[IMPORTANT] release test script for layers | basic layer update (#389)

* release test script for layers
* update basic layer 
* fixed averaged embedding layer for python2

Author: zsdonghao

.travis.yml

@@ -19,6 +19,20 @@ script:
   - python tests/test_yapf_format.py
   - python tests/test_pydocstyle.py
   - python tests/test_mnist_simple.py
-  - python tests/test_pooling.py
   - python tests/test_reuse_mlp.py
-  - python tests/test_time_distributed.py
+  - python tests/test_layers_basic.py
+  - python tests/test_layers_convolution.py
+  - python tests/test_layers_core.py
+  - python tests/test_layers_extend.py
+  - python tests/test_layers_flow_control.py
+  - python tests/test_layers_importer.py
+  - python tests/test_layers_merge.py
+  - python tests/test_layers_normalization.py
+  - python tests/test_layers_pooling.py
+  - python tests/test_layers_recurrent.py
+  - python tests/test_layers_shape.py
+  - python tests/test_layers_spatial_transformer.py
+  - python tests/test_layers_special_activation.py
+  - python tests/test_layers_stack.py
+  - python tests/test_layers_super_resolution.py
+  - python tests/test_layers_time_distributed.py

example/tutorial_imagenet_inceptionV3_distributed.py

@@ -158,7 +158,7 @@ def build_network(image_input, num_classes=1001, is_training=False):
     net_in = tl.layers.InputLayer(image_input, name='input_layer')
     with slim.arg_scope(inception_v3_arg_scope()):
         network = tl.layers.SlimNetsLayer(
-            layer=net_in, slim_layer=inception_v3, slim_args={
+            prev_layer=net_in, slim_layer=inception_v3, slim_args={
                 'num_classes': num_classes,
                 'is_training': is_training
             }, name='InceptionV3')

example/tutorial_inceptionV3_tfslim.py

@@ -100,7 +100,7 @@ def print_prob(prob):
     # logits, end_points = inception_v3(X, num_classes=1001,
     #                                   is_training=False)
     network = tl.layers.SlimNetsLayer(
-        layer=net_in,
+        prev_layer=net_in,
         slim_layer=inception_v3,
         slim_args={
             'num_classes': 1001,

example/tutorial_keras.py

@@ -17,10 +17,7 @@
 
 batch_size = 128
 x = tf.placeholder(tf.float32, shape=[None, 784])
-y_ = tf.placeholder(
-    tf.int64, shape=[
-        None,
-    ])
+y_ = tf.placeholder(tf.int64, shape=[None])
 
 
 def keras_block(x):