[TEST] TensorFlow 10, 11 Python 2, 3

README.md

@@ -38,7 +38,7 @@ If you already had the pre-requisites ready, the simplest way to install TensorL
 
 
 ```bash
-[for stable version] pip install tensorlayer==1.2.2
+[for stable version] pip install tensorlayer==1.2.3
 [for master version] pip install git+https://github.com/zsdonghao/tensorlayer.git
 ```
 

data/__init__.py

@@ -0,0 +1,6 @@
+
+from __future__ import absolute_import
+
+
+from . import imagenet_classes
+# from . import 

tensorlayer/layers.py

@@ -2671,15 +2671,22 @@ def __init__(
         layer = None,
         slim_layer = None,
         slim_args = {},
-        name ='slim_layer',
+        name ='InceptionV3',
     ):
         Layer.__init__(self, name=name)
         self.inputs = layer.outputs
         print("  tensorlayer:Instantiate SlimNetsLayer %s: %s" % (self.name, slim_layer.__name__))
 
-        with tf.variable_scope(name) as vs:
-            net, end_points = slim_layer(self.inputs, **slim_args)
-            slim_variables = tf.get_collection(tf.GraphKeys.VARIABLES, scope=vs.name)
+        # with tf.variable_scope(name) as vs:
+        #     net, end_points = slim_layer(self.inputs, **slim_args)
+        #     slim_variables = tf.get_collection(tf.GraphKeys.VARIABLES, scope=vs.name)
+
+        net, end_points = slim_layer(self.inputs, **slim_args)
+
+        slim_variables = tf.get_collection(tf.GraphKeys.VARIABLES, scope=name)
+        if slim_variables == []:
+            print("No variables found under %s : the name of SlimNetsLayer should be matched with the begining of the ckpt file, see tutorial_inceptionV3_tfslim.py for more details" % name)
+
 
         self.outputs = net
 

tensorlayer/visualize.py

@@ -58,7 +58,7 @@ def W(W=None, second=10, saveable=True, shape=[28,28], name='mnist', fig_idx=239
             #     feature = np.zeros_like(feature)
             plt.imshow(np.reshape(feature ,(shape[0],shape[1])),
                     cmap='gray', interpolation="nearest")#, vmin=np.min(feature), vmax=np.max(feature))
-            plt.title(name)
+            # plt.title(name)
             # ------------------------------------------------------------
             # plt.imshow(np.reshape(W[:,count-1] ,(np.sqrt(size),np.sqrt(size))), cmap='gray', interpolation="nearest")
             plt.gca().xaxis.set_major_locator(plt.NullLocator())    # distable tick
@@ -223,11 +223,11 @@ def images2d(images=None, second=10, saveable=True, name='images', dtype=None,
                 plt.imshow(
                         np.reshape(images[count-1,:,:], (n_row, n_col)),
                         cmap='gray', interpolation="nearest")
-                plt.title(name)
+                # plt.title(name)
             elif n_color == 3:
                 plt.imshow(images[count-1,:,:],
                         cmap='gray', interpolation="nearest")
-                plt.title(name)
+                # plt.title(name)
             else:
                 raise Exception("Unknown n_color")
             plt.gca().xaxis.set_major_locator(plt.NullLocator())    # distable tick

tutorial_generate_text.py

@@ -1,4 +1,9 @@
- # Copyright 2016 TensorLayer. All Rights Reserved.
+#! /usr/bin/python
+# -*- coding: utf8 -*-
+
+
+
+# Copyright 2016 TensorLayer. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -264,7 +269,7 @@ def inference(x, is_training, num_steps, reuse=None):
                 network = tl.layers.DropoutLayer(network, keep=keep_prob, name='drop1')
             network = tl.layers.RNNLayer(network,
                         cell_fn=tf.nn.rnn_cell.BasicLSTMCell,
-                        cell_init_args={'forget_bias': 0.0},# 'state_is_tuple': True},
+                        cell_init_args={'forget_bias': 0.0, 'state_is_tuple': True},
                         n_hidden=hidden_size,
                         initializer=tf.random_uniform_initializer(-init_scale, init_scale),
                         n_steps=num_steps,
@@ -275,7 +280,7 @@ def inference(x, is_training, num_steps, reuse=None):
                 network = tl.layers.DropoutLayer(network, keep=keep_prob, name='drop2')
             network = tl.layers.RNNLayer(network,
                         cell_fn=tf.nn.rnn_cell.BasicLSTMCell,
-                        cell_init_args={'forget_bias': 0.0}, # 'state_is_tuple': True},
+                        cell_init_args={'forget_bias': 0.0, 'state_is_tuple': True},
                         n_hidden=hidden_size,
                         initializer=tf.random_uniform_initializer(-init_scale, init_scale),
                         n_steps=num_steps,

tutorial_inceptionV3_tfslim.py

@@ -100,7 +100,7 @@ def print_prob(prob):
                                             #  'reuse' : None,
                                             #  'scope' : 'InceptionV3'
                                             },
-                                        name=''
+                                        name='InceptionV3'  # <-- the name should be the same with the ckpt model
                                         )
 saver = tf.train.Saver()
 

tutorial_mnist.py

@@ -103,10 +103,10 @@ def main_test_layers(model='relu'):
 
     params = network.all_params
     # train
-    n_epoch = 1
+    n_epoch = 100
     batch_size = 128
     learning_rate = 0.0001
-    print_freq = 10
+    print_freq = 5
     train_op = tf.train.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999,
                                 epsilon=1e-08, use_locking=False).minimize(cost)
 

tutorial_tfrecord.py

@@ -4,9 +4,10 @@
 
 import tensorflow as tf
 import tensorlayer as tl
-import os
+import numpy as np
 from PIL import Image
 import io
+import os
 
 
 """
@@ -67,7 +68,7 @@
     label = example.features.feature['label'].int64_list.value
     ## converts a image from bytes
     image = Image.frombytes('RGB', (224, 224), img_raw[0])
-    tl.visualize.frame(image, second=0.5, saveable=False, name='frame', fig_idx=1283)
+    tl.visualize.frame(np.asarray(image), second=0.5, saveable=False, name='frame', fig_idx=1283)
     print(label)
 
 

tutorial_tfrecord2.py

@@ -45,7 +45,7 @@
     ## Visualize a image
     # tl.visualize.frame(np.asarray(img, dtype=np.uint8), second=1, saveable=False, name='frame', fig_idx=1236)
     label = int(y_train[index])
-    print(label)
+    # print(label)
     ## Convert the bytes back to image as follow:
         # image = Image.frombytes('RGB', (32, 32), img_raw)
     # image = np.fromstring(img_raw, np.float32)

tutorial_tfrecord3.py

@@ -117,7 +117,7 @@ def _bytes_feature_list(values):
 c = tf.contrib.learn.run_n(features, n=1, feed_dict=None)
 from PIL import Image
 im = Image.frombytes('RGB', (299, 299), c[0]['image/img_raw'])
-tl.visualize.frame(im, second=1, saveable=False, name='frame', fig_idx=1236)
+tl.visualize.frame(np.asarray(im), second=1, saveable=False, name='frame', fig_idx=1236)
 c = tf.contrib.learn.run_n(sequence_features, n=1, feed_dict=None)
 print(c[0])
 
@@ -334,10 +334,16 @@ def prefetch_input_data(reader,
 img = tf.decode_raw(context["image/img_raw"], tf.uint8)
 img = tf.reshape(img, [height, width, 3])
 img = tf.image.convert_image_dtype(img, dtype=tf.float32)
+# for TensorFlow 0.10
+# img = tf.image.resize_images(img,
+#                            new_height=resize_height,
+#                            new_width=resize_width,
+#                            method=tf.image.ResizeMethod.BILINEAR)
+# for TensorFlow 0.11
 img = tf.image.resize_images(img,
-                           new_height=resize_height,
-                           new_width=resize_width,
+                           size=(resize_height, resize_width),
                            method=tf.image.ResizeMethod.BILINEAR)
+
 # Crop to final dimensions.
 if is_training:
     img = tf.random_crop(img, [height, width, 3])