Bumped numpy from 1.13.1 to 1.14.0 to satisfy TF 1.12.2 dependency. Updated grep commands in validator to also be compatible with BSD.

script/git-hooks/pre-commit

@@ -14,7 +14,7 @@
 #   ln -s ../../script/git-hooks/pre-commit ./pre-commit
 
 
-FILES=$(git diff --name-only --cached --diff-filter=d | grep -E '*\.(py|java)$')
+FILES=$(git diff --name-only --cached --diff-filter=d | grep -E '.*\.(py|java)$')
 
 WEBSITE_TESTS_RESULT=0
 ANALYSIS_TESTS_RESULT=0

script/validators/source_validator.py

@@ -206,15 +206,15 @@ def validate_dir(root_dir):
 def get_git_files(state):
     if state == 'staged':
         # Files staged for commit
-        cmd = r"git diff --name-only --cached --diff-filter=d | grep -E '*\.(py|java)$'"
+        cmd = r"git diff --name-only --cached --diff-filter=d | grep -E '.*\.(py|java)$'"
 
     elif state == 'unstaged':
         # Tracked files not staged for commit
-        cmd = r"git diff --name-only --diff-filter=d | grep -E '*\.(py|java)$'"
+        cmd = r"git diff --name-only --diff-filter=d | grep -E '.*\.(py|java)$'"
 
     elif state == 'untracked':
         # Untracked files not staged for commit
-        cmd = r"git ls-files --other --exclude-standard | grep -E '*\.(py|java)$'"
+        cmd = r"git ls-files --other --exclude-standard | grep -E '.*\.(py|java)$'"
 
     with settings(warn_only=True):
         res = local(cmd, capture=True)

server/analysis/nn_tf.py

@@ -12,7 +12,6 @@ import pickle
 import numpy as np
 import tensorflow as tf
 from tensorflow import keras
-from tensorflow.keras import layers
 
 from .util import get_analysis_logger
 
@@ -50,10 +49,10 @@ class NeuralNet(object):
         self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
         # input X is placeholder, weights are variables.
         self.model = keras.Sequential([
-            layers.Dense(64, activation=tf.nn.relu, input_shape=[n_input]),
-            layers.Dropout(0.5),
-            layers.Dense(64, activation=tf.nn.relu),
-            layers.Dense(1)
+            keras.layers.Dense(64, activation=tf.nn.relu, input_shape=[n_input]),
+            keras.layers.Dropout(0.5),
+            keras.layers.Dense(64, activation=tf.nn.relu),
+            keras.layers.Dense(1)
         ])
         self.model.compile(loss='mean_squared_error',
                            optimizer=self.optimizer,

server/website/requirements.txt

@@ -8,7 +8,7 @@ django-request-logging==0.4.6
 mock==2.0.0
 Fabric3>=1.13.1.post1
 hurry.filesize>=0.9
-numpy==1.13.1
+numpy==1.14.0
 requests==2.18.4
 pycodestyle==2.3.1
 astroid==1.5.1