Merge pull request #2 from zsdonghao/master

a

Author: vyokky

docs/modules/layers.rst

@@ -269,6 +269,7 @@ Layer list
    PadLayer
    UpSampling2dLayer
    DownSampling2dLayer
+   DeformableConv2dLayer
    AtrousConv1dLayer
    AtrousConv2dLayer
 
@@ -303,6 +304,11 @@ Layer list
 
    RNNLayer
    BiRNNLayer
+
+   ConvRNNCell
+   BasicConvLSTMCell
+   ConvLSTMLayer
+
    advanced_indexing_op
    retrieve_seq_length_op
    retrieve_seq_length_op2
@@ -458,6 +464,10 @@ Convolutional layer (Pro)
 ^^^^^^^^^^^^^^^^^^^^^^^
 .. autoclass:: DownSampling2dLayer
 
+2D Deformable Conv
+^^^^^^^^^^^^^^^^^^^^^^^
+.. autoclass:: DeformableConv2dLayer
+
 1D Atrous convolution
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 .. autofunction:: AtrousConv1dLayer
@@ -608,6 +618,24 @@ Bidirectional layer
 .. autoclass:: BiRNNLayer
 
 
+
+Recurrent Convolutional layer
+-------------------------------
+
+Conv RNN Cell
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+.. autoclass:: ConvRNNCell
+
+Basic Conv LSTM Cell
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+.. autoclass:: BasicConvLSTMCell
+
+Conv LSTM layer
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+.. autoclass:: ConvLSTMLayer
+
+
+
 Advanced Ops for Dynamic RNN
 -------------------------------
 These operations usually be used inside Dynamic RNN layer, they can

example/tutorial_mnist.py

@@ -61,13 +61,13 @@ def main_test_layers(model='relu'):
         network = tl.layers.InputLayer(x, name='input')
         network = tl.layers.DropoutLayer(network, keep=0.8, name='drop1')
         network = tl.layers.DenseLayer(network, n_units=800,
-                                        act = tf.nn.relu, name='relu1')
+                                        act=tf.nn.relu, name='relu1')
         network = tl.layers.DropoutLayer(network, keep=0.5, name='drop2')
         network = tl.layers.DenseLayer(network, n_units=800,
-                                        act = tf.nn.relu, name='relu2')
+                                        act=tf.nn.relu, name='relu2')
         network = tl.layers.DropoutLayer(network, keep=0.5, name='drop3')
         network = tl.layers.DenseLayer(network, n_units=10,
-                                        act = tf.identity,
+                                        act=tf.identity,
                                         name='output')
     elif model == 'dropconnect':
         network = tl.layers.InputLayer(x, name='input')
@@ -79,7 +79,7 @@ def main_test_layers(model='relu'):
                                                 name='dropconnect_relu2')
         network = tl.layers.DropconnectDenseLayer(network, keep = 0.5,
                                                 n_units=10,
-                                                act = tf.identity,
+                                                act=tf.identity,
                                                 name='output')
 
     # To print all attributes of a Layer.
@@ -295,20 +295,18 @@ def main_test_stacked_denoise_AE(model='relu'):
     network = tl.layers.DropoutLayer(network, keep=0.5, name='denoising1')
     # 1st layer
     network = tl.layers.DropoutLayer(network, keep=0.8, name='drop1')
-    network = tl.layers.DenseLayer(network, n_units=800, act = act, name=model+'1')
+    network = tl.layers.DenseLayer(network, n_units=800, act=act, name=model+'1')
     x_recon1 = network.outputs
     recon_layer1 = tl.layers.ReconLayer(network, x_recon=x, n_units=784,
-                                        act = act_recon, name='recon_layer1')
+                                        act=act_recon, name='recon_layer1')
     # 2nd layer
     network = tl.layers.DropoutLayer(network, keep=0.5, name='drop2')
     network = tl.layers.DenseLayer(network, n_units=800, act = act, name=model+'2')
     recon_layer2 = tl.layers.ReconLayer(network, x_recon=x_recon1, n_units=800,
-                                        act = act_recon, name='recon_layer2')
+                                        act=act_recon, name='recon_layer2')
     # 3rd layer
     network = tl.layers.DropoutLayer(network, keep=0.5, name='drop3')
-    network = tl.layers.DenseLayer(network, n_units=10,
-                                            act = tf.identity,
-                                            name='output')
+    network = tl.layers.DenseLayer(network, 10, act=tf.identity, name='output')
 
     # Define fine-tune process
     y = network.outputs
@@ -485,23 +483,20 @@ def main_test_cnn_layer():
     #                     pool = tf.nn.max_pool,
     #                     name ='pool2',)   # output: (?, 7, 7, 64)
     ## Simplified conv API for beginner (the same with the above layers)
-    network = tl.layers.Conv2d(network, n_filter=32, filter_size=(5, 5), strides=(1, 1),
+    network = tl.layers.Conv2d(network, 32, (5, 5), (1, 1),
             act=tf.nn.relu, padding='SAME', name='cnn1')
-    network = tl.layers.MaxPool2d(network, filter_size=(2, 2), strides=(2, 2),
+    network = tl.layers.MaxPool2d(network, (2, 2), (2, 2),
             padding='SAME', name='pool1')
-    network = tl.layers.Conv2d(network, n_filter=64, filter_size=(5, 5), strides=(1, 1),
+    network = tl.layers.Conv2d(network, 64, (5, 5), (1, 1),
             act=tf.nn.relu, padding='SAME', name='cnn2')
-    network = tl.layers.MaxPool2d(network, filter_size=(2, 2), strides=(2, 2),
+    network = tl.layers.MaxPool2d(network, (2, 2), (2, 2),
             padding='SAME', name='pool2')
     ## end of conv
     network = tl.layers.FlattenLayer(network, name='flatten')
     network = tl.layers.DropoutLayer(network, keep=0.5, name='drop1')
-    network = tl.layers.DenseLayer(network, n_units=256,
-                                    act = tf.nn.relu, name='relu1')
+    network = tl.layers.DenseLayer(network, 256, act=tf.nn.relu, name='relu1')
     network = tl.layers.DropoutLayer(network, keep=0.5, name='drop2')
-    network = tl.layers.DenseLayer(network, n_units=10,
-                                    act = tf.identity,
-                                    name='output')
+    network = tl.layers.DenseLayer(network, 10, act=tf.identity, name='output')
 
     y = network.outputs