diff options
| author | Renato Lui Geh | 2020-02-06 15:05:27 -0300 |
|---|---|---|
| committer | Renato Lui Geh | 2020-02-06 15:05:27 -0300 |
| commit | 6783a962b3fa55d823f2e0a3d1ffe74051da77ef (patch) | |
| tree | 8480ac282c6fd075b8a55b4bdcc40cb92778c270 | |
| parent | f44032b476166b3847429c5286d1a8fba941f3c4 (diff) | |
| download | aur-6783a962b3fa55d823f2e0a3d1ffe74051da77ef.tar.gz | |
Update to 0.0.40
Signed-off-by: Renato Lui Geh <renatogeh@gmail.com>
| -rw-r--r-- | .SRCINFO | 8 | ||||
| -rw-r--r-- | PKGBUILD | 8 | ||||
| -rw-r--r-- | tf2.patch | 165 |
3 files changed, 79 insertions, 102 deletions
@@ -1,7 +1,7 @@ pkgbase = python-spflow pkgdesc = Sum-Product Flow: An Easy and Extensible Library for Sum-Product Networks - pkgver = 0.0.39 - pkgrel = 5 + pkgver = 0.0.40 + pkgrel = 1 url = https://github.com/SPFlow/SPFlow arch = any license = Apache 2.0 @@ -25,8 +25,8 @@ pkgbase = python-spflow depends = python-tensorflow-cuda depends = python-pytorch-cuda depends = cppyy - source = https://files.pythonhosted.org/packages/76/49/6b37eaa9bf8c758b99c3f297b39d81bdc5b07329ac674659d67f56493ead/spflow-0.0.39.tar.gz - sha256sums = a32a237c4ca01742a5dc4cf5ef895423d8275df8d9fac43f3e44e19ee1931a6e + source = https://files.pythonhosted.org/packages/18/95/f22f40e7e53c3d7b2bb0c698aa723b701b844efcb7cad7d6667bd5bfadd1/spflow-0.0.40.tar.gz + sha256sums = 90183d810894dc1dcdc0ec63b7757b502d5b316311c4b91c07e892288a1f9d2e pkgname = python-spflow @@ -2,8 +2,8 @@ pkgname=python-spflow _pypiname=${pkgname/python-/} -pkgver=0.0.39 -pkgrel=5 +pkgver=0.0.40 +pkgrel=1 pkgdesc="Sum-Product Flow: An Easy and Extensible Library for Sum-Product Networks" arch=('any') url="https://github.com/SPFlow/SPFlow" @@ -13,8 +13,8 @@ depends=('python' 'python-numpy' 'python-scipy' 'python-statsmodels' 'python-net 'python-sympy' 'python-pyqt5' 'python-pytest' 'python-ete' 'python-arff' 'python-torchvision' 'python-tensorflow-cuda' 'python-pytorch-cuda' 'cppyy') makedepends=('python-setuptools') -source=("https://files.pythonhosted.org/packages/76/49/6b37eaa9bf8c758b99c3f297b39d81bdc5b07329ac674659d67f56493ead/${_pypiname}-${pkgver}.tar.gz") -sha256sums=('a32a237c4ca01742a5dc4cf5ef895423d8275df8d9fac43f3e44e19ee1931a6e') +source=("https://files.pythonhosted.org/packages/18/95/f22f40e7e53c3d7b2bb0c698aa723b701b844efcb7cad7d6667bd5bfadd1/${_pypiname}-${pkgver}.tar.gz") +sha256sums=('90183d810894dc1dcdc0ec63b7757b502d5b316311c4b91c07e892288a1f9d2e') build() { cd $srcdir diff --git a/tf2.patch b/tf2.patch index 0625eda0b1b3..5564cf4a2580 100644 --- a/tf2.patch +++ b/tf2.patch @@ -1,6 +1,6 @@ -diff -x '*.pyc' -x __pycache__ -Naur spn/algorithms/sklearn.py /usr/lib/python3.8/site-packages/spn/algorithms/sklearn.py ---- spn/algorithms/sklearn.py 2019-03-06 08:26:14.000000000 -0300 -+++ /usr/lib/python3.8/site-packages/spn/algorithms/sklearn.py 2020-02-04 15:22:34.000000000 -0300 +diff '--color=auto' -x '*.pyc' -x __pycache__ -Naur /usr/lib/python3.8/site-packages/spn/algorithms/sklearn.py spn/algorithms/sklearn.py +--- /usr/lib/python3.8/site-packages/spn/algorithms/sklearn.py 2020-02-06 14:44:53.000000000 -0300 ++++ spn/algorithms/sklearn.py 2020-02-06 14:46:43.587996915 -0300 @@ -30,7 +30,7 @@ tf_optimize_weights=False, tf_n_epochs=100, @@ -28,9 +28,9 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/algorithms/sklearn.py /usr/lib/python3. - return tf.distributions.Categorical(probs=probs).prob(data_placeholder[:, node.scope[0]]) + return tf.compat.v1.distributions.Categorical(probs=probs).prob(data_placeholder[:, node.scope[0]]) -diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/AQP/Ranges.py /usr/lib/python3.8/site-packages/spn/experiments/AQP/Ranges.py ---- spn/experiments/AQP/Ranges.py 2018-12-21 16:34:25.000000000 -0200 -+++ /usr/lib/python3.8/site-packages/spn/experiments/AQP/Ranges.py 2020-02-04 15:22:34.000000000 -0300 +diff '--color=auto' -x '*.pyc' -x __pycache__ -Naur /usr/lib/python3.8/site-packages/spn/experiments/AQP/Ranges.py spn/experiments/AQP/Ranges.py +--- /usr/lib/python3.8/site-packages/spn/experiments/AQP/Ranges.py 2020-02-06 14:44:53.000000000 -0300 ++++ spn/experiments/AQP/Ranges.py 2020-02-06 14:46:37.724570894 -0300 @@ -1,62 +1,62 @@ -"""
-Created on May 22, 2018
@@ -156,9 +156,9 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/AQP/Ranges.py /usr/lib/pyth + + def get_ranges(self): + return self.ranges -diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/AQP/tests/cumulative_distribution.py /usr/lib/python3.8/site-packages/spn/experiments/AQP/tests/cumulative_distribution.py ---- spn/experiments/AQP/tests/cumulative_distribution.py 2018-12-21 16:34:25.000000000 -0200 -+++ /usr/lib/python3.8/site-packages/spn/experiments/AQP/tests/cumulative_distribution.py 2020-02-04 15:22:34.000000000 -0300 +diff '--color=auto' -x '*.pyc' -x __pycache__ -Naur /usr/lib/python3.8/site-packages/spn/experiments/AQP/tests/cumulative_distribution.py spn/experiments/AQP/tests/cumulative_distribution.py +--- /usr/lib/python3.8/site-packages/spn/experiments/AQP/tests/cumulative_distribution.py 2020-02-06 14:44:53.000000000 -0300 ++++ spn/experiments/AQP/tests/cumulative_distribution.py 2020-02-06 14:46:38.634585290 -0300 @@ -1,294 +1,294 @@ -"""
-Created on Jun 21, 2018
@@ -748,9 +748,9 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/AQP/tests/cumulative_distri + plt.title("Inverse cumulative distribution") + plt.plot(x_domain, y_domain) + plt.show() -diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/FPGA/RunNative.py /usr/lib/python3.8/site-packages/spn/experiments/FPGA/RunNative.py ---- spn/experiments/FPGA/RunNative.py 2018-12-21 16:34:25.000000000 -0200 -+++ /usr/lib/python3.8/site-packages/spn/experiments/FPGA/RunNative.py 2020-02-04 15:22:34.000000000 -0300 +diff '--color=auto' -x '*.pyc' -x __pycache__ -Naur /usr/lib/python3.8/site-packages/spn/experiments/FPGA/RunNative.py spn/experiments/FPGA/RunNative.py +--- /usr/lib/python3.8/site-packages/spn/experiments/FPGA/RunNative.py 2020-02-06 14:44:53.000000000 -0300 ++++ spn/experiments/FPGA/RunNative.py 2020-02-06 14:46:37.447899849 -0300 @@ -23,12 +23,12 @@ @@ -794,9 +794,9 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/FPGA/RunNative.py /usr/lib/ # start = time.perf_counter() tf_ll = sess.run( tf_graph, -diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/layers/spflow_vs_pytorch.py /usr/lib/python3.8/site-packages/spn/experiments/layers/spflow_vs_pytorch.py ---- spn/experiments/layers/spflow_vs_pytorch.py 2019-10-24 08:51:04.000000000 -0300 -+++ /usr/lib/python3.8/site-packages/spn/experiments/layers/spflow_vs_pytorch.py 2020-02-04 15:22:34.000000000 -0300 +diff '--color=auto' -x '*.pyc' -x __pycache__ -Naur /usr/lib/python3.8/site-packages/spn/experiments/layers/spflow_vs_pytorch.py spn/experiments/layers/spflow_vs_pytorch.py +--- /usr/lib/python3.8/site-packages/spn/experiments/layers/spflow_vs_pytorch.py 2020-02-06 14:44:53.000000000 -0300 ++++ spn/experiments/layers/spflow_vs_pytorch.py 2020-02-06 14:46:35.431201264 -0300 @@ -161,8 +161,8 @@ x = np.random.rand(batch_size, n_feats).astype(np.float32) tf_graph, placeholder, _ = spn_to_tf_graph(spflow_spn, x, dtype=np.float32) @@ -808,9 +808,9 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/layers/spflow_vs_pytorch.py # warmup: for i in range(10): result = sess.run(tf_graph, feed_dict={placeholder: x}) -diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/LearnRGSPN.py /usr/lib/python3.8/site-packages/spn/experiments/RandomSPNs/LearnRGSPN.py ---- spn/experiments/RandomSPNs/LearnRGSPN.py 2019-09-12 05:46:48.000000000 -0300 -+++ /usr/lib/python3.8/site-packages/spn/experiments/RandomSPNs/LearnRGSPN.py 2020-02-04 15:22:34.000000000 -0300 +diff '--color=auto' -x '*.pyc' -x __pycache__ -Naur /usr/lib/python3.8/site-packages/spn/experiments/RandomSPNs/LearnRGSPN.py spn/experiments/RandomSPNs/LearnRGSPN.py +--- /usr/lib/python3.8/site-packages/spn/experiments/RandomSPNs/LearnRGSPN.py 2020-02-06 14:44:53.000000000 -0300 ++++ spn/experiments/RandomSPNs/LearnRGSPN.py 2020-02-06 14:46:31.461138372 -0300 @@ -126,7 +126,7 @@ spns = vector_list[-1][0] @@ -831,26 +831,19 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/LearnRGSPN.py /u print("starting") tfstart = time.perf_counter() sess.run(output, feed_dict={input_ph: train_im[0:1000]}) -diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/RAT_SPN.py /usr/lib/python3.8/site-packages/spn/experiments/RandomSPNs/RAT_SPN.py ---- spn/experiments/RandomSPNs/RAT_SPN.py 2019-09-12 05:46:48.000000000 -0300 -+++ /usr/lib/python3.8/site-packages/spn/experiments/RandomSPNs/RAT_SPN.py 2020-02-04 15:54:17.607091839 -0300 -@@ -3,9 +3,14 @@ - import spn.structure.Base as base - import spn.structure.leaves.parametric.Parametric as para - -+from spn.gpu.TensorFlow import tf_major_version +diff '--color=auto' -x '*.pyc' -x __pycache__ -Naur /usr/lib/python3.8/site-packages/spn/experiments/RandomSPNs/RAT_SPN.py spn/experiments/RandomSPNs/RAT_SPN.py +--- /usr/lib/python3.8/site-packages/spn/experiments/RandomSPNs/RAT_SPN.py 2020-02-06 14:44:53.000000000 -0300 ++++ spn/experiments/RandomSPNs/RAT_SPN.py 2020-02-06 14:57:05.721507515 -0300 +@@ -5,7 +5,7 @@ + from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops -import tensorflow.contrib.distributions as dists -+ -+if tf_major_version == 1: -+ import tensorflow.contrib.distributions as dists -+else: -+ from tensorflow_probability import distributions as dists ++from tensorflow_probability import distributions as dists import time -@@ -17,37 +22,37 @@ +@@ -17,37 +17,37 @@ def variable_with_weight_decay(name, shape, stddev, wd, mean=0.0, values=None): if values is None: @@ -900,7 +893,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/RAT_SPN.py /usr/ class NodeVector(object): -@@ -135,7 +140,7 @@ +@@ -135,7 +135,7 @@ # gauss_log_pdf_single = tf.log(weighted_gauss_pdf + local_marginalized_broadcast) gauss_log_pdf_single = gauss_log_pdf_single * (1 - local_marginalized) @@ -909,7 +902,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/RAT_SPN.py /usr/ return gauss_log_pdf def sample(self, num_samples, num_dims, seed=None): -@@ -144,7 +149,7 @@ +@@ -144,7 +144,7 @@ sample_shape = [num_samples, num_dims, self.size] indices = tf.meshgrid(tf.range(num_samples), self.scope, tf.range(self.size)) indices = tf.stack(indices, axis=-1) @@ -918,7 +911,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/RAT_SPN.py /usr/ samples = tf.scatter_nd(indices, sample_values, sample_shape) return samples -@@ -187,7 +192,7 @@ +@@ -187,7 +187,7 @@ if classes: return bernoulli_log_pdf_single else: @@ -927,7 +920,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/RAT_SPN.py /usr/ return bernoulli_log_pdf def sample(self, num_samples, num_dims, seed=None): -@@ -195,7 +200,7 @@ +@@ -195,7 +195,7 @@ sample_shape = [num_samples, num_dims, self.size] indices = tf.meshgrid(tf.range(num_samples), self.scope, tf.range(self.size)) indices = tf.stack(indices, axis=-1) @@ -936,7 +929,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/RAT_SPN.py /usr/ samples = tf.scatter_nd(indices, sample_values, sample_shape) return samples -@@ -223,7 +228,7 @@ +@@ -223,7 +223,7 @@ def forward(self, inputs): dists1 = inputs[0] dists2 = inputs[1] @@ -945,7 +938,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/RAT_SPN.py /usr/ num_dist1 = int(dists1.shape[1]) num_dist2 = int(dists2.shape[1]) -@@ -234,7 +239,7 @@ +@@ -234,7 +234,7 @@ # product == sum in log-domain prod = dists1_expand + dists2_expand # flatten out the outer product @@ -954,7 +947,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/RAT_SPN.py /usr/ return prod -@@ -279,8 +284,8 @@ +@@ -279,8 +279,8 @@ if args.sum_weight_l2: exp_weights = tf.exp(self.weights) weight_decay = tf.multiply(tf.nn.l2_loss(exp_weights), args.sum_weight_l2) @@ -965,7 +958,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/RAT_SPN.py /usr/ else: self.weights = self.params -@@ -289,7 +294,7 @@ +@@ -289,7 +289,7 @@ weights = self.weights if self.args.linear_sum_weights: @@ -974,7 +967,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/RAT_SPN.py /usr/ else: prods = tf.expand_dims(prods, axis=-1) if self.dropout_op is not None: -@@ -299,26 +304,26 @@ +@@ -299,26 +299,26 @@ dropout_shape = [batch_size, prod_num, self.size] random_tensor = random_ops.random_uniform(dropout_shape, dtype=self.weights.dtype) @@ -1006,7 +999,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/RAT_SPN.py /usr/ others = tf.meshgrid(tf.range(inputs.shape[1]), tf.range(inputs.shape[0]), tf.range(self.size)) -@@ -355,7 +360,7 @@ +@@ -355,7 +355,7 @@ self.output_vector = None # make the SPN... @@ -1015,7 +1008,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/RAT_SPN.py /usr/ if region_graph is not None: self._make_spn_from_region_graph() elif vector_list is not None: -@@ -363,7 +368,7 @@ +@@ -363,7 +363,7 @@ else: raise ValueError("Either vector_list or region_graph must not be None") @@ -1024,7 +1017,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/RAT_SPN.py /usr/ self.num_dims = len(self.output_vector.scope) def _make_spn_from_vector_list(self, vector_list, sess): -@@ -380,7 +385,7 @@ +@@ -380,7 +380,7 @@ bernoulli_vector = BernoulliVector( scope, self.args, name, given_params=a_node.probs.eval(session=sess) ) @@ -1033,7 +1026,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/RAT_SPN.py /usr/ sess.run(init_new_vars_op) self.vector_list[0].append(bernoulli_vector) node_to_vec[id(a_node)] = bernoulli_vector -@@ -393,7 +398,7 @@ +@@ -393,7 +393,7 @@ given_means=a_node.means.eval(session=sess), given_stddevs=a_node.sigma_params.eval(session=sess), ) @@ -1042,9 +1035,9 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/RAT_SPN.py /usr/ [gauss_vector.means, gauss_vector.sigma_params], name="init" ) sess.run(init_new_vars_op) -diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/train_mnist.py /usr/lib/python3.8/site-packages/spn/experiments/RandomSPNs/train_mnist.py ---- spn/experiments/RandomSPNs/train_mnist.py 2019-10-25 11:20:57.000000000 -0300 -+++ /usr/lib/python3.8/site-packages/spn/experiments/RandomSPNs/train_mnist.py 2020-02-04 15:22:34.000000000 -0300 +diff '--color=auto' -x '*.pyc' -x __pycache__ -Naur /usr/lib/python3.8/site-packages/spn/experiments/RandomSPNs/train_mnist.py spn/experiments/RandomSPNs/train_mnist.py +--- /usr/lib/python3.8/site-packages/spn/experiments/RandomSPNs/train_mnist.py 2020-02-06 14:44:53.000000000 -0300 ++++ spn/experiments/RandomSPNs/train_mnist.py 2020-02-06 14:46:33.921177352 -0300 @@ -27,24 +27,24 @@ return (train_im, train_lab), (test_im, test_lab) @@ -1097,14 +1090,13 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/experiments/RandomSPNs/train_mnist.py / output_tensor = spn.forward(input_ph) tf_output = sess.run(output_tensor, feed_dict={input_ph: dummy_input}) -diff -x '*.pyc' -x __pycache__ -Naur spn/gpu/TensorFlow.py /usr/lib/python3.8/site-packages/spn/gpu/TensorFlow.py ---- spn/gpu/TensorFlow.py 2019-11-01 10:10:05.000000000 -0300 -+++ /usr/lib/python3.8/site-packages/spn/gpu/TensorFlow.py 2020-02-04 16:33:40.567003875 -0300 -@@ -19,15 +19,17 @@ +diff '--color=auto' -x '*.pyc' -x __pycache__ -Naur /usr/lib/python3.8/site-packages/spn/gpu/TensorFlow.py spn/gpu/TensorFlow.py +--- /usr/lib/python3.8/site-packages/spn/gpu/TensorFlow.py 2020-02-06 14:44:53.000000000 -0300 ++++ spn/gpu/TensorFlow.py 2020-02-06 14:56:29.021290273 -0300 +@@ -19,15 +19,16 @@ logger = logging.getLogger(__name__) -+tf_major_version = int(tf.version.VERSION[0]) +tf.compat.v1.disable_eager_execution() def log_sum_to_tf_graph(node, children, data_placeholder=None, variable_dict=None, log_space=True, dtype=np.float32): @@ -1121,7 +1113,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/gpu/TensorFlow.py /usr/lib/python3.8/si def tf_graph_to_sum(node, tfvar): -@@ -36,12 +38,12 @@ +@@ -36,12 +37,12 @@ def log_prod_to_tf_graph(node, children, data_placeholder=None, variable_dict=None, log_space=True, dtype=np.float32): assert log_space @@ -1136,7 +1128,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/gpu/TensorFlow.py /usr/lib/python3.8/si inps = np.arange(int(max(node.breaks))).reshape((-1, 1)) tmpscope = node.scope[0] node.scope[0] = 0 -@@ -72,11 +74,11 @@ +@@ -72,11 +73,11 @@ def spn_to_tf_graph(node, data, batch_size=None, node_tf_graph=_node_log_tf_graph, log_space=True, dtype=None): @@ -1150,7 +1142,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/gpu/TensorFlow.py /usr/lib/python3.8/si variable_dict = {} tf_graph = eval_spn_bottom_up( node, -@@ -95,7 +97,7 @@ +@@ -95,7 +96,7 @@ for n, tfvars in variable_dict.items(): tensors.append(tfvars) @@ -1159,7 +1151,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/gpu/TensorFlow.py /usr/lib/python3.8/si for i, (n, tfvars) in enumerate(variable_dict.items()): tf_graph_to_node[type(n)](n, variable_list[i]) -@@ -103,7 +105,7 @@ +@@ -103,7 +104,7 @@ def likelihood_loss(tf_graph): # minimize negative log likelihood @@ -1168,7 +1160,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/gpu/TensorFlow.py /usr/lib/python3.8/si def optimize_tf( -@@ -111,7 +113,7 @@ +@@ -111,7 +112,7 @@ data: np.ndarray, epochs=1000, batch_size: int = None, @@ -1177,7 +1169,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/gpu/TensorFlow.py /usr/lib/python3.8/si return_loss=False, ) -> Union[Tuple[Node, List[float]], Node]: """ -@@ -148,14 +150,14 @@ +@@ -148,14 +149,14 @@ tf_graph, variable_dict, data_placeholder, data, epochs=1000, batch_size=None, optimizer=None ) -> List[float]: if optimizer is None: @@ -1196,7 +1188,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/gpu/TensorFlow.py /usr/lib/python3.8/si if not batch_size: batch_size = data.shape[0] batches_per_epoch = data.shape[0] // batch_size -@@ -189,26 +191,26 @@ +@@ -189,26 +190,26 @@ def eval_tf_graph(tf_graph, data_placeholder, data, save_graph_path=None): @@ -1231,7 +1223,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/gpu/TensorFlow.py /usr/lib/python3.8/si start = time.perf_counter() result = sess.run(tf_graph, feed_dict={data_placeholder: data}, options=run_options, run_metadata=run_metadata) -@@ -230,7 +232,7 @@ +@@ -230,7 +231,7 @@ return result, elapsed if save_graph_path is not None: @@ -1240,18 +1232,9 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/gpu/TensorFlow.py /usr/lib/python3.8/si if trace: summary_fw.add_run_metadata(run_metadata, "run") -diff -x '*.pyc' -x __pycache__ -Naur spn/structure/leaves/parametric/Tensorflow.py /usr/lib/python3.8/site-packages/spn/structure/leaves/parametric/Tensorflow.py ---- spn/structure/leaves/parametric/Tensorflow.py 2019-03-22 07:35:30.000000000 -0300 -+++ /usr/lib/python3.8/site-packages/spn/structure/leaves/parametric/Tensorflow.py 2020-02-04 15:51:06.150432240 -0300 -@@ -6,7 +6,7 @@ - - import tensorflow as tf - --from spn.gpu.TensorFlow import add_node_to_tf_graph, add_tf_graph_to_node -+from spn.gpu.TensorFlow import add_node_to_tf_graph, add_tf_graph_to_node, tf_major_version - from spn.structure.leaves.parametric.Parametric import ( - Gaussian, - Categorical, +diff '--color=auto' -x '*.pyc' -x __pycache__ -Naur /usr/lib/python3.8/site-packages/spn/structure/leaves/parametric/Tensorflow.py spn/structure/leaves/parametric/Tensorflow.py +--- /usr/lib/python3.8/site-packages/spn/structure/leaves/parametric/Tensorflow.py 2020-02-06 14:44:53.000000000 -0300 ++++ spn/structure/leaves/parametric/Tensorflow.py 2020-02-06 14:55:43.414359616 -0300 @@ -23,12 +23,12 @@ @@ -1284,7 +1267,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/structure/leaves/parametric/Tensorflow. if log_space: return dist.log_prob(data_placeholder[:, node.scope[0]]) -@@ -48,10 +48,14 @@ +@@ -48,10 +48,11 @@ def poisson_to_tf_graph(node, data_placeholder=None, log_space=True, variable_dict=None, dtype=np.float32): @@ -1294,15 +1277,12 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/structure/leaves/parametric/Tensorflow. + mean = tf.maximum(tf.compat.v1.get_variable("lambda", initializer=node.mean, dtype=dtype), 0.001) variable_dict[node] = mean - dist = tf.contrib.distributions.Poisson(rate=mean) -+ if tf_major_version == 1: -+ dist = tf.contrib.distributions.Poisson(rate=mean) -+ else: -+ import tensorflow_probability as tfp -+ dist = tfp.distributions.Poisson(rate=mean) ++ import tensorflow_probability as tfp ++ dist = tfp.distributions.Poisson(rate=mean) if log_space: return dist.log_prob(data_placeholder[:, node.scope[0]]) -@@ -59,10 +63,10 @@ +@@ -59,10 +60,10 @@ def bernoulli_to_tf_graph(node, data_placeholder=None, log_space=True, variable_dict=None, dtype=np.float32): @@ -1316,7 +1296,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/structure/leaves/parametric/Tensorflow. if log_space: return dist.log_prob(data_placeholder[:, node.scope[0]]) -@@ -70,11 +74,11 @@ +@@ -70,11 +71,11 @@ def gamma_to_tf_graph(node, data_placeholder=None, log_space=True, variable_dict=None, dtype=np.float32): @@ -1332,7 +1312,7 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/structure/leaves/parametric/Tensorflow. if log_space: return dist.log_prob(data_placeholder[:, node.scope[0]]) -@@ -82,14 +86,19 @@ +@@ -82,14 +83,16 @@ def lognormal_to_tf_graph(node, data_placeholder=None, log_space=True, variable_dict=None, dtype=np.float32): @@ -1347,18 +1327,15 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/structure/leaves/parametric/Tensorflow. - dist = tf.contrib.distributions.TransformedDistribution( - distribution=tf.distributions.Normal(loc=mean, scale=stdev), - bijector=tf.contrib.distributions.bijectors.Exp(), -+ if tf_major_version == 1: -+ import tf.contrib.distributions as dists -+ else: -+ import tensorflow_probability as tfp -+ from tensorflow_probability import distributions as dists ++ import tensorflow_probability as tfp ++ from tensorflow_probability import distributions as dists + dist = dists.TransformedDistribution( + distribution=tf.compat.v1.distributions.Normal(loc=mean, scale=stdev), + bijector=tfp.bijectors.Exp(), name="LogNormalDistribution", ) if log_space: -@@ -99,15 +108,15 @@ +@@ -99,15 +102,15 @@ def categorical_to_tf_graph(node, data_placeholder=None, log_space=True, variable_dict=None, dtype=np.float32): @@ -1378,9 +1355,9 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/structure/leaves/parametric/Tensorflow. def tf_graph_to_gaussian(node, tfvar): -diff -x '*.pyc' -x __pycache__ -Naur spn/tests/test_rat_spn.py /usr/lib/python3.8/site-packages/spn/tests/test_rat_spn.py ---- spn/tests/test_rat_spn.py 2018-12-21 16:34:26.000000000 -0200 -+++ /usr/lib/python3.8/site-packages/spn/tests/test_rat_spn.py 2020-02-04 15:22:34.000000000 -0300 +diff '--color=auto' -x '*.pyc' -x __pycache__ -Naur /usr/lib/python3.8/site-packages/spn/tests/test_rat_spn.py spn/tests/test_rat_spn.py +--- /usr/lib/python3.8/site-packages/spn/tests/test_rat_spn.py 2020-02-06 14:44:53.000000000 -0300 ++++ spn/tests/test_rat_spn.py 2020-02-06 14:46:25.667713125 -0300 @@ -10,7 +10,7 @@ class TestRatSpn(unittest.TestCase): def test_inference_results(self): @@ -1404,9 +1381,9 @@ diff -x '*.pyc' -x __pycache__ -Naur spn/tests/test_rat_spn.py /usr/lib/python3. output_tensor = spn.forward(input_ph) tf_output = sess.run(output_tensor, feed_dict={input_ph: dummy_input}) -diff -x '*.pyc' -x __pycache__ -Naur spn/tests/test_tensorflow.py /usr/lib/python3.8/site-packages/spn/tests/test_tensorflow.py ---- spn/tests/test_tensorflow.py 2019-03-22 07:35:30.000000000 -0300 -+++ /usr/lib/python3.8/site-packages/spn/tests/test_tensorflow.py 2020-02-04 15:22:34.000000000 -0300 +diff '--color=auto' -x '*.pyc' -x __pycache__ -Naur /usr/lib/python3.8/site-packages/spn/tests/test_tensorflow.py spn/tests/test_tensorflow.py +--- /usr/lib/python3.8/site-packages/spn/tests/test_tensorflow.py 2020-02-06 14:44:53.000000000 -0300 ++++ spn/tests/test_tensorflow.py 2020-02-06 14:46:29.631109356 -0300 @@ -47,8 +47,8 @@ tf_graph, data_placeholder, variable_dict = spn_to_tf_graph(spn_copy, data, 1) |