View source for Trains a ResNet on the CIFAR10 small images dataset

Sumber: https://github.com/keras-team/keras/blob/master/examples/cifar10_resnet.py

 #"""
 #Trains a ResNet on the CIFAR10 dataset.
 # ResNet v1:
 # [Deep Residual Learning for Image Recognition
 # ](https://arxiv.org/pdf/1512.03385.pdf)
 # ResNet v2:
 # [Identity Mappings in Deep Residual Networks
 # ](https://arxiv.org/pdf/1603.05027.pdf)
 # Model|n|200-epoch accuracy|Original paper accuracy |sec/epoch GTX1080Ti
 # :------------|--:|-------:|-----------------------:|---:
 # ResNet20   v1|  3| 92.16 %|                 91.25 %|35
 # ResNet32   v1|  5| 92.46 %|                 92.49 %|50
 # ResNet44   v1|  7| 92.50 %|                 92.83 %|70
 # ResNet56   v1|  9| 92.71 %|                 93.03 %|90
 # ResNet110  v1| 18| 92.65 %|            93.39+-.16 %|165
 # ResNet164  v1| 27|     - %|                 94.07 %|  -
 # ResNet1001 v1|N/A|     - %|                 92.39 %|  -
 # &nbsp;
 # Model|n|200-epoch accuracy|Original paper accuracy |sec/epoch GTX1080Ti
 # :------------|--:|-------:|-----------------------:|---:
 # ResNet20   v2|  2|     - %|                     - %|---
 # ResNet32   v2|N/A| NA    %|            NA         %| NA
 # ResNet44   v2|N/A| NA    %|            NA         %| NA
 # ResNet56   v2|  6| 93.01 %|            NA         %|100
 # ResNet110  v2| 12| 93.15 %|            93.63      %|180
 # ResNet164  v2| 18|     - %|            94.54      %|  -
 # ResNet1001 v2|111|     - %|            95.08+-.14 %|  -
 # """
 
 from __future__ import print_function
 import keras
 from keras.layers import Dense, Conv2D, BatchNormalization, Activation
 from keras.layers import AveragePooling2D, Input, Flatten
 from keras.optimizers import Adam
 from keras.callbacks import ModelCheckpoint, LearningRateScheduler
 from keras.callbacks import ReduceLROnPlateau
 from keras.preprocessing.image import ImageDataGenerator
 from keras.regularizers import l2
 from keras import backend as K
 from keras.models import Model
 from keras.datasets import cifar10
 import numpy as np
 import os
 
 # Training parameters
 batch_size = 32  # orig paper trained all networks with batch_size=128
 epochs = 200
 data_augmentation = True
 num_classes = 10
 
 # Subtracting pixel mean improves accuracy
 subtract_pixel_mean = True
 
 # Model parameter
 # ----------------------------------------------------------------------------
 #           |      | 200-epoch | Orig Paper| 200-epoch | Orig Paper| sec/epoch
 # Model     |  n   | ResNet v1 | ResNet v1 | ResNet v2 | ResNet v2 | GTX1080Ti
 #           |v1(v2)| %Accuracy | %Accuracy | %Accuracy | %Accuracy | v1 (v2)
 # ----------------------------------------------------------------------------
 # ResNet20  | 3 (2)| 92.16     | 91.25     | -----     | -----     | 35 (---)
 # ResNet32  | 5(NA)| 92.46     | 92.49     | NA        | NA        | 50 ( NA)
 # ResNet44  | 7(NA)| 92.50     | 92.83     | NA        | NA        | 70 ( NA)
 # ResNet56  | 9 (6)| 92.71     | 93.03     | 93.01     | NA        | 90 (100)
 # ResNet110 |18(12)| 92.65     | 93.39+-.16| 93.15     | 93.63     | 165(180)
 # ResNet164 |27(18)| -----     | 94.07     | -----     | 94.54     | ---(---)
 # ResNet1001| (111)| -----     | 92.39     | -----     | 95.08+-.14| ---(---)
 # ---------------------------------------------------------------------------
 n = 3
 
 # Model version
 # Orig paper: version = 1 (ResNet v1), Improved ResNet: version = 2 (ResNet v2)
 version = 1
 
 # Computed depth from supplied model parameter n
 if version == 1:
     depth = n * 6 + 2
 elif version == 2:
     depth = n * 9 + 2
 
 # Model name, depth and version
 model_type = 'ResNet%dv%d' % (depth, version)
 
 # Load the CIFAR10 data.
 (x_train, y_train), (x_test, y_test) = cifar10.load_data()
 
 # Input image dimensions.
 input_shape = x_train.shape[1:]
 
 # Normalize data.
 x_train = x_train.astype('float32') / 255
 x_test = x_test.astype('float32') / 255  
 
 # If subtract pixel mean is enabled
 if subtract_pixel_mean:
     x_train_mean = np.mean(x_train, axis=0)
     x_train -= x_train_mean
     x_test -= x_train_mean
 
 print('x_train shape:', x_train.shape)
 print(x_train.shape[0], 'train samples')
 print(x_test.shape[0], 'test samples')
 print('y_train shape:', y_train.shape)
 
 # 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)
 
 def lr_schedule(epoch):
     # """Learning Rate Schedule
     # Learning rate is scheduled to be reduced after 80, 120, 160, 180 epochs.
     # Called automatically every epoch as part of callbacks during training.
     ## Arguments
     #    epoch (int): The number of epochs
     # # Returns
     #     lr (float32): learning rate
     # """
     lr = 1e-3
     if epoch > 180:
         lr *= 0.5e-3
     elif epoch > 160:
         lr *= 1e-3
     elif epoch > 120:
         lr *= 1e-2
     elif epoch > 80:
         lr *= 1e-1
     print('Learning rate: ', lr)
     return lr

 def resnet_layer(inputs,
                  num_filters=16,
                  kernel_size=3,
                  strides=1,
                  activation='relu',
                  batch_normalization=True,
                  conv_first=True):
     # """2D Convolution-Batch Normalization-Activation stack builder
     # # Arguments
     #    inputs (tensor): input tensor from input image or previous layer
     #    num_filters (int): Conv2D number of filters
     #    kernel_size (int): Conv2D square kernel dimensions
     #    strides (int): Conv2D square stride dimensions
     #    activation (string): activation name
     #    batch_normalization (bool): whether to include batch normalization
     #    conv_first (bool): conv-bn-activation (True) or
     #        bn-activation-conv (False)
     # # Returns
     #   x (tensor): tensor as input to the next layer
     # """
     conv = Conv2D(num_filters,
                   kernel_size=kernel_size,
                   strides=strides,
                   padding='same',
                   kernel_initializer='he_normal',
                   kernel_regularizer=l2(1e-4)) 
 
     x = inputs
     if conv_first:
         x = conv(x)
         if batch_normalization:
             x = BatchNormalization()(x)
         if activation is not None:
             x = Activation(activation)(x)
     else:
         if batch_normalization:
             x = BatchNormalization()(x)
         if activation is not None:
             x = Activation(activation)(x)
         x = conv(x)
     return x

 def resnet_v1(input_shape, depth, num_classes=10):
     # """ResNet Version 1 Model builder [a]
     # Stacks of 2 x (3 x 3) Conv2D-BN-ReLU
     # ast ReLU is after the shortcut connection.
     # At the beginning of each stage, the feature map size is halved (downsampled)
     # by a convolutional layer with strides=2, while the number of filters is
     # doubled. Within each stage, the layers have the same number filters and the
     # same number of filters.
     # Features maps sizes:
     # stage 0: 32x32, 16
     # tage 1: 16x16, 32
     # stage 2:  8x8,  64
     # he Number of parameters is approx the same as Table 6 of [a]:
     # ResNet20 0.27M
     # ResNet32 0.46M
     # ResNet44 0.66M
     # ResNet56 0.85M
     # ResNet110 1.7M
     # # Arguments
     #     input_shape (tensor): shape of input image tensor
     #     depth (int): number of core convolutional layers
     #     num_classes (int): number of classes (CIFAR10 has 10)
     # # Returns
     #     model (Model): Keras model instance
     # """
     if (depth - 2) % 6 != 0:
         raise ValueError('depth should be 6n+2 (eg 20, 32, 44 in [a])')
     # Start model definition.
     num_filters = 16
     num_res_blocks = int((depth - 2) / 6)  
 
     inputs = Input(shape=input_shape)
     x = resnet_layer(inputs=inputs)
     # Instantiate the stack of residual units
     for stack in range(3):
         for res_block in range(num_res_blocks):
             strides = 1
             if stack > 0 and res_block == 0:  # first layer but not first stack
                 strides = 2  # downsample
             y = resnet_layer(inputs=x,
                              num_filters=num_filters,
                              strides=strides)
             y = resnet_layer(inputs=y,
                              num_filters=num_filters,
                              activation=None)
             if stack > 0 and res_block == 0:  # first layer but not first stack
                 # linear projection residual shortcut connection to match
                 # changed dims
                 x = resnet_layer(inputs=x,
                                  num_filters=num_filters,
                                  kernel_size=1,
                                  strides=strides,
                                  activation=None,
                                  batch_normalization=False)
             x = keras.layers.add([x, y])
             x = Activation('relu')(x)
         num_filters *= 2 
 
     # Add classifier on top.
     # v1 does not use BN after last shortcut connection-ReLU
     x = AveragePooling2D(pool_size=8)(x)
     y = Flatten()(x)
     outputs = Dense(num_classes,
                     activation='softmax',
                     kernel_initializer='he_normal')(y)
 
     # Instantiate model.
     model = Model(inputs=inputs, outputs=outputs)
     return model

 
 def resnet_v2(input_shape, depth, num_classes=10):
     # """ResNet Version 2 Model builder [b]
     # Stacks of (1 x 1)-(3 x 3)-(1 x 1) BN-ReLU-Conv2D or also known as
     # bottleneck layer
     # First shortcut connection per layer is 1 x 1 Conv2D.
     # Second and onwards shortcut connection is identity.
     # At the beginning of each stage, the feature map size is halved (downsampled)
     # by a convolutional layer with strides=2, while the number of filter maps is
     # doubled. Within each stage, the layers have the same number filters and the
     # same filter map sizes.
     # Features maps sizes:
     # conv1  : 32x32,  16
     # stage 0: 32x32,  64
     # stage 1: 16x16, 128
     # stage 2:  8x8,  256
     # # Arguments
     #   input_shape (tensor): shape of input image tensor
     #   depth (int): number of core convolutional layers
     #   num_classes (int): number of classes (CIFAR10 has 10)
     # # Returns
     #     model (Model): Keras model instance
     # """
     if (depth - 2) % 9 != 0:
         raise ValueError('depth should be 9n+2 (eg 56 or 110 in [b])')
     # Start model definition.
     num_filters_in = 16
     num_res_blocks = int((depth - 2) / 9) 
 
     inputs = Input(shape=input_shape)
     # v2 performs Conv2D with BN-ReLU on input before splitting into 2 paths
     x = resnet_layer(inputs=inputs,
                      num_filters=num_filters_in,
                      conv_first=True) 
 
     # Instantiate the stack of residual units
     for stage in range(3):
         for res_block in range(num_res_blocks):
             activation = 'relu'
             batch_normalization = True
             strides = 1
             if stage == 0:
                 num_filters_out = num_filters_in * 4
                 if res_block == 0:  # first layer and first stage
                     activation = None
                     batch_normalization = False
             else:
                 num_filters_out = num_filters_in * 2
                 if res_block == 0:  # first layer but not first stage
                     strides = 2    # downsample 
 
             # bottleneck residual unit
             y = resnet_layer(inputs=x,
                              num_filters=num_filters_in,
                              kernel_size=1,
                              strides=strides,
                              activation=activation,
                              batch_normalization=batch_normalization,
                              conv_first=False)
             y = resnet_layer(inputs=y,
                              num_filters=num_filters_in,
                              conv_first=False)
             y = resnet_layer(inputs=y,
                              num_filters=num_filters_out,
                              kernel_size=1,
                              conv_first=False)
             if res_block == 0:
                 # linear projection residual shortcut connection to match
                 # changed dims
                 x = resnet_layer(inputs=x,
                                  num_filters=num_filters_out,
                                  kernel_size=1,
                                  strides=strides,
                                  activation=None,
                                  batch_normalization=False)
             x = keras.layers.add([x, y])
 
         num_filters_in = num_filters_out
 
     # Add classifier on top.
     # v2 has BN-ReLU before Pooling
     x = BatchNormalization()(x)
     x = Activation('relu')(x)
     x = AveragePooling2D(pool_size=8)(x)
     y = Flatten()(x)
     outputs = Dense(num_classes,
                     activation='softmax',
                     kernel_initializer='he_normal')(y)
 
     # Instantiate model.
     model = Model(inputs=inputs, outputs=outputs)
     return model

 if version == 2:
     model = resnet_v2(input_shape=input_shape, depth=depth)
 else:
     model = resnet_v1(input_shape=input_shape, depth=depth)
 
 model.compile(loss='categorical_crossentropy',
               optimizer=Adam(lr=lr_schedule(0)),
               metrics=['accuracy'])
 model.summary()
 print(model_type)
 
 # Prepare model model saving directory.
 save_dir = os.path.join(os.getcwd(), 'saved_models')
 model_name = 'cifar10_%s_model.{epoch:03d}.h5' % model_type
 if not os.path.isdir(save_dir):
     os.makedirs(save_dir)
 filepath = os.path.join(save_dir, model_name)  
 
 # Prepare callbacks for model saving and for learning rate adjustment.
 checkpoint = ModelCheckpoint(filepath=filepath,
                              monitor='val_acc',
                              verbose=1,
                              save_best_only=True)
 
 lr_scheduler = LearningRateScheduler(lr_schedule)
 
 lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),
                                cooldown=0,
                                patience=5,
                                min_lr=0.5e-6) 
 
 callbacks = [checkpoint, lr_reducer, lr_scheduler]
 
 # Run training, with or without data augmentation.
 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,
               callbacks=callbacks)
 else:
     print('Using real-time data augmentation.')
     # This will do preprocessing and realtime data augmentation:
     datagen = ImageDataGenerator(
         # set input mean to 0 over the dataset
         featurewise_center=False,
         # set each sample mean to 0
         samplewise_center=False,
         # divide inputs by std of dataset
         featurewise_std_normalization=False,
         # divide each input by its std
         samplewise_std_normalization=False,
         # apply ZCA whitening
         zca_whitening=False,
         # epsilon for ZCA whitening
         zca_epsilon=1e-06,
         # randomly rotate images in the range (deg 0 to 180)
         rotation_range=0,
         # randomly shift images horizontally
         width_shift_range=0.1,
         # randomly shift images vertically
         height_shift_range=0.1,
         # set range for random shear
         shear_range=0.,
         # set range for random zoom
         zoom_range=0.,
         # set range for random channel shifts
         channel_shift_range=0.,
         # set mode for filling points outside the input boundaries
         fill_mode='nearest',
         # value used for fill_mode = "constant"
         cval=0.,
         # randomly flip images
         horizontal_flip=True,
         # randomly flip images
         vertical_flip=False,
         # set rescaling factor (applied before any other transformation)
         rescale=None,
         # set function that will be applied on each input
         preprocessing_function=None,
         # image data format, either "channels_first" or "channels_last"
         data_format=None,
         # fraction of images reserved for validation (strictly between 0 and 1)
         validation_split=0.0) 
 
     # Compute quantities required for featurewise 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),
                         validation_data=(x_test, y_test),
                         epochs=epochs, verbose=1, workers=4,
                         callbacks=callbacks)
 
 # Score trained model.
 scores = model.evaluate(x_test, y_test, verbose=1)
 print('Test loss:', scores[0])
 print('Test accuracy:', scores[1])



==Pranala Menarik==

* [[Keras]]