fix vgg finetuning example

examples/images/vgg_network_finetuning.py

@@ -5,15 +5,13 @@
 last layer (softmax) that will be retrained to match the new task (finetuning).
 '''
 import tflearn
-from tflearn.layers.estimator import regression
+from tflearn.data_preprocessing import ImagePreprocessing
 import os
 
 
-def vgg16(num_class, placeholderX=None):
-    x = tflearn.input_data(shape=[None, 224, 224, 3], name='input',
-                           placeholder=placeholderX)
+def vgg16(input, num_class):
 
-    x = tflearn.conv_2d(x, 64, 3, activation='relu', scope='conv1_1')
+    x = tflearn.conv_2d(input, 64, 3, activation='relu', scope='conv1_1')
     x = tflearn.conv_2d(x, 64, 3, activation='relu', scope='conv1_2')
     x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool1')
 
@@ -42,7 +40,8 @@ def vgg16(num_class, placeholderX=None):
     x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc7')
     x = tflearn.dropout(x, 0.5, name='dropout2')
 
-    x = tflearn.fully_connected(x, num_class, activation='softmax', scope='fc8', restore=False)
+    x = tflearn.fully_connected(x, num_class, activation='softmax', scope='fc8',
+                                restore=False)
 
     return x
 
@@ -54,26 +53,38 @@ def vgg16(num_class, placeholderX=None):
 
 from tflearn.data_utils import image_preloader
 
-X, Y = image_preloader(files_list, image_shape=(224, 224), mode='file', categorical_labels=True, normalize=True,
+X, Y = image_preloader(files_list, image_shape=(224, 224), mode='file',
+                       categorical_labels=True, normalize=True,
                        files_extension=['.jpg', '.png'], filter_channel=True)
 # or use the mode 'floder'
-# X, Y = image_preloader(data_dir, image_shape=(224, 224), mode='folder', categorical_labels=True, normalize=True,
+# X, Y = image_preloader(data_dir, image_shape=(224, 224), mode='folder',
+#                        categorical_labels=True, normalize=True,
 #                        files_extension=['.jpg', '.png'], filter_channel=True)
+
 num_classes = 10 # num of your dataset
 
-softmax = vgg16(num_classes)
-regression = regression(softmax, optimizer='adam',
-                        loss='categorical_crossentropy',
-                        learning_rate=0.001, restore=False)
+# VGG preprocessing
+img_prep = ImagePreprocessing()
+img_prep.add_featurewise_zero_center(mean=[123.68, 116.779, 103.939],
+                                     per_channel=True)
+# VGG Network
+x = tflearn.input_data(shape=[None, 224, 224, 3], name='input',
+                       data_preprocessing=img_prep)
+softmax = vgg16(x, num_classes)
+regression = tflearn.regression(softmax, optimizer='adam',
+                                loss='categorical_crossentropy',
+                                learning_rate=0.001, restore=False)
 
 model = tflearn.DNN(regression, checkpoint_path='vgg-finetuning',
-                    max_checkpoints=3, tensorboard_verbose=2, tensorboard_dir="./logs")
+                    max_checkpoints=3, tensorboard_verbose=2,
+                    tensorboard_dir="./logs")
 
 model_file = os.path.join(model_path, "vgg16.tflearn")
 model.load(model_file, weights_only=True)
 
 # Start finetuning
 model.fit(X, Y, n_epoch=10, validation_set=0.1, shuffle=True,
-          show_metric=True, batch_size=64, snapshot_epoch=False, snapshot_step=200, run_id='vgg-finetuning')
+          show_metric=True, batch_size=64, snapshot_epoch=False,
+          snapshot_step=200, run_id='vgg-finetuning')
 
 model.save('your-task-model-retrained-by-vgg')