Trains a ResNet on the CIFAR10 small images dataset
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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 %| - # # 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])