import tensorflow as tf
layers = tf.keras.layers
from tensorflow.contrib import autograph
import numpy as np
tf.enable_eager_execution()
def square_if_positive(x):
if x > 0:
x = x * x
else:
x = 0.0
return x
print(autograph.to_code(square_if_positive))
tf_square_if_positive = autograph.to_graph(square_if_positive)
with tf.Graph().as_default():
# The result works like a regular op: takes tensors in, returns tensors.
# You can inspect the graph using tf.get_default_graph().as_graph_def()
g_out1 = tf_square_if_positive(tf.constant(9.0))
g_out2 = tf_square_if_positive(tf.constant(-9.0))
with tf.Session() as sess:
print('Graph results: %2.2f, %2.2f\n' %
(sess.run(g_out1), sess.run(g_out2)))
from __future__ import division, print_function, absolute_import import tensorflow as tf import xgb_model_zzr2 import testvars2 import tensorflow as tf layers = tf.keras.layers from tensorflow.contrib import autograph print(autograph.to_code(testvars2.XGBprocess))
((ANY_UPPER, ), )))
# TODO uncomment, dependency: bug in TF (31.1.19) # 25339 that breaks gradient of resource var in cond
# CrystalBall.register_analytic_integral(func=crystalball_integral, limits=crystalball_integral_limits)
if __name__ == '__main__':
mu = ztf.constant(0)
sigma = ztf.constant(0.5)
alpha = ztf.constant(3)
n = ztf.constant(1)
# res = crystalball_func(np.random.random(size=100), mu, sigma, alpha, n)
# int1 = crystalball_integral(limits=zfit.Space(obs='obs1', limits=(-3, 5)),
# params={'mu': mu, "sigma": sigma, "alpha": alpha, "n": n})
from tensorflow.contrib import autograph
import matplotlib.pyplot as plt
new_code = autograph.to_code(crystalball_integral)
obs = zfit.Space(obs='obs1', limits=(-3, 1))
cb1 = CrystalBall(mu, sigma, alpha, n, obs=obs)
res = cb1.pdf(np.random.random(size=100))
int1 = cb1.integrate(limits=(-0.01, 2), norm_range=obs)
# tf.add_check_numerics_ops()
x = np.linspace(-5, 1, num=1000)
vals = cb1.pdf(x=x)
y = zfit.run(vals)[0]
plt.plot(x, y)
plt.show()
# print(new_code)
print(zfit.run(res))
print(zfit.run(int1))
pre_op_idx = -1
i = 0
def _path2dag(path, opt_ind, end_ind, cd_length, num_cells, dag, pre_op_idx,
i):
dag = []
autograph.set_element_type(dag, tf.int32)
for x in range(5):
dag.append(x)
# logger.info(path)
# logger.info(np.reshape(dag, (self.num_cells, self.cd_length)))
return dag
print(autograph.to_code(_path2dag))
'''
for op in path:
start_idx = i*cd_length
if op == 0:
dag[start_idx] = 2
else:
dag[start_idx+opt_ind] = op
dag[start_idx+opt_ind-num_cells+pre_op_idx+1] = 1
if pre_op_idx != -1:
dag[(pre_op_idx+1)*cd_length-1] = 0
dag[start_idx+end_ind] = 1
pre_op_idx = i
i += 1
'''
##################################fail################################################# import tensorflow as tf import xgb_model_zzr from __future__ import division, print_function, absolute_import import tensorflow as tf layers = tf.keras.layers from tensorflow.contrib import autograph print(autograph.to_code(xgb_tree))
import numpy as np
import matplotlib.pyplot as plt
tf.enable_eager_execution()
def square_if_positive(x):
if x > 0:
x = x * x
else:
x = 0.0
return x
print(autograph.to_code(square_if_positive))
print('Eager results: %2.2f, %2.2f' % (square_if_positive(
tf.constant(9.0)), square_if_positive(tf.constant(-9.0))))
tf_square_if_positive = autograph.to_graph(square_if_positive)
with tf.Graph().as_default():
# The result works like a regular op: takes tensors in, returns tensors.
# You can inspect the graph using tf.get_default_graph().as_graph_def()
g_out1 = tf_square_if_positive(tf.constant(9.0))
g_out2 = tf_square_if_positive(tf.constant(-9.0))
with tf.Session() as sess:
print('Graph results: %2.2f, %2.2f\n' %
(sess.run(g_out1), sess.run(g_out2)))