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Fix TF deprecation warnings #11

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Fix TF deprecation warnings #11

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12 changes: 6 additions & 6 deletions batch_norm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,15 +32,15 @@ def _set_default_initializer(self, var_name):

def _build_statistics_variance(self, input_batch,
reduction_indices, use_batch_stats):
self._moving_mean = tf.get_variable(
self._moving_mean = tf.compat.v1.get_variable(
"moving_mean",
shape=self._mean_shape,
collections=[tf.GraphKeys.MOVING_AVERAGE_VARIABLES,
tf.GraphKeys.VARIABLES],
initializer=tf.zeros_initializer,
trainable=False)

self._moving_variance = tf.get_variable(
self._moving_variance = tf.compat.v1.get_variable(
"moving_variance",
shape=self._mean_shape,
collections=[tf.GraphKeys.MOVING_AVERAGE_VARIABLES,
Expand Down Expand Up @@ -81,15 +81,15 @@ def build_moving_stats():

def _build_statistics_second_moment(self, input_batch,
reduction_indices, use_batch_stats):
self._moving_mean = tf.get_variable(
self._moving_mean = tf.compat.v1.get_variable(
"moving_mean",
shape=self._mean_shape,
collections=[tf.GraphKeys.MOVING_AVERAGE_VARIABLES,
tf.GraphKeys.VARIABLES],
initializer=tf.zeros_initializer,
trainable=False)

self._moving_second_moment = tf.get_variable(
self._moving_second_moment = tf.compat.v1.get_variable(
"moving_second_moment",
shape=self._mean_shape,
collections=[tf.GraphKeys.MOVING_AVERAGE_VARIABLES,
Expand Down Expand Up @@ -252,7 +252,7 @@ def _build(self, input_batch, is_training=True, test_local_stats=True):
# Set up optional scale and offset factors.
if self._offset:
self._set_default_initializer(self.BETA)
self._beta = tf.get_variable(
self._beta = tf.compat.v1.get_variable(
self.BETA,
shape=self._mean_shape,
initializer=self._initializers[self.BETA])
Expand All @@ -261,7 +261,7 @@ def _build(self, input_batch, is_training=True, test_local_stats=True):

if self._scale:
self._set_default_initializer(self.GAMMA)
self._gamma = tf.get_variable(
self._gamma = tf.compat.v1.get_variable(
self.GAMMA,
shape=self._mean_shape,
initializer=self._initializers[self.GAMMA])
Expand Down
2 changes: 1 addition & 1 deletion eval.py
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Expand Up @@ -23,7 +23,7 @@ def main():
config = json.load(config_file,
object_hook=lambda d:namedtuple('x', d.keys())(*d.values()))
num_unrolls = config.num_steps // config.unroll_length
with tf.Session() as sess:
with tf.compat.v1.Session() as sess:
model = util.load_model(sess, config, logger)
all_y = []
for i in range(10):
Expand Down
4 changes: 2 additions & 2 deletions gmm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,10 +69,10 @@ def test_tf():
xr = list(np.arange(0, 1, 0.02))
X = np.array(list(product(xr, repeat=2)))
Y = []
with tf.Session() as sess:
with tf.compat.v1.Session() as sess:
gmm = tf_GMM(batch_size=1, ncoef=6, num_dims=2, cov=0.5)
y = gmm(tf.placeholder(tf.float32, shape=[1, 2], name='x'))
sess.run(tf.global_variables_initializer())
sess.run(tf.compat.v1.global_variables_initializer())
for x in X:
Y.append(sess.run(y, feed_dict={'x:0':x.reshape((1, 2))}))

Expand Down
6 changes: 3 additions & 3 deletions infer_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,7 @@ def __init__(self, cell, func, ndim, nsteps, ckpt_path, logger, constraints):
self.init_state = self.cell.get_initial_state(1, tf.float32)
self.results = self.build_graph()

self.saver = tf.train.Saver(tf.global_variables())
self.saver = tf.compat.v1.train.Saver(tf.compat.v1.global_variables())

def get_state_shapes(self):
return [(s[0].get_shape().as_list(), s[1].get_shape().as_list())
Expand Down Expand Up @@ -78,7 +78,7 @@ def get_init(self):
return x, y, init_state

def run(self):
with tf.Session() as sess:
with tf.compat.v1.Session() as sess:
self.load(sess, self.ckpt_path)
x, y, state = self.get_init()
x_array = np.zeros((self.nsteps + 1, self.ndim))
Expand Down Expand Up @@ -147,6 +147,6 @@ def main():
ax2.plot(x_array[:, 0], x_array[:, 1], x_array[:, 2])
fig2.show()
plt.show()

if __name__ == '__main__':
main()
2 changes: 1 addition & 1 deletion lets_start.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@ def main():
logger.info(str(json.load(config_file)))
config_file.close()
num_unrolls = config.num_steps // config.unroll_length
with tf.Session() as sess:
with tf.compat.v1.Session() as sess:
# tf.get_default_graph().finalize()
model = util.create_model(sess, config, logger)
step, loss, reset, fx_array, x_array = model.step()
Expand Down
14 changes: 7 additions & 7 deletions model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,29 +17,29 @@ def __init__(self, cell, logger, func, ndim, batch_size, unroll_len,
self.make_loss(func, ndim, batch_size, unroll_len)
loss_func = self.get_loss_func(loss_type, direction)
self.loss = loss_func(self.fx_array)
optimizer = getattr(tf.train, optimizer + 'Optimizer')(lr)
optimizer = getattr(tf.compat.v1.train, optimizer + 'Optimizer')(lr)
gvs = optimizer.compute_gradients(self.loss)
capped_gvs = [(tf.clip_by_value(grad, -0.1, 0.1), var) for grad, var in gvs]
self.opt = optimizer.apply_gradients(capped_gvs)

# self.opt = optimizer.minimize(self.loss)
self.saver = tf.train.Saver(tf.global_variables(), max_to_keep=3)
self.saver = tf.compat.v1.train.Saver(tf.compat.v1.global_variables(), max_to_keep=3)
logger.info('model variable:')
logger.info(str([var.name for var in tf.global_variables()]))
logger.info(str([var.name for var in tf.compat.v1.global_variables()]))
logger.info('trainable variables:')
logger.info(str([var.name for var in tf.trainable_variables()]))
logger.info(str([var.name for var in tf.compat.v1.trainable_variables()]))
self.fx_array = self.fx_array.stack()
self.x_array = self.x_array.stack()


def make_discount(self, gamma, unroll_len):
df = [(gamma ** (unroll_len - i)) for i in range(unroll_len + 1)]
return tf.constant(df, shape=[unroll_len + 1, 1], dtype=tf.float32)


def make_loss(self, func, ndim, batch_size, unroll_len):
self.unroll_len = unroll_len
x = tf.get_variable('x', shape=[batch_size, ndim],
x = tf.compat.v1.get_variable('x', shape=[batch_size, ndim],
initializer=tf.truncated_normal_initializer(mean=0.5, stddev=0.2),
trainable=self.trainable_init)
constants = func.get_parameters()
Expand Down Expand Up @@ -81,7 +81,7 @@ def step(t, x, state, fx_array, x_array):

variables = [x,] + constants
# Empty array as part of the reset process.
self.reset = [tf.variables_initializer(variables),
self.reset = [tf.compat.v1.variables_initializer(variables),
self.fx_array.close(), self.x_array.close()]

return self.fx_array, self.x_array
Expand Down
2 changes: 1 addition & 1 deletion ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@

def wrap_variable_creation(func, custom_getter):
"""Provides a custom getter for all variable creations."""
original_get_variable = tf.get_variable
original_get_variable = tf.compat.v1.get_variable
def custom_get_variable(*args, **kwargs):
if hasattr(kwargs, 'custom_getter'):
raise AttributeError('Custom getters are not supported for '
Expand Down
31 changes: 16 additions & 15 deletions reactions.py
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Original file line number Diff line number Diff line change
@@ -1,18 +1,19 @@
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='3'
import tensorflow as tf
import tensorflow_probability as tfp
import numpy as np

class ConstraintQuadratic:
"""Quadratic problem: f(x) = ||Wx - y||."""
def __init__(self, batch_size=128, num_dims=3, ptype='convex',
random=0.05, dtype=tf.float32):
self.ptype = ptype
self.w = tf.get_variable('w', shape=[batch_size, num_dims, num_dims],
self.w = tf.compat.v1.get_variable('w', shape=[batch_size, num_dims, num_dims],
dtype=dtype, initializer=tf.random_normal_initializer(),
trainable=False)

self.a = tf.get_variable('y', shape=[batch_size, num_dims],
self.a = tf.compat.v1.get_variable('y', shape=[batch_size, num_dims],
dtype=dtype, initializer=tf.random_uniform_initializer(minval=0.01, maxval=0.99),
trainable=False)

Expand Down Expand Up @@ -41,7 +42,7 @@ def _barrier(self, var):

def __call__(self, x):
'''
x = tf.get_variable('x', shape=[batch_size, num_dims],
x = tf.compat.v1.get_variable('x', shape=[batch_size, num_dims],
dtype=dtype, initializer=tf.random_normal_initializer(stddev=stdev))
'''
res = (self._func(x) / self.normalizer + self.e + self._barrier(x))
Expand All @@ -56,21 +57,21 @@ def __init__(self, batch_size=128, ncoef=6, num_dims=3, random=None,
self.num_dim = num_dims
self.batch_size = batch_size
self.dtype = dtype
with tf.variable_scope('func_gmm'):
self.m = [tf.get_variable('mu_{}'.format(i), shape=[batch_size, num_dims],
with tf.compat.v1.variable_scope('func_gmm'):
self.m = [tf.compat.v1.get_variable('mu_{}'.format(i), shape=[batch_size, num_dims],
dtype=dtype,
initializer=tf.random_uniform_initializer(minval=0.01, maxval=0.99),
trainable=False)
for i in range(ncoef)]

self.cov = [tf.get_variable('cov_{}'.format(i), shape=[batch_size, num_dims],
self.cov = [tf.compat.v1.get_variable('cov_{}'.format(i), shape=[batch_size, num_dims],
dtype=dtype,
initializer=tf.truncated_normal_initializer(
mean=cov, stddev=cov/5),
trainable=False)
for i in range(ncoef)]

self.coef = tf.get_variable('coef', shape=[ncoef, 1], dtype=dtype,
self.coef = tf.compat.v1.get_variable('coef', shape=[ncoef, 1], dtype=dtype,
initializer=tf.random_normal_initializer(stddev=0.2),
trainable=False)

Expand All @@ -91,7 +92,7 @@ def get_parameters(self):
return self.m + self.cov + [self.coef]

def __call__(self, x):
dist = [tf.contrib.distributions.MultivariateNormalDiag(
dist = [tfp.distributions.MultivariateNormalDiag(
self.m[i], self.cov[i], name='MultVarNorm_{}'.format(i))
for i in range(self.ncoef)]
p = tf.concat([tf.reshape(dist[i].prob(x), [-1, 1])
Expand All @@ -101,7 +102,7 @@ def __call__(self, x):
result = (fx / self.cst - self.bots) / (self.tops - self.bots)
# import pdb; pdb.set_trace()
if self.random:
result = result + tf.random_normal(shape=[self.batch_size, 1],
result = result + tf.random_normal(shape=[self.batch_size, 1],
stddev=self.random,
dtype=self.dtype, name='error')
return result
Expand All @@ -113,11 +114,11 @@ class Quadratic:
def __init__(self, batch_size=128, num_dims=3, ptype='convex',
random=0.05, dtype=tf.float32):
self.ptype = ptype
self.w = tf.get_variable('w', shape=[batch_size, num_dims, num_dims],
self.w = tf.compat.v1.get_variable('w', shape=[batch_size, num_dims, num_dims],
dtype=dtype, initializer=tf.random_normal_initializer(),
trainable=False)

self.a = tf.get_variable('y', shape=[batch_size, num_dims],
self.a = tf.compat.v1.get_variable('y', shape=[batch_size, num_dims],
dtype=dtype, initializer=tf.truncated_normal_initializer(mean=0.5, stddev=0.2),
trainable=False)

Expand All @@ -144,7 +145,7 @@ def _func(self, var):

def __call__(self, x):
'''
x = tf.get_variable('x', shape=[batch_size, num_dims],
x = tf.compat.v1.get_variable('x', shape=[batch_size, num_dims],
dtype=dtype, initializer=tf.random_normal_initializer(stddev=stdev))
'''
res = (self._func(x) / self.normalizer + self.e)
Expand Down Expand Up @@ -195,7 +196,7 @@ def __call__(self, x):
if self.record:
self.history['x'].append(x)
self.history['y'].append(res)
return res
return res

class ConstraintQuadraticEval:
def __init__(self, num_dim=3, random=0.5, ptype='convex',
Expand Down Expand Up @@ -233,7 +234,7 @@ def __call__(self, x):
res = 1 - res
print('Output:')
print(res)
return res
return res


class RealReaction:
Expand Down Expand Up @@ -264,4 +265,4 @@ def __call__(self, x):
result = float(input('Input the reaction yield:'))
return self.y_convert(result)


8 changes: 4 additions & 4 deletions realreaction.py
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Expand Up @@ -25,7 +25,7 @@ def __init__(self, cell, func, ndim, nsteps, ckpt_path, logger, constraints):
self.init_state = self.cell.get_initial_state(1, tf.float32)
self.results = self.build_graph()

self.saver = tf.train.Saver(tf.global_variables())
self.saver = tf.compat.v1.train.Saver(tf.compat.v1.global_variables())

def get_state_shapes(self):
return [(s[0].get_shape().as_list(), s[1].get_shape().as_list())
Expand Down Expand Up @@ -75,7 +75,7 @@ def get_init(self):
return x, y, init_state

def run(self):
with tf.Session() as sess:
with tf.compat.v1.Session() as sess:
self.load(sess, self.ckpt_path)
x, y, state = self.get_init()
x_array = np.zeros((self.nsteps + 1, self.ndim))
Expand Down Expand Up @@ -110,10 +110,10 @@ def main():
constraints=config.constraints)
x_array, y_array = optimizer.run()


# plt.figure(1)
# plt.plot(y_array)
# plt.show()

if __name__ == '__main__':
main()
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