initial

1. CNN(NIN)_cifar10_Kreas.py

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+
+# coding: utf-8
+
+# In[1]:
+
+import keras
+from keras.datasets import cifar10
+from keras.preprocessing.image import ImageDataGenerator
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation, Flatten
+from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
+
+
+# In[2]:
+
+batch_size = 128
+num_classes = 10
+epochs = 200
+data_augmentation = True
+
+
+# In[3]:
+
+# The data, shuffled and split between train and test sets:
+(x_train, y_train), (x_test, y_test) = cifar10.load_data()
+print('x_train shape:', x_train.shape)
+print(x_train.shape[0], 'train samples')
+print(x_test.shape[0], 'test samples')
+
+
+# In[4]:
+
+# Convert class vectors to binary class matrices.
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
+
+model = Sequential()
+
+
+# In[5]:
+
+#layer1
+model.add(Conv2D(192, (5, 5), padding='same', data_format='channels_last',
+                 input_shape=x_train.shape[1:],
+                 activation='relu')) #4D tensor with shape: (samples, channels, rows, cols)
+model.add(Conv2D(160, (1, 1), data_format='channels_last',activation='relu'))
+model.add(Conv2D(96, (1, 1),data_format='channels_last',activation='relu'))
+model.add(MaxPooling2D(pool_size=(3, 3), padding='same', strides=2,data_format='channels_last'))
+model.add(Dropout(0.5))
+
+
+# In[6]:
+
+#layer2
+model.add(Conv2D(192, (5, 5), padding='same',data_format='channels_last',activation='relu'))
+model.add(Conv2D(192, (1, 1),data_format='channels_last',activation='relu'))
+model.add(Conv2D(192, (1, 1),data_format='channels_last',activation='relu'))
+model.add(MaxPooling2D(pool_size=(3, 3), padding='same', strides=2, data_format='channels_last'))
+model.add(Dropout(0.5))
+
+
+# In[7]:
+
+#layer3
+model.add(Conv2D(192, (3, 3), padding='same',data_format='channels_last', activation='relu'))
+model.add(Conv2D(192, (1, 1),data_format='channels_last', activation='relu'))
+model.add(Conv2D(10,(1, 1),data_format='channels_last',activation='relu'))
+model.add(AveragePooling2D(pool_size=(8, 8), strides=1,data_format='channels_last'))
+model.add(Flatten()) 
+model.add(Activation('softmax'))
+
+
+# In[ ]:
+
+opt = keras.optimizers.rmsprop(lr=0.1, decay=1e-6)
+#opt = keras.optimizers.Adam(lr=0.001)
+
+
+# In[8]:
+
+# train model 
+model.compile(loss='categorical_crossentropy',
+              optimizer=opt,
+              metrics=['accuracy'])
+
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
+
+if not data_augmentation:
+    print('Not using data augmentation.')
+    model.fit(x_train, y_train,
+              batch_size=batch_size,
+              epochs=epochs,
+              validation_data=(x_test, y_test),
+              shuffle=True)
+else:
+    print('Using real-time data augmentation.')
+    # This will do preprocessing and realtime data augmentation:
+    datagen = ImageDataGenerator(
+                                featurewise_center=True,  # set input mean to 0 over the dataset
+                                samplewise_center=True,  # set each sample mean to 0
+                                featurewise_std_normalization=True,  # divide inputs by std of the dataset
+                                samplewise_std_normalization=True,  # divide each input by its std
+                                zca_whitening=False,  # apply ZCA whitening
+                                rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
+                                width_shift_range=4/32,  # randomly shift images horizontally (fraction of total width)
+                                height_shift_range=4/32,  # randomly shift images vertically (fraction of total height)
+                                horizontal_flip=True,  # randomly flip images
+                                vertical_flip=False # randomly flip images
+        )
+    # Compute quantities required for feature-wise normalization
+    # (std, mean, and principal components if ZCA whitening is applied).
+    datagen.fit(x_train)
+
+    # Fit the model on the batches generated by datagen.flow().
+    model.fit_generator(datagen.flow(x_train, y_train,
+                                     batch_size=batch_size),
+                        steps_per_epoch=x_train.shape[0] // batch_size,
+                        epochs=epochs,
+                        validation_data=(x_test, y_test) )
+
+
+# In[ ]:
+
+
+

2. VGG19_retrain_cifar10_Keras.py

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+
+# coding: utf-8
+
+# to do:
+#     - Initial learning rate: 0.01, divide by 10 at 81, 122 epoch
+#     - subtract RGB mean value
+#     - conv layer init = random_normal(stddev = 0.03), fc layer init = random_normal(stddev = 0.01)
+
+# In[1]:
+
+
+import numpy as np
+from keras.applications.vgg19 import VGG19
+from keras.preprocessing import image
+from keras.applications.vgg19 import preprocess_input, decode_predictions
+from keras.models import Model, Sequential
+from keras.layers import Input, Dense, Dropout, Flatten, Activation, Conv2D
+from keras.optimizers import SGD
+from keras.datasets import cifar10
+from keras.preprocessing.image import ImageDataGenerator
+from keras.utils import to_categorical
+from keras.initializers import RandomNormal
+from keras import backend as K
+
+
+# ### Data
+
+# In[2]:
+
+batch_size = 128
+num_classes = 10
+epochs = 164
+data_augmentation = True
+
+
+# In[3]:
+
+# The data, shuffled and split between train and test sets:
+(x_train, y_train), (x_test, y_test) = cifar10.load_data()
+# Convert class vectors to binary class matrices.
+y_train = to_categorical(y_train, num_classes)
+y_test = to_categorical(y_test, num_classes)
+# data util
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255 # norm to [0,1]
+x_test /= 255
+datagen = ImageDataGenerator(
+                            featurewise_center=False,  # set input mean to 0 over the dataset
+                            samplewise_center=False,  # set each sample mean to 0
+                            featurewise_std_normalization=False,  # divide inputs by std of the dataset
+                            samplewise_std_normalization=False,  # divide each input by its std
+                            zca_whitening=False,  # apply ZCA whitening
+                            rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
+                            width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
+                            height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
+                            horizontal_flip=True,  # randomly flip images
+                            vertical_flip=False # randomly flip images
+                            )
+datagen.fit(x_train)
+
+
+# ### Model
+
+# In[4]:
+
+base_model = VGG19(weights='imagenet')
+
+
+# In[5]:
+
+conv_init = RandomNormal(stddev=0.03)
+fc_init = RandomNormal(stddev=0.01)
+
+
+# In[6]:
+
+img_input = Input(shape=(32,32,3))
+# get layers from VGG19
+x = base_model.layers[1](img_input)
+for layer in base_model.layers[2:21]: 
+    x = layer(x)
+# add layers by ourselves    
+x = Flatten()(x)
+x = Dense(4096, activation='relu', name='fc6',kernel_initializer=fc_init)(x)
+x = Dropout(0.5)(x)
+x = base_model.layers[24](x)
+x = Dropout(0.5)(x)
+predictions =  Dense(10, activation='softmax', name='fc8',kernel_initializer=fc_init)(x)
+model = Model(inputs=img_input, outputs=predictions)
+
+
+# In[7]:
+
+opt = SGD(lr=0.01,momentum=0.9)
+model.compile(loss='categorical_crossentropy',
+              optimizer=opt,
+              metrics=['accuracy'])
+
+
+
+# ### Train
+
+# In[9]:
+
+model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
+                    steps_per_epoch= (x_train.shape[0] // batch_size),
+                    epochs=epochs,
+                    validation_data=(x_test, y_test) )
+
+
+# In[ ]:
+
+
+