fix exception too broad issue.

Author: luomai

example/tutorial_cifar10_tfrecord.py

@@ -121,7 +121,7 @@ def read_and_decode(filename, is_train=None):
         # 5. Subtract off the mean and divide by the variance of the pixels.
         try:  # TF 0.12+
             img = tf.image.per_image_standardization(img)
-        except:  # earlier TF versions
+        except Exception:  # earlier TF versions
             img = tf.image.per_image_whitening(img)
 
     elif is_train == False:
@@ -130,7 +130,7 @@ def read_and_decode(filename, is_train=None):
         # 2. Subtract off the mean and divide by the variance of the pixels.
         try:  # TF 0.12+
             img = tf.image.per_image_standardization(img)
-        except:  # earlier TF versions
+        except Exception:  # earlier TF versions
             img = tf.image.per_image_whitening(img)
     elif is_train == None:
         img = img

example/tutorial_inceptionV3_tfslim.py

@@ -123,7 +123,7 @@ def print_prob(prob):
     exit()
 try:  # TF12+
     saver.restore(sess, "./inception_v3.ckpt")
-except:  # TF11
+except Exception:  # TF11
     saver.restore(sess, "inception_v3.ckpt")
 print("Model Restored")
 

tensorlayer/db.py

@@ -148,7 +148,7 @@ def find_one_params(self, args=None, sort=None):
             params = self.__deserialization(self.paramsfs.get(f_id).read())
             print("[TensorDB] Find one params SUCCESS, {} took: {}s".format(args, round(time.time() - s, 2)))
             return params, f_id
-        except:
+        except Exception:
             return False, False
 
     @AutoFill

tensorlayer/files.py

@@ -1180,7 +1180,7 @@ def save_npz(save_list=None, name='model.npz', sess=None):
         try:
             for k, value in enumerate(save_list):
                 save_list_var.append(value.eval())
-        except:
+        except Exception:
             logging.info(" Fail to save model, Hint: pass the session into this function, tl.files.save_npz(network.all_params, name='model.npz', sess=sess)")
     np.savez(name, params=save_list_var)
     save_list_var = None

tensorlayer/layers/core.py

@@ -22,7 +22,7 @@
 
 try:  # For TF12 and later
     TF_GRAPHKEYS_VARIABLES = tf.GraphKeys.GLOBAL_VARIABLES
-except:  # For TF11 and before
+except Exception:  # For TF11 and before
     TF_GRAPHKEYS_VARIABLES = tf.GraphKeys.VARIABLES
 
 
@@ -152,7 +152,7 @@ def initialize_rnn_state(state, feed_dict=None):
     """
     try:  # TF1.0
         LSTMStateTuple = tf.contrib.rnn.LSTMStateTuple
-    except:
+    except Exception:
         LSTMStateTuple = tf.nn.rnn_cell.LSTMStateTuple
 
     if isinstance(state, LSTMStateTuple):
@@ -183,7 +183,7 @@ def print_all_variables(train_only=False):
     else:
         try:  # TF1.0+
             t_vars = tf.global_variables()
-        except:  # TF0.12
+        except Exception:  # TF0.12
             t_vars = tf.all_variables()
         logging.info("  [*] printing global variables")
     for idx, v in enumerate(t_vars):
@@ -998,7 +998,7 @@ def __init__(
         p_hat = tf.reduce_mean(activation_out, 0)  # theano: p_hat = T.mean( self.a[i], axis=0 )
         try:  # TF1.0
             KLD = beta * tf.reduce_sum(rho * tf.log(tf.divide(rho, p_hat)) + (1 - rho) * tf.log((1 - rho) / (tf.subtract(float(1), p_hat))))
-        except:  # TF0.12
+        except Exception:  # TF0.12
             KLD = beta * tf.reduce_sum(rho * tf.log(tf.div(rho, p_hat)) + (1 - rho) * tf.log((1 - rho) / (tf.sub(float(1), p_hat))))
             # KLD = beta * tf.reduce_sum( rho * tf.log(rho/ p_hat) + (1- rho) * tf.log((1- rho)/(1- p_hat)) )
             # theano: L1_a = l1_a[i] * T.sum( rho[i] * T.log(rho[i]/ p_hat) + (1- rho[i]) * T.log((1- rho[i])/(1- p_hat)) )

tensorlayer/layers/extend.py

@@ -32,7 +32,7 @@ def __init__(
         with tf.variable_scope(name) as vs:
             try:  # TF12 TF1.0
                 self.outputs = tf.expand_dims(self.inputs, axis=axis)
-            except:  # TF11
+            except Exception:  # TF11
                 self.outputs = tf.expand_dims(self.inputs, dim=axis)
         self.all_layers = list(layer.all_layers)
         self.all_params = list(layer.all_params)

tensorlayer/layers/flow_control.py

@@ -61,7 +61,7 @@ def __init__(self, layers, name='mux_layer'):
             self.inputs.append(l.outputs)
         try:  # TF1.0
             all_inputs = tf.stack(self.inputs, name=name)  # pack means concat a list of tensor in a new dim  # 1.2
-        except:
+        except Exception:
             all_inputs = tf.pack(self.inputs, name=name)  # pack means concat a list of tensor in a new dim  # 1.2
 
         logging.info("MultiplexerLayer %s: n_inputs:%d" % (self.name, self.n_inputs))

tensorlayer/layers/merge.py

@@ -52,7 +52,7 @@ def __init__(
             self.inputs.append(l.outputs)
         try:  # TF1.0
             self.outputs = tf.concat(self.inputs, concat_dim, name=name)
-        except:  # TF0.12
+        except Exception:  # TF0.12
             self.outputs = tf.concat(concat_dim, self.inputs, name=name)
 
         logging.info("ConcatLayer %s: axis: %d" % (self.name, concat_dim))

tensorlayer/layers/recurrent.py

@@ -1268,7 +1268,7 @@ def __init__(
         if 'GRU' in cell_fn.__name__:
             try:
                 cell_init_args.pop('state_is_tuple')
-            except:
+            except Exception:
                 pass
         self.inputs = layer.outputs
 
@@ -1278,7 +1278,7 @@ def __init__(
         # Input dimension should be rank 3 [batch_size, n_steps(max), n_features]
         try:
             self.inputs.get_shape().with_rank(3)
-        except:
+        except Exception:
             raise Exception("RNN : Input dimension should be rank 3 : [batch_size, n_steps(max), n_features]")
 
         # Get the batch_size
@@ -1308,7 +1308,7 @@ def __init__(
                     raise Exception("Invalid dropout type (must be a 2-D tuple of " "float)")
                 try:
                     DropoutWrapper_fn = tf.contrib.rnn.DropoutWrapper
-                except:
+                except Exception:
                     DropoutWrapper_fn = tf.nn.rnn_cell.DropoutWrapper
 
                     # cell_instance_fn1=cell_instance_fn            # HanSheng
@@ -1337,7 +1337,7 @@ def __init__(
             if sequence_length is None:
                 try:  # TF1.0
                     sequence_length = retrieve_seq_length_op(self.inputs if isinstance(self.inputs, tf.Tensor) else tf.stack(self.inputs))
-                except:  # TF0.12
+                except Exception:  # TF0.12
                     sequence_length = retrieve_seq_length_op(self.inputs if isinstance(self.inputs, tf.Tensor) else tf.pack(self.inputs))
 
             if n_layer > 1:
@@ -1373,7 +1373,7 @@ def __init__(
             # Manage the outputs
             try:  # TF1.0
                 outputs = tf.concat(outputs, 2)
-            except:  # TF0.12
+            except Exception:  # TF0.12
                 outputs = tf.concat(2, outputs)
             if return_last:
                 # [batch_size, 2 * n_hidden]
@@ -1386,7 +1386,7 @@ def __init__(
                     # 2D Tensor [n_example, 2 * n_hidden]
                     try:  # TF1.0
                         self.outputs = tf.reshape(tf.concat(outputs, 1), [-1, 2 * n_hidden])
-                    except:  # TF0.12
+                    except Exception:  # TF0.12
                         self.outputs = tf.reshape(tf.concat(1, outputs), [-1, 2 * n_hidden])
                 else:
                     # <akara>:
@@ -1546,7 +1546,7 @@ def __init__(
         if 'GRU' in cell_fn.__name__:
             try:
                 cell_init_args.pop('state_is_tuple')
-            except:
+            except Exception:
                 pass
         # self.inputs = layer.outputs
         logging.info("  [**] Seq2Seq %s: n_hidden:%d cell_fn:%s dropout:%s n_layer:%d" % (self.name, n_hidden, cell_fn.__name__, dropout, n_layer))

tensorlayer/nlp.py

@@ -1048,7 +1048,7 @@ def moses_multi_bleu(hypotheses, references, lowercase=False):
     try:
         multi_bleu_path, _ = urllib.request.urlretrieve("https://raw.githubusercontent.com/moses-smt/mosesdecoder/" "master/scripts/generic/multi-bleu.perl")
         os.chmod(multi_bleu_path, 0o755)
-    except:  # pylint: disable=W0702
+    except Exception:  # pylint: disable=W0702
         tf.logging.info("Unable to fetch multi-bleu.perl script, using local.")
         metrics_dir = os.path.dirname(os.path.realpath(__file__))
         bin_dir = os.path.abspath(os.path.join(metrics_dir, "..", "..", "bin"))