train_features)
self.summed_features_emb = tf.reduce_sum(nonzero_embeddings, 1) # None * K
# get the element-multiplication
self.summed_features_emb_square = tf.square(self.summed_features_emb) # None * K
# _________ square_sum part _____________
self.squared_features_emb = tf.square(nonzero_embeddings)
self.squared_sum_features_emb = tf.reduce_sum(self.squared_features_emb, 1) # None * K
# ________ FM __________
self.FM = 0.5 * tf.subtract (self.summed_features_emb_square, self.squared_sum_features_emb) # None * K
if self.batch_norm:
self.FM = self.batch_norm_layer(self.FM, train_phase=self.train_phase, scope_bn='bn_fm')
self.FM = tf.nn.dropout(self.FM, self.dropout_keep[-1]) # dropout at the bilinear interactin layer
# ________ Deep Layers __________
for i in range(0, len(self.layers)):
self.FM = tf.add(tf.matmul(self.FM, self.weights['layer_%d' %i]), self.weights['bias_%d'%i]) # None * layer[i] * 1
if self.batch_norm:
self.FM = self.batch_norm_layer(self.FM, train_phase=self.train_phase, scope_bn='bn_%d' %i) # None * layer[i] * 1
self.FM = self.activation_function(self.FM)
self.FM = tf.nn.dropout(self.FM, self.dropout_keep[i]) # dropout at each Deep layer
self.FM = tf.matmul(self.FM, self.weights['prediction']) # None * 1
# _________out _________
Bilinear = tf.reduce_sum(self.FM, 1, keep_dims=True) # None * 1
self.Feature_bias = tf.reduce_sum(tf.nn.embedding_lookup(self.weights['feature_bias'], self.train_features) , 1) # None * 1
Bias = self.weights['bias'] * tf.ones_like(self.train_labels) # None * 1
self.out = tf.add_n([Bilinear, self.Feature_bias, Bias]) # None * 1
# Compute the loss.
if self.loss_type == 'square_loss':
if self.lamda_bilinear > 0:
self.loss = tf.nn.l2_loss(tf.subtract (self.train_labels, self.out)) + tf.contrib.layers.l2_regularizer(self.lamda_bilinear)(self.weights['feature_embeddings']) # regulizer
else:
self.loss = tf.nn.l2_loss(tf.subtract (self.train_labels, self.out))
elif self.loss_type == 'log_loss':
self.out = tf.sigmoid(self.out)
if self.lambda_bilinear > 0:
self.loss = tf.contrib.losses.log_loss(self.out, self.train_labels, weight=1.0, epsilon=1e-07, scope=None) + tf.contrib.layers.l2_regularizer(self.lamda_bilinear)(self.weights['feature_embeddings']) # regulizer
else:
self.loss = tf.contrib.losses.log_loss(self.out, self.train_labels, weight=1.0, epsilon=1e-07, scope=None)
# Optimizer.
if self.optimizer_type == 'AdamOptimizer':
self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-8).minimize(self.loss)
elif self.optimizer_type == 'AdagradOptimizer':
self.optimizer = tf.train.AdagradOptimizer(learning_rate=self.learning_rate, initial_accumulator_value=1e-8).minimize(self.loss)
elif self.optimizer_type == 'GradientDescentOptimizer':
self.optimizer = tf.train.GradientDescentOptimizer(learning_rate=self.learning_rate).minimize(self.loss)
elif self.optimizer_type == 'MomentumOptimizer':
self.optimizer = tf.train.MomentumOptimizer(learning_rate=self.learning_rate, momentum=0.95).minimize(self.loss)
# init
self.saver = tf.train.Saver()
init = tf.global_variables_initializer()
self.sess = tf.Session()
self.sess.run(init)
# number of params
total_parameters = 0
for variable in self.weights.values():
shape = variable.get_shape() # shape is an array of tf.Dimension
variable_parameters = 1
for dim in shape:
variable_parameters *= dim.value
total_parameters += variable_parameters
if self.verbose > 0:
print("#params: %d" %total_parameters)
def build_model(self, is_train):
self.abstract_size = self.sample_size // 2 ** 4
_,_,images= get_pipeline_training_from_dump('data_example.tfrecords',
self.batch_size*3,
1000, image_size=60,resize_size=60,
img_channels=self.c_dim)
_,_,test_images1 = get_pipeline_training_from_dump('data_example.tfrecords',
self.batch_size*2,
10000000, image_size=60,resize_size=60,
img_channels=self.c_dim)
self.images = images[0:self.batch_size,:,:,:]
self.imagesR = images[self.batch_size:self.batch_size*2,:,:,:]
self.third_image = images[self.batch_size*2:self.batch_size*3,:,:,:]
self.test_images1 = test_images1[0:self.batch_size,:,:,:]
self.test_images2 = test_images1[self.batch_size:self.batch_size*2,:,:,:]
self.chunk_num = 8
self.chunk_size = 64
self.feature_size = self.chunk_size*self.chunk_num
with tf.variable_scope('generator') as scope:
self.rep = self.encoder(self.images)
self.D_I = self.generator(self.rep)
_ = self.classifier(self.D_I,self.D_I,self.D_I)
scope.reuse_variables()
self.repR = self.encoder(self.imagesR)
self.D_IR = self.generator(self.repR)
k = tf.random_uniform(shape=[self.chunk_num],minval=0,maxval=2,dtype=tf.int32)
a_chunk = tf.ones((self.batch_size,self.chunk_size),dtype=tf.int32)
a_fea = tf.ones_like(self.rep,dtype=tf.int32)
i=0
t1 = self.rep[:,i*self.chunk_size:(i+1)*self.chunk_size]
e1 = self.repR[:,i*self.chunk_size:(i+1)*self.chunk_size]
self.fea = tf.where(tf.equal(k[0]*a_chunk,0),t1,e1)
self.fea_mix = self.fea
self.feaR = tf.where(tf.equal(k[0]*a_chunk,1),t1,e1)
self.fea_mixR = self.feaR
# mix the feature
for i in xrange(1,self.chunk_num):
t1 = self.rep[:,i*self.chunk_size:(i+1)*self.chunk_size]
e1 = self.repR[:,i*self.chunk_size:(i+1)*self.chunk_size]
self.fea = tf.where(tf.equal(k[i]*a_chunk,0),t1,e1)
self.fea_mix = tf.concat(axis=1,values=[self.fea_mix,self.fea])
self.feaR = tf.where(tf.equal(k[i]*a_chunk,1),t1,e1)
self.fea_mixR = tf.concat(axis=1,values=[self.fea_mixR,self.feaR])
self.k = k
self.k0 = k[0]
self.D_mix = self.generator(self.fea_mix)
self.cf = self.classifier(self.images,self.imagesR,self.D_mix)
self.kfc = tf.cast(tf.ones((self.batch_size,self.chunk_num),dtype=tf.int32)*k,tf.float32)
self.D_mixR = self.generator(self.fea_mixR)
self.rep_mix = self.encoder(self.D_mix)
i = 0
tt = self.rep_mix[:,i*self.chunk_size:(i+1)*self.chunk_size]
ee = self.rep[:,i*self.chunk_size:(i+1)*self.chunk_size]
eeR = self.repR[:,i*self.chunk_size:(i+1)*self.chunk_size]
self.rep_re = tf.where(tf.equal(k[i]*a_chunk,0),tt,ee)
self.repR_re = tf.where(tf.equal(k[i]*a_chunk,1),tt,eeR)
for i in xrange(1,self.chunk_num):
tt = self.rep_mix[:,i*self.chunk_size:(i+1)*self.chunk_size]
ee = self.rep[:,i*self.chunk_size:(i+1)*self.chunk_size]
eeR = self.repR[:,i*self.chunk_size:(i+1)*self.chunk_size]
self.rep_re = tf.concat(axis=1,values=[self.rep_re,tf.where(tf.equal(k[i]*a_chunk,0),tt,ee)])
self.repR_re = tf.concat(axis=1,values=[self.repR_re,tf.where(tf.equal(k[i]*a_chunk,1),tt,eeR)])
self.D_regenerate1 = self.generator(self.rep_re)
self.D_regenerate2 = self.generator(self.repR_re)
scope.reuse_variables()
self.rep_test1 = self.encoder(self.test_images1)
self.rep_test2 = self.encoder(self.test_images2)
i = 0
self.rep_test = self.rep_test2[:,0*self.chunk_size:1*self.chunk_size]
for j in xrange(1,self.chunk_num):
tmp = self.rep_test1[:,j*self.chunk_size:(j+1)*self.chunk_size]
self.rep_test = tf.concat(axis=1,values=[self.rep_test,tmp])
self.D_mix_allchunk = self.generator(self.rep_test,reuse=True)
self.D_mix_allchunk_sup = self.D_mix_allchunk
for i in xrange(1,self.chunk_num):
self.rep_test = self.rep_test1[:,0*self.chunk_size:1*self.chunk_size]
for j in xrange(1,self.chunk_num):
if j==i:
tmp = self.rep_test2[:,j*self.chunk_size:(j+1)*self.chunk_size]
self.rep_test = tf.concat(axis=1,values=[self.rep_test,tmp])
else:
tmp = self.rep_test1[:,j*self.chunk_size:(j+1)*self.chunk_size]
self.rep_test = tf.concat(axis=1,values=[self.rep_test,tmp])
tmp_mix = self.generator(self.rep_test)
self.D_mix_allchunk = tf.concat(axis=0,values=[self.D_mix_allchunk,tmp_mix])
for i in xrange(1,self.chunk_num):
self.rep_test = self.rep_test2[:,0*self.chunk_size:1*self.chunk_size]
for j in xrange(1,self.chunk_num):
if j<=i:
tmp = self.rep_test2[:,j*self.chunk_size:(j+1)*self.chunk_size]
self.rep_test = tf.concat(axis=1,values=[self.rep_test,tmp])
else:
tmp = self.rep_test1[:,j*self.chunk_size:(j+1)*self.chunk_size]
self.rep_test = tf.concat(axis=1,values=[self.rep_test,tmp])
tmp_mix = self.generator(self.rep_test)
self.D_mix_allchunk_sup = tf.concat(axis=0,values=[self.D_mix_allchunk_sup,tmp_mix])
with tf.variable_scope('classifier_loss') as scope:
self.cf_loss = binary_cross_entropy_with_logits(self.kfc,self.cf)
with tf.variable_scope('discriminator') as scope:
self.D = self.discriminator(self.images)
self.D_ = self.discriminator(self.D_mix, reuse=True)
with tf.variable_scope('discriminator_loss') as scope:
self.d_loss_real = binary_cross_entropy_with_logits(tf.ones_like(self.D), self.D)
self.d_loss_fake = binary_cross_entropy_with_logits(tf.zeros_like(self.D_), self.D_)
self.d_loss = self.d_loss_real + self.d_loss_fake
with tf.variable_scope('generator_loss') as scope:
self.g_loss = binary_cross_entropy_with_logits(tf.ones_like(self.D_), self.D_)
with tf.variable_scope('L2') as scope:
self.rec_loss = tf.reduce_mean(tf.square(self.D_I - self.images))
self.recR_loss = tf.reduce_mean(tf.square(self.D_IR - self.imagesR))
self.rec_mix_loss = tf.reduce_mean(tf.square(self.D_regenerate1 - self.images))
self.recR_mix_loss = tf.reduce_mean(tf.square(self.D_regenerate2 - self.imagesR))
self.bn_assigners = tf.group(*batch_norm.assigners)
t_vars = tf.trainable_variables()
self.d_vars = [var for var in t_vars if 'd_' in var.name]
self.g_vars = [var for var in t_vars if 'g_' in var.name]
self.g_s_vars = [var for var in t_vars if 'g_s' in var.name]
self.g_e_vars = [var for var in t_vars if 'g_en' in var.name]
self.c_vars = [var for var in t_vars if 'c_' in var.name]
self.saver = tf.train.Saver(self.d_vars + self.g_vars + self.c_vars+
batch_norm.shadow_variables,
max_to_keep=0)
def square(x, name=None):
return _auto_upcast(tf.square(x, name))
# Build graph
real_data = tf.placeholder(tf.float32, shape=[None, OUTPUT_DIM])
input_noise = tf.placeholder(tf.float32, shape=[None, NOISE_DIM])
fake_data = Generator(BATCH_SIZE, input_noise)
dis_real, real_noise = Discriminator(real_data)
dis_fake, invert_noise = Discriminator(fake_data)
gen_params = lib.params_with_name('Generator')
dis_params = lib.params_with_name('Discriminator')
inv_params = lib.params_with_name('Invertor')
# Optimize cost function
if MODE == 'wgan-gp':
inv_cost = tf.reduce_mean(tf.square(input_noise - invert_noise))
gen_cost = -tf.reduce_mean(dis_fake)
dis_cost = tf.reduce_mean(dis_fake) - tf.reduce_mean(dis_real)
alpha = tf.random_uniform(shape=[BATCH_SIZE, 1], minval=0., maxval=1.)
differences = fake_data - real_data
interpolates = real_data + alpha * differences
gradients = tf.gradients(Discriminator(interpolates)[0], [interpolates])[0]
slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients), axis=1))
gradient_penalty = tf.reduce_mean((slopes - 1.) ** 2)
dis_cost_gp = dis_cost + LAMBDA * gradient_penalty
inv_train_op = tf.train.AdamOptimizer(learning_rate=1e-4, beta1=0.5,
beta2=0.9).minimize(inv_cost,
var_list=inv_params)
gen_train_op = tf.train.AdamOptimizer(learning_rate=1e-4, beta1=0.5,
def build_mnist_model(num_hidden, decay, activation):
x = tf.placeholder(dtype=tf.float32, shape=[None, args.x_dim])
y = tf.placeholder(dtype=tf.float32, shape=[None, 1])
is_training = tf.placeholder(dtype=tf.bool, shape=[])
with tf.variable_scope('network'):
out, reg, layers = feed_forward(x, num_hidden, decay, activation,
is_training)
rmse_loss = tf.reduce_mean(tf.reduce_sum(tf.square(y - out), 1))
loss = rmse_loss + reg
all_weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='network')
show_variables(all_weights)
last_layer_weights = tf.get_collection(
tf.GraphKeys.GLOBAL_VARIABLES,
scope='network/dense_{}'.format(len(num_hidden) - 1))
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='network')
for item in update_ops:
print('Update {}'.format(item))
lr_decay = tf.placeholder(dtype=tf.float32, shape=[])
all_op = tf.train.GradientDescentOptimizer(args.lr * lr_decay)
all_grads = all_op.compute_gradients(loss=loss, var_list=all_weights)
all_train_op = all_op.apply_gradients(grads_and_vars=all_grads)
lr = args.lr * lr_decay
TEMPERATURE = 1e-8
noise_train_ops = []
for g, v in all_grads:
if g is None:
continue
noise_train_ops.append(
tf.assign(
v, v - lr * g - tf.sqrt(lr) * TEMPERATURE *
tf.random_normal(v.shape, stddev=1)))
all_train_op_noise = tf.group(noise_train_ops)
lst_op = tf.train.GradientDescentOptimizer(args.lr * lr_decay)
lst_grads = lst_op.compute_gradients(loss=loss,
var_list=last_layer_weights)
lst_train_op = lst_op.apply_gradients(grads_and_vars=lst_grads)
reset_lst_op = tf.variables_initializer(lst_op.variables())
reset_all_op = tf.variables_initializer(all_op.variables())
weight_dict = {}
for item in all_weights:
if 'kernel' in item.name:
weight_dict[item.name] = item
print('weights to be saved')
print(weight_dict)
ph = {'x': x, 'y': y, 'lr_decay': lr_decay, 'is_training': is_training}
ph['kernel_l0'] = tf.placeholder(
dtype=tf.float32,
shape=weight_dict['network/dense_0/kernel:0'].get_shape())
#ph['bias_l0'] = tf.placeholder(dtype=tf.float32, shape=weight_dict['network/dense_0/bias:0'].get_shape())
targets = {
'layers': layers,
'all': {
'weights': all_weights,
'train': all_train_op,
'rmse_loss': rmse_loss,
'update': update_ops,
'reg_loss': reg
},
'all_noise': {
'weights': all_weights,
'train': all_train_op_noise,
'rmse_loss': rmse_loss,
'update': update_ops,
'reg_loss': reg
},
'lst': {
'weights': all_weights,
'train': lst_train_op,
'rmse_loss': rmse_loss,
'update': update_ops,
'reg_loss': reg
},
'eval': {
'weights': weight_dict,
'rmse_loss': rmse_loss,
'out': out
},
'assign_weights': {
'weights_l0':
tf.assign(weight_dict['network/dense_0/kernel:0'],
ph['kernel_l0']),
#'bias': tf.assign(weight_dict['network/dense_0/bias:0'], ph['bias_l0']),
},
'reset': {
'lst': reset_lst_op,
'all': reset_all_op
}
}
return ph, targets
h1 = tf.nn.relu(h1)
#[b,256] => [b,128]
#不加broadcast也会自动转换
h2 = h1@w2 + b2
h2 = tf.nn.relu(h2)
# [b,128] => [b,10]
out=h2@w3 + b3
#compute loss
#out: [b,10]
#y: [b] => [b,10]
y_onehot = tf.one_hot(y,depth=10)
#mse = mean(sum(y-out)^2) 均方差
#[b,10]
loss = tf.square(y_onehot - out)
#mean:scalar
loss=tf.reduce_mean(loss)
#compute gradients
grads = tape.gradient(loss,[w1,b1,w2,b2,w3,b3])
#w1 = w1- lr * w1 grad
w1.assign_sub(lr * grads[0])
b1.assign_sub(lr * grads[1])
w2.assign_sub(lr * grads[2])
b2.assign_sub(lr * grads[3])
w3.assign_sub(lr * grads[4])
b3.assign_sub(lr * grads[5])
stddev=1.0 / math.sqrt(embedding_size))
)
nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
loss = tf.reduce_mean(tf.nn.nce_loss(weights=nce_weights,
biases=nce_biases,
labels=train_labels,
inputs=embed,
num_sampled=num_sampled,
num_classes=vocabulary_size
))
optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)
norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
normalized_embeddings = embeddings / norm
valid_embeddings = tf.nn.embedding_lookup(
normalized_embeddings, valid_dataset
)
similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b = True)
init = tf.global_variables_initializer()
num_steps = 100001
with tf.Session(graph=graph) as session:
init.run()
print('Initialized')
average_loss = 0
for step in range(num_steps):
def learn(env,
q_func,
optimizer_spec,
session,
exploration=LinearSchedule(1000000, 0.1),
stopping_criterion=None,
replay_buffer_size=1000000,
batch_size=32,
gamma=0.99,
learning_starts=50000,
learning_freq=4,
frame_history_len=4,
target_update_freq=10000,
grad_norm_clipping=10):
"""Run Deep Q-learning algorithm.
You can specify your own convnet using q_func.
All schedules are w.r.t. total number of steps taken in the environment.
Parameters
----------
env: gym.Env
gym environment to train on.
q_func: function
Model to use for computing the q function. It should accept the
following named arguments:
img_in: tf.Tensor
tensorflow tensor representing the input image
num_actions: int
number of actions
scope: str
scope in which all the model related variables
should be created
reuse: bool
whether previously created variables should be reused.
optimizer_spec: OptimizerSpec
Specifying the constructor and kwargs, as well as learning rate schedule
for the optimizer
session: tf.Session
tensorflow session to use.
exploration: rl_algs.deepq.utils.schedules.Schedule
schedule for probability of chosing random action.
stopping_criterion: (env, t) -> bool
should return true when it's ok for the RL algorithm to stop.
takes in env and the number of steps executed so far.
replay_buffer_size: int
How many memories to store in the replay buffer.
batch_size: int
How many transitions to sample each time experience is replayed.
gamma: float
Discount Factor
learning_starts: int
After how many environment steps to start replaying experiences
learning_freq: int
How many steps of environment to take between every experience replay
frame_history_len: int
How many past frames to include as input to the model.
target_update_freq: int
How many experience replay rounds (not steps!) to perform between
each update to the target Q network
grad_norm_clipping: float or None
If not None gradients' norms are clipped to this value.
"""
assert type(env.observation_space) == gym.spaces.Box
assert type(env.action_space) == gym.spaces.Discrete
###############
# BUILD MODEL #
###############
if len(env.observation_space.shape) == 1:
# This means we are running on low-dimensional observations (e.g. RAM)
input_shape = env.observation_space.shape
else:
img_h, img_w, img_c = env.observation_space.shape
input_shape = (img_h, img_w, frame_history_len * img_c)
num_actions = env.action_space.n
# set up placeholders
# placeholder for current observation (or state)
obs_t_ph = tf.placeholder(tf.uint8, [None] + list(input_shape))
# placeholder for current action
act_t_ph = tf.placeholder(tf.int32, [None])
# placeholder for current reward
rew_t_ph = tf.placeholder(tf.float32, [None])
# placeholder for next observation (or state)
obs_tp1_ph = tf.placeholder(tf.uint8, [None] + list(input_shape))
# placeholder for end of episode mask
# this value is 1 if the next state corresponds to the end of an episode,
# in which case there is no Q-value at the next state; at the end of an
# episode, only the current state reward contributes to the target, not the
# next state Q-value (i.e. target is just rew_t_ph, not rew_t_ph + gamma * q_tp1)
done_mask_ph = tf.placeholder(tf.float32, [None])
# casting to float on GPU ensures lower data transfer times.
obs_t_float = tf.cast(obs_t_ph, tf.float32) / 255.0
obs_tp1_float = tf.cast(obs_tp1_ph, tf.float32) / 255.0
# Here, you should fill in your own code to compute the Bellman error. This requires
# evaluating the current and next Q-values and constructing the corresponding error.
# TensorFlow will differentiate this error for you, you just need to pass it to the
# optimizer. See assignment text for details.
# Your code should produce one scalar-valued tensor: total_error
# This will be passed to the optimizer in the provided code below.
# Your code should also produce two collections of variables:
# q_func_vars
# target_q_func_vars
# These should hold all of the variables of the Q-function network and target network,
# respectively. A convenient way to get these is to make use of TF's "scope" feature.
# For example, you can create your Q-function network with the scope "q_func" like this:
# <something> = q_func(obs_t_float, num_actions, scope="q_func", reuse=False)
# And then you can obtain the variables like this:
# q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='q_func')
# Older versions of TensorFlow may require using "VARIABLES" instead of "GLOBAL_VARIABLES"
######
q_eval_values = q_func(obs_t_float, num_actions, 'q_func', reuse=False)
q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='q_func')
q_target_values = q_func(obs_tp1_float,
num_actions,
'target_q_func',
reuse=False)
target_q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='target_q_func')
eval_acts = tf.one_hot(act_t_ph, num_actions, dtype=tf.float32)
delta_t = rew_t_ph + (gamma * tf.reduce_max(q_target_values, axis=1) -
tf.reduce_sum(eval_acts * q_eval_values, axis=1))
total_error = tf.square(delta_t)
######
# construct optimization op (with gradient clipping)
learning_rate = tf.placeholder(tf.float32, (), name="learning_rate")
optimizer = optimizer_spec.constructor(learning_rate=learning_rate,
**optimizer_spec.kwargs)
train_fn = minimize_and_clip(optimizer,
total_error,
var_list=q_func_vars,
clip_val=grad_norm_clipping)
# update_target_fn will be called periodically to copy Q network to target Q network
update_target_fn = []
for var, var_target in zip(
sorted(q_func_vars, key=lambda v: v.name),
sorted(target_q_func_vars, key=lambda v: v.name)):
update_target_fn.append(var_target.assign(var))
update_target_fn = tf.group(*update_target_fn)
# construct the replay buffer
replay_buffer = ReplayBuffer(replay_buffer_size, frame_history_len)
###############
# RUN ENV #
###############
model_initialized = False
num_param_updates = 0
mean_episode_reward = -float('nan')
best_mean_episode_reward = -float('inf')
last_obs = env.reset()
LOG_EVERY_N_STEPS = 10000
for t in itertools.count():
### 1. Check stopping criterion
if stopping_criterion is not None and stopping_criterion(env, t):
break
### 2. Step the env and store the transition
# At this point, "last_obs" contains the latest observation that was
# recorded from the simulator. Here, your code needs to store this
# observation and its outcome (reward, next observation, etc.) into
# the replay buffer while stepping the simulator forward one step.
# At the end of this block of code, the simulator should have been
# advanced one step, and the replay buffer should contain one more
# transition.
# Specifically, last_obs must point to the new latest observation.
# Useful functions you'll need to call:
# obs, reward, done, info = env.step(action)
# this steps the environment forward one step
# obs = env.reset()
# this resets the environment if you reached an episode boundary.
# Don't forget to call env.reset() to get a new observation if done
# is true!!
# Note that you cannot use "last_obs" directly as input
# into your network, since it needs to be processed to include context
# from previous frames. You should check out the replay buffer
# implementation in dqn_utils.py to see what functionality the replay
# buffer exposes. The replay buffer has a function called
# encode_recent_observation that will take the latest observation
# that you pushed into the buffer and compute the corresponding
# input that should be given to a Q network by appending some
# previous frames.
# Don't forget to include epsilon greedy exploration!
# And remember that the first time you enter this loop, the model
# may not yet have been initialized (but of course, the first step
# might as well be random, since you haven't trained your net...)
#####
frame_id = replay_buffer.store_frame(last_obs)
epsilon = exploration.value(t)
if not model_initialized:
action = env.action_space.sample()
elif random.random() < epsilon:
action = env.action_space.sample()
else:
img_in = replay_buffer.encode_recent_observation()
q = session.run(q_eval_values,
feed_dict={obs_t_ph: img_in[None, :]})
action = np.argmax(q)
next_state, reward, done, _ = env.step(action)
replay_buffer.store_effect(frame_id,
action=action,
done=done,
reward=reward)
if done:
next_state = env.reset()
last_obs = next_state
#####
# at this point, the environment should have been advanced one step (and
# reset if done was true), and last_obs should point to the new latest
# observation
### 3. Perform experience replay and train the network.
# note that this is only done if the replay buffer contains enough samples
# for us to learn something useful -- until then, the model will not be
# initialized and random actions should be taken
if (t > learning_starts and t % learning_freq == 0
and replay_buffer.can_sample(batch_size)):
# Here, you should perform training. Training consists of four steps:
# 3.a: use the replay buffer to sample a batch of transitions (see the
# replay buffer code for function definition, each batch that you sample
# should consist of current observations, current actions, rewards,
# next observations, and done indicator).
# 3.b: initialize the model if it has not been initialized yet; to do
# that, call
# initialize_interdependent_variables(session, tf.global_variables(), {
# obs_t_ph: obs_t_batch,
# obs_tp1_ph: obs_tp1_batch,
# })
# where obs_t_batch and obs_tp1_batch are the batches of observations at
# the current and next time step. The boolean variable model_initialized
# indicates whether or not the model has been initialized.
# Remember that you have to update the target network too (see 3.d)!
# 3.c: train the model. To do this, you'll need to use the train_fn and
# total_error ops that were created earlier: total_error is what you
# created to compute the total Bellman error in a batch, and train_fn
# will actually perform a gradient step and update the network parameters
# to reduce total_error. When calling session.run on these you'll need to
# populate the following placeholders:
# obs_t_ph
# act_t_ph
# rew_t_ph
# obs_tp1_ph
# done_mask_ph
# (this is needed for computing total_error)
# learning_rate -- you can get this from optimizer_spec.lr_schedule.value(t)
# (this is needed by the optimizer to choose the learning rate)
# 3.d: periodically update the target network by calling
# session.run(update_target_fn)
# you should update every target_update_freq steps, and you may find the
# variable num_param_updates useful for this (it was initialized to 0)
#####
batch_state, batch_action, batch_reward, batch_next, batch_done = replay_buffer.sample(
batch_size)
if not model_initialized:
initialize_interdependent_variables(session,
tf.global_variables(), {
obs_t_ph: batch_state,
obs_tp1_ph: batch_next,
})
feeding = {
obs_t_ph: batch_state,
obs_tp1_ph: batch_next,
act_t_ph: batch_action,
rew_t_ph: batch_reward,
done_mask_ph: batch_done,
learning_rate: optimizer_spec.lr_schedule.value(t)
}
session.run(train_fn, feed_dict=feeding)
if t % target_update_freq == 0:
session.run(update_target_fn)
num_param_updates += 1
#####
### 4. Log progress
episode_rewards = get_wrapper_by_name(env,
"Monitor").get_episode_rewards()
if len(episode_rewards) > 0:
mean_episode_reward = np.mean(episode_rewards[-100:])
if len(episode_rewards) > 100:
best_mean_episode_reward = max(best_mean_episode_reward,
mean_episode_reward)
if t % LOG_EVERY_N_STEPS == 0 and model_initialized:
print("Timestep %d" % (t, ))
print("mean reward (100 episodes) %f" % mean_episode_reward)
print("best mean reward %f" % best_mean_episode_reward)
print("episodes %d" % len(episode_rewards))
print("exploration %f" % exploration.value(t))
print("learning_rate %f" % optimizer_spec.lr_schedule.value(t))
sys.stdout.flush()
import tensorflow as tf
import numpy as np
x_data = np.random.rand(100).astype(float)
y_data = x_data*.1 + .3
# Try to find values for W and b that compute y_data = W * x_data + b
# (We know that W should be 0.1 and b 0.3, but TensorFlow will
# figure that out for us.)
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
y = W * x_data + b
# Minimize the mean squared errors.
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
# Before starting, initialize the variables. We will 'run' this first.
init = tf.global_variables_initializer()
# Launch the graph.
sess = tf.Session()
sess.run(init)
# Fit the line.
for step in range(201):
sess.run(train)
if step % 20 == 0:
print(step, sess.run(W), sess.run(b), )
def get_train_model(num_channels, label_len, b, img_size):
inputs = tf.placeholder(
tf.float32,
shape=(b, img_size[0], img_size[1], num_channels))
# 定义ctc_loss需要的稀疏矩阵
targets = tf.sparse_placeholder(tf.int32)
# 1维向量 序列长度 [batch_size,]
seq_len = tf.placeholder(tf.int32, [None])
x = inputs
x = conv(x,num_channels,64,ksize=[3,3])
x = tf.layers.batch_normalization(x)
x = tf.nn.relu(x)
x = tf.nn.max_pool(x,
ksize=[1, 3, 3, 1],
strides=[1, 1, 1, 1],
padding='SAME')
x = small_basic_block(x,64,64)
x2=x
x = tf.layers.batch_normalization(x)
x = tf.nn.relu(x)
x = tf.nn.max_pool(x,
ksize=[1, 3, 3, 1],
strides=[1, 2, 1, 1],
padding='SAME')
x = small_basic_block(x, 64,256)
x = tf.layers.batch_normalization(x)
x = tf.nn.relu(x)
x = small_basic_block(x, 256, 256)
x3 = x
x = tf.layers.batch_normalization(x)
x = tf.nn.relu(x)
x = tf.nn.max_pool(x,
ksize=[1, 3, 3, 1],
strides=[1, 2, 1, 1],
padding='SAME')
x = tf.layers.dropout(x)
x = conv(x, 256, 256, ksize=[4, 1])
x = tf.layers.dropout(x)
x = tf.layers.batch_normalization(x)
x = tf.nn.relu(x)
x = conv(x,256,NUM_CHARS+1,ksize=[1,13],pad='SAME')
x = tf.nn.relu(x)
cx = tf.reduce_mean(tf.square(x))
x = tf.div(x,cx)
#x = tf.reduce_mean(x,axis = 2)
#x1 = conv(inputs,num_channels,num_channels,ksize = (5,1))
x1 = tf.nn.avg_pool(inputs,
ksize=[1, 4, 1, 1],
strides=[1, 4, 1, 1],
padding='SAME')
cx1 = tf.reduce_mean(tf.square(x1))
x1 = tf.div(x1, cx1)
# x1 = tf.image.resize_images(x1, size = [18, 16], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
x2 = tf.nn.avg_pool(x2,
ksize=[1, 4, 1, 1],
strides=[1, 4, 1, 1],
padding='SAME')
cx2 = tf.reduce_mean(tf.square(x2))
x2 = tf.div(x2, cx2)
#x2 = tf.image.resize_images(x2, size=[18, 16], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
x3 = tf.nn.avg_pool(x3,
ksize=[1, 2, 1, 1],
strides=[1, 2, 1, 1],
padding='SAME')
cx3 = tf.reduce_mean(tf.square(x3))
x3 = tf.div(x3, cx3)
#x3 = tf.image.resize_images(x3, size=[18, 16], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
#x1 = tf.nn.relu(x1)
x = tf.concat([x,x1,x2,x3],3)
x = conv(x, x.get_shape().as_list()[3], NUM_CHARS + 1, ksize=(1, 1))
logits = tf.reduce_mean(x,axis=2)
# x_shape = x.get_shape().as_list()
# outputs = tf.reshape(x, [-1,x_shape[2]*x_shape[3]])
# W1 = tf.Variable(tf.truncated_normal([x_shape[2]*x_shape[3],
# 150],
# stddev=0.1))
# b1 = tf.Variable(tf.constant(0., shape=[150]))
# # [batch_size*max_timesteps,num_classes]
# x = tf.matmul(outputs, W1) + b1
# x= tf.layers.dropout(x)
# x = tf.nn.relu(x)
# W2 = tf.Variable(tf.truncated_normal([150,
# NUM_CHARS+1],
# stddev=0.1))
# b2 = tf.Variable(tf.constant(0., shape=[NUM_CHARS+1]))
# x = tf.matmul(x, W2) + b2
# x = tf.layers.dropout(x)
# # [batch_size,max_timesteps,num_classes]
# logits = tf.reshape(x, [b, -1, NUM_CHARS+1])
return logits, inputs, targets, seq_len
def __init__(self, x0, u0, x1, layers, dt, lb, ub, q):
self.lb = lb
self.ub = ub
self.x0 = x0
self.x1 = x1
self.u0 = u0
self.layers = layers
self.dt = dt
self.q = max(q, 1)
# Initialize NN
self.weights, self.biases = self.initialize_NN(layers)
# Load IRK weights
tmp = np.float32(
np.loadtxt("../../Utilities/IRK_weights/Butcher_IRK%d.txt" % (q),
ndmin=2))
self.IRK_weights = np.reshape(tmp[0:q**2 + q], (q + 1, q))
self.IRK_times = tmp[q**2 + q:]
# tf placeholders and graph
self.sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True, log_device_placement=True))
self.x0_tf = tf.placeholder(tf.float32, shape=(None, self.x0.shape[1]))
self.x1_tf = tf.placeholder(tf.float32, shape=(None, self.x1.shape[1]))
self.u0_tf = tf.placeholder(tf.float32, shape=(None, self.u0.shape[1]))
self.dummy_x0_tf = tf.placeholder(
tf.float32,
shape=(None, self.q)) # dummy variable for fwd_gradients
self.dummy_x1_tf = tf.placeholder(
tf.float32,
shape=(None, self.q + 1)) # dummy variable for fwd_gradients
self.U0_pred = self.net_U0(self.x0_tf) # N x (q+1)
self.U1_pred, self.U1_x_pred = self.net_U1(self.x1_tf) # N1 x (q+1)
self.loss = (
tf.reduce_sum(tf.square(self.u0_tf - self.U0_pred)) +
tf.reduce_sum(tf.square(self.U1_pred[0, :] - self.U1_pred[1, :])) +
tf.reduce_sum(
tf.square(self.U1_x_pred[0, :] - self.U1_x_pred[1, :])))
self.optimizer = tf.contrib.opt.ScipyOptimizerInterface(
self.loss,
method="L-BFGS-B",
options={
"maxiter": 50000,
"maxfun": 50000,
"maxcor": 50,
"maxls": 50,
"ftol": 1.0 * np.finfo(float).eps,
},
)
self.optimizer_Adam = tf.train.AdamOptimizer()
self.train_op_Adam = self.optimizer_Adam.minimize(self.loss)
init = tf.global_variables_initializer()
self.sess.run(init)
def distanceFunc(X, mu): # Returns distance squared
expandPoints = tf.expand_dims(X, 0)
expandCentroid = tf.expand_dims(mu, 1)
return tf.reduce_sum(tf.square(tf.subtract(expandPoints, expandCentroid)),
2)