[release] TF1.0

README.md

@@ -11,8 +11,8 @@ TensorFlow / TensorLayer implementation of [Deep Convolutional Generative Advers
 ## Prerequisites
 
 - Python 2.7 or Python 3.3+
-- [TensorFlow==0.10.0 or higher](https://www.tensorflow.org/)
-- [TensorLayer==1.2.6 or higher](https://github.com/zsdonghao/tensorlayer) (already in this repo)
+- [TensorFlow==1.0+](https://www.tensorflow.org/)
+- [TensorLayer==1.4+](https://github.com/zsdonghao/tensorlayer)
 
 
 ## Usage
@@ -25,4 +25,6 @@ To train a model with downloaded dataset:
 
     $ python main.py
 
+## Result
 
+![alt tag](result.png)

model.py

@@ -0,0 +1,89 @@
+
+import tensorflow as tf
+import tensorlayer as tl
+from tensorlayer.layers import *
+
+
+flags = tf.app.flags
+FLAGS = flags.FLAGS
+
+
+
+def generator_simplified_api(inputs, is_train=True, reuse=False):
+    image_size = 64
+    s2, s4, s8, s16 = int(image_size/2), int(image_size/4), int(image_size/8), int(image_size/16)
+    gf_dim = 64 # Dimension of gen filters in first conv layer. [64]
+    c_dim = FLAGS.c_dim # n_color 3
+    batch_size = FLAGS.batch_size # 64
+
+    w_init = tf.random_normal_initializer(stddev=0.02)
+    gamma_init = tf.random_normal_initializer(1., 0.02)
+
+    with tf.variable_scope("generator", reuse=reuse):
+        tl.layers.set_name_reuse(reuse)
+
+        net_in = InputLayer(inputs, name='g/in')
+        net_h0 = DenseLayer(net_in, n_units=gf_dim*8*s16*s16, W_init=w_init,
+                act = tf.identity, name='g/h0/lin')
+        net_h0 = ReshapeLayer(net_h0, shape=[-1, s16, s16, gf_dim*8], name='g/h0/reshape')
+        net_h0 = BatchNormLayer(net_h0, act=tf.nn.relu, is_train=is_train,
+                gamma_init=gamma_init, name='g/h0/batch_norm')
+
+        net_h1 = DeConv2d(net_h0, gf_dim*4, (5, 5), out_size=(s8, s8), strides=(2, 2),
+                padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h1/decon2d')
+        net_h1 = BatchNormLayer(net_h1, act=tf.nn.relu, is_train=is_train,
+                gamma_init=gamma_init, name='g/h1/batch_norm')
+
+        net_h2 = DeConv2d(net_h1, gf_dim*2, (5, 5), out_size=(s4, s4), strides=(2, 2),
+                padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h2/decon2d')
+        net_h2 = BatchNormLayer(net_h2, act=tf.nn.relu, is_train=is_train,
+                gamma_init=gamma_init, name='g/h2/batch_norm')
+
+        net_h3 = DeConv2d(net_h2, gf_dim, (5, 5), out_size=(s2, s2), strides=(2, 2),
+                padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h3/decon2d')
+        net_h3 = BatchNormLayer(net_h3, act=tf.nn.relu, is_train=is_train,
+                gamma_init=gamma_init, name='g/h3/batch_norm')
+
+        net_h4 = DeConv2d(net_h3, c_dim, (5, 5), out_size=(image_size, image_size), strides=(2, 2),
+                padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h4/decon2d')
+        logits = net_h4.outputs
+        net_h4.outputs = tf.nn.tanh(net_h4.outputs)
+    return net_h4, logits
+
+
+def discriminator_simplified_api(inputs, is_train=True, reuse=False):
+    df_dim = 64 # Dimension of discrim filters in first conv layer. [64]
+    c_dim = FLAGS.c_dim # n_color 3
+    batch_size = FLAGS.batch_size # 64
+
+    w_init = tf.random_normal_initializer(stddev=0.02)
+    gamma_init = tf.random_normal_initializer(1., 0.02)
+
+    with tf.variable_scope("discriminator", reuse=reuse):
+        tl.layers.set_name_reuse(reuse)
+
+        net_in = InputLayer(inputs, name='d/in')
+        net_h0 = Conv2d(net_in, df_dim, (5, 5), (2, 2), act=lambda x: tl.act.lrelu(x, 0.2),
+                padding='SAME', W_init=w_init, name='d/h0/conv2d')
+
+        net_h1 = Conv2d(net_h0, df_dim*2, (5, 5), (2, 2), act=None,
+                padding='SAME', W_init=w_init, name='d/h1/conv2d')
+        net_h1 = BatchNormLayer(net_h1, act=lambda x: tl.act.lrelu(x, 0.2),
+                is_train=is_train, gamma_init=gamma_init, name='d/h1/batch_norm')
+
+        net_h2 = Conv2d(net_h1, df_dim*4, (5, 5), (2, 2), act=None,
+                padding='SAME', W_init=w_init, name='d/h2/conv2d')
+        net_h2 = BatchNormLayer(net_h2, act=lambda x: tl.act.lrelu(x, 0.2),
+                is_train=is_train, gamma_init=gamma_init, name='d/h2/batch_norm')
+
+        net_h3 = Conv2d(net_h2, df_dim*8, (5, 5), (2, 2), act=None,
+                padding='SAME', W_init=w_init, name='d/h3/conv2d')
+        net_h3 = BatchNormLayer(net_h3, act=lambda x: tl.act.lrelu(x, 0.2),
+                is_train=is_train, gamma_init=gamma_init, name='d/h3/batch_norm')
+
+        net_h4 = FlattenLayer(net_h3, name='d/h4/flatten')
+        net_h4 = DenseLayer(net_h4, n_units=1, act=tf.identity,
+                W_init = w_init, name='d/h4/lin_sigmoid')
+        logits = net_h4.outputs
+        net_h4.outputs = tf.nn.sigmoid(net_h4.outputs)
+    return net_h4, logits

tensorlayer/__init__.py

@@ -25,4 +25,7 @@
 from . import rein
 
 
-__version__ = "1.2.3"
+__version__ = "1.4.2"
+
+global_flag = {}
+global_dict = {}

tensorlayer/activation.py

@@ -97,11 +97,13 @@ def pixel_wise_softmax(output, name='pixel_wise_softmax'):
     - `tf.reverse <https://www.tensorflow.org/versions/master/api_docs/python/array_ops.html#reverse>`_
     """
     with tf.name_scope(name) as scope:
-        exp_map = tf.exp(output)
-        if output.get_shape().ndims == 4:   # 2d image
-            evidence = tf.add(exp_map, tf.reverse(exp_map, [False, False, False, True]))
-        elif output.get_shape().ndims == 5: # 3d image
-            evidence = tf.add(exp_map, tf.reverse(exp_map, [False, False, False, False, True]))
-        else:
-            raise Exception("output parameters should be 2d or 3d image, not %s" % str(output._shape))
-        return tf.div(exp_map, evidence)
+        return tf.nn.softmax(output)
+        ## old implementation
+        # exp_map = tf.exp(output)
+        # if output.get_shape().ndims == 4:   # 2d image
+        #     evidence = tf.add(exp_map, tf.reverse(exp_map, [False, False, False, True]))
+        # elif output.get_shape().ndims == 5: # 3d image
+        #     evidence = tf.add(exp_map, tf.reverse(exp_map, [False, False, False, False, True]))
+        # else:
+        #     raise Exception("output parameters should be 2d or 3d image, not %s" % str(output._shape))
+        # return tf.div(exp_map, evidence)