- Unnecessary random call fixed issue #92

Author: terrytangyuan

examples/boston.py

@@ -12,15 +12,11 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-import random
-
 from sklearn import datasets, cross_validation, metrics
 from sklearn import preprocessing
 
 import skflow
 
-random.seed(42)
-
 # Load dataset
 boston = datasets.load_boston()
 X, y = boston.data, boston.target

examples/digits.py

@@ -12,7 +12,6 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-import random
 from sklearn import datasets, cross_validation, metrics
 import tensorflow as tf
 

examples/iris.py

@@ -12,14 +12,10 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-import random
-
 from sklearn import datasets, metrics, cross_validation
 
 import skflow
 
-random.seed(42)
-
 # Load dataset.
 iris = datasets.load_iris()
 X_train, X_test, y_train, y_test = cross_validation.train_test_split(iris.data, iris.target,

examples/iris_custom_decay_dnn.py

@@ -12,17 +12,12 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-import random
-
 from sklearn import datasets, metrics
 from sklearn.cross_validation import train_test_split
 
 import skflow
 import tensorflow as tf
 
-
-random.seed(42)
-
 iris = datasets.load_iris()
 X_train, X_test, y_train, y_test = train_test_split(iris.data,
                                                     iris.target,

examples/iris_custom_model.py

@@ -12,17 +12,13 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-import random
-
 import skflow
 from sklearn import datasets, metrics, cross_validation
 
 iris = datasets.load_iris()
 X_train, X_test, y_train, y_test = cross_validation.train_test_split(iris.data, iris.target,
     test_size=0.2, random_state=42)
 
-random.seed(42)
-
 def my_model(X, y):
     """This is DNN with 10, 20, 10 hidden layers, and dropout of 0.9 probability."""
     layers = skflow.ops.dnn(X, [10, 20, 10], keep_prob=0.9)

examples/iris_early_stopping.py

@@ -15,16 +15,12 @@
 import tensorflow as tf
 from tensorflow.python.platform import googletest
 
-import random
-
 from sklearn import datasets, metrics
 from sklearn.cross_validation import train_test_split
 
 import skflow
 
 
-random.seed(42)
-
 iris = datasets.load_iris()
 X_train, X_test, y_train, y_test = train_test_split(iris.data,
                                                     iris.target,

examples/iris_save_restore.py

@@ -12,7 +12,6 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-import random
 import shutil
 
 import skflow
@@ -22,8 +21,6 @@
 X_train, X_test, y_train, y_test = cross_validation.train_test_split(iris.data, iris.target,
     test_size=0.2, random_state=42)
 
-random.seed(42)
-
 classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
 classifier.fit(X_train, y_train)
 score = metrics.accuracy_score(y_test, classifier.predict(X_test))

examples/mnist.py

@@ -18,7 +18,6 @@
     https://www.tensorflow.org/versions/master/tutorials/mnist/pros/index.html#deep-mnist-for-experts
 """
 
-import random
 from sklearn import metrics
 
 import tensorflow as tf

examples/multiple_gpu.py

@@ -12,8 +12,6 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-import random
-
 import skflow
 import tensorflow as tf
 from sklearn import datasets, metrics, cross_validation
@@ -22,8 +20,6 @@
 X_train, X_test, y_train, y_test = cross_validation.train_test_split(iris.data, iris.target,
     test_size=0.2, random_state=42)
 
-random.seed(42)
-
 def my_model(X, y):
     """
     This is DNN with 10, 20, 10 hidden layers, and dropout of 0.5 probability.

examples/out_of_core_data_classification.py

@@ -12,8 +12,6 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-import random
-
 from sklearn import datasets, metrics, cross_validation
 
 import skflow
@@ -23,7 +21,6 @@
 # Sometimes when your dataset is too large to hold in the memory
 # you may want to load it into a out-of-core dataframe as provided by dask library
 # to firstly draw sample batches and then load into memory for training. 
-random.seed(42)
 
 # Load dataset.
 iris = datasets.load_iris()