forked from sdierauf/cifar-classifier
-
Notifications
You must be signed in to change notification settings - Fork 5
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
1a0cdab
commit 03bee17
Showing
2 changed files
with
194 additions
and
150 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -65,4 +65,6 @@ target/ | |
| #Ipython Notebook | ||
| .ipynb_checkpoints | ||
|
|
||
| tiny-imagenet-200/ | ||
| tiny-imagenet-200 | ||
| datagen/ | ||
| *.h5 | ||
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,149 +1,191 @@ | ||
| '''Trains a convnet on the tiny imagenet dataset | ||
| ''' | ||
|
|
||
| # System | ||
| import numpy as np | ||
| from PIL import Image | ||
| np.random.seed(1337) # for reproducibility | ||
|
|
||
| #Keras | ||
| from keras.datasets import mnist | ||
| from keras.models import Sequential | ||
| from keras.datasets import cifar10 | ||
| from keras.regularizers import WeightRegularizer, ActivityRegularizer | ||
| from keras.layers.core import Dense, Dropout, Activation, Flatten | ||
| from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D, AveragePooling2D | ||
| from keras.layers.normalization import BatchNormalization | ||
| from keras.utils import np_utils | ||
| from keras.optimizers import SGD | ||
| from keras.preprocessing.image import ImageDataGenerator | ||
| from plotter import Plotter | ||
| # from keras.utils.visualize_util import plot | ||
| import h5py | ||
|
|
||
| #Custom | ||
| from load_images import load_images | ||
|
|
||
| #Params | ||
| loss_functions = ['hinge', 'squared_hinge','categorical_crossentropy'] | ||
| # loss_functions = ['categorical_crossentropy'] | ||
| num_classes = 20 | ||
| batch_size = num_classes | ||
| nb_epoch = 100 | ||
|
|
||
| #Load images | ||
| path='./tiny-imagenet-200' | ||
| X_train,y_train,X_test,y_test=load_images(path,num_classes) | ||
|
|
||
| print('X_train shape:', X_train.shape) | ||
| print(X_train.shape[0], 'train samples') | ||
| print(X_test.shape[0], 'test samples') | ||
|
|
||
| # num_samples=round(len(X_train)*0.8) | ||
| num_samples=len(X_train) | ||
|
|
||
| # input image dimensions | ||
| num_channels , img_rows, img_cols = X_train.shape[1], X_train.shape[2], X_train.shape[3] | ||
|
|
||
| X_train = X_train.astype('float32') | ||
| X_test = X_test.astype('float32') | ||
| X_train /= 255 | ||
| X_test /= 255 | ||
|
|
||
| # convert class vectors to binary class matrices | ||
| Y_train = np_utils.to_categorical(y_train, num_classes) | ||
| Y_test = np_utils.to_categorical(y_test, num_classes) | ||
|
|
||
| for loss_function in loss_functions: | ||
| # for num_classes in num_classes_arr: # num classes loop | ||
|
|
||
| #Define model | ||
| model = Sequential() | ||
| # input: 64x64 images with 3 channels -> (3, 64, 64) tensors. | ||
| # this applies 32 convolution filters of size 3x3 each. | ||
| model.add(Convolution2D(64, 5, 5, border_mode='valid', input_shape=(num_channels,img_rows,img_cols), init='glorot_uniform')) | ||
| model.add(Activation('relu')) | ||
| # model.add(Convolution2D(32, 3, 3, init='glorot_uniform')) | ||
| # model.add(Activation('relu')) | ||
| model.add(MaxPooling2D(pool_size=(2, 2))) | ||
| model.add(Dropout(0.25)) | ||
|
|
||
| model.add(Convolution2D(64, 5, 5, border_mode='valid', init='glorot_uniform')) | ||
| model.add(Activation('relu')) | ||
| # model.add(Convolution2D(64, 3, 3, init='glorot_uniform')) | ||
| # model.add(Activation('relu')) | ||
| model.add(MaxPooling2D(pool_size=(2, 2))) | ||
| model.add(Dropout(0.5)) | ||
|
|
||
| model.add(Convolution2D(128, 5, 5, border_mode='valid', init='glorot_uniform')) | ||
| model.add(Activation('relu')) | ||
| # model.add(Convolution2D(64, 3, 3, init='glorot_uniform')) | ||
| # model.add(Activation('relu')) | ||
| model.add(MaxPooling2D(pool_size=(2, 2))) | ||
| model.add(Dropout(0.5)) | ||
|
|
||
| model.add(Flatten()) | ||
| model.add(Dense(256,W_regularizer=WeightRegularizer(l1=1e-6,l2=1e-6), init='glorot_uniform')) | ||
| model.add(Activation('relu')) | ||
| model.add(Dropout(0.5)) | ||
|
|
||
| #SGD params | ||
| sgd = SGD(lr=0.1, decay=1e-4, momentum=0.9, nesterov=True) | ||
|
|
||
| print() | ||
| print() | ||
| print('===========================') | ||
| print('Testing: ' + loss_function + ' with ' + str(num_classes) + ' classes') | ||
| print('===========================') | ||
| print() | ||
|
|
||
| if loss_function=='categorical_crossentropy': | ||
| #affine layer w/ softmax activation added | ||
| model.add(Dense(num_classes,activation='softmax',W_regularizer=WeightRegularizer(l1=1e-5,l2=1e-5), init='glorot_uniform')) | ||
| else: | ||
| model.add(Dense(num_classes,W_regularizer=WeightRegularizer(l1=1e-5,l2=1e-5), init='glorot_uniform')) | ||
|
|
||
| model.summary() | ||
| # plot(model, to_file='model_'+loss_function+'_.png') | ||
|
|
||
| model.compile(loss=loss_function, | ||
| optimizer=sgd, | ||
| metrics=['accuracy']) | ||
|
|
||
|
|
||
| datagen = ImageDataGenerator( | ||
| featurewise_center=True, # set input mean to 0 over the dataset | ||
| samplewise_center=False, # set each sample mean to 0 | ||
| featurewise_std_normalization=True, # 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=10., # 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 | ||
|
|
||
|
|
||
| fpath = 'loss-' + loss_function + str(num_classes) | ||
| datagen.fit(X_train) | ||
|
|
||
| # df=datagen.flow(X_train, Y_train, batch_size=batch_size, shuffle=True) | ||
|
|
||
| model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size, shuffle=True),# save_to_dir='./datagen/', save_prefix='datagen-',save_format='png'), # To save the images created by the generator | ||
| samples_per_epoch=num_samples, nb_epoch=nb_epoch, | ||
| verbose=1, validation_data=(X_test,Y_test), #df,nb_val_samples=len(X_train)-num_samples, | ||
| callbacks=[Plotter(show_regressions=False, save_to_filepath=fpath, show_plot_window=False)]) | ||
|
|
||
| # model.fit(X_train, Y_train, batch_size=64, nb_epoch=nb_epoch, | ||
| # verbose=1, validation_data=(X_test, Y_test), | ||
| # callbacks=[Plotter(show_regressions=False, save_to_filepath=fpath, show_plot_window=False)]) | ||
|
|
||
|
|
||
|
|
||
| score = model.evaluate(X_test, Y_test, verbose=1) | ||
| print('Test score:', score[0]) | ||
| print('Test accuracy:', score[1]) | ||
|
|
||
| pathWeights='model'+loss_function+'.h5' | ||
| model.save_weights(pathWeights) | ||
| '''Trains a convnet on the tiny imagenet dataset | ||
| ''' | ||
|
|
||
| # System | ||
| import numpy as np | ||
| from PIL import Image | ||
| np.random.seed(1337) # for reproducibility | ||
|
|
||
| #Keras | ||
| from keras.datasets import mnist | ||
| from keras.models import Sequential | ||
| from keras.datasets import cifar10 | ||
| from keras.regularizers import WeightRegularizer, ActivityRegularizer | ||
| from keras.layers.core import Dense, Dropout, Activation, Flatten | ||
| from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D, AveragePooling2D | ||
| from keras.layers.normalization import BatchNormalization | ||
| from keras.utils import np_utils | ||
| from keras.optimizers import SGD | ||
| from keras.preprocessing.image import ImageDataGenerator | ||
| from plotter import Plotter | ||
| # from keras.utils.visualize_util import plot | ||
| import h5py | ||
|
|
||
| #Custom | ||
| from load_images import load_images | ||
|
|
||
| #Params | ||
| loss_functions = ['hinge', 'squared_hinge','categorical_crossentropy'] | ||
| # loss_functions = ['categorical_crossentropy'] | ||
| num_classes = 20 | ||
| batch_size = 64 | ||
| nb_epoch = 100 | ||
|
|
||
| #Load images | ||
| path='./tiny-imagenet-200' | ||
| X_train,y_train,X_test,y_test=load_images(path,num_classes) | ||
|
|
||
| print('X_train shape:', X_train.shape) | ||
| print(X_train.shape[0], 'train samples') | ||
| print(X_test.shape[0], 'test samples') | ||
|
|
||
| # num_samples=round(len(X_train)*0.8) | ||
| num_samples=len(X_train) | ||
|
|
||
| # input image dimensions | ||
| num_channels , img_rows, img_cols = X_train.shape[1], X_train.shape[2], X_train.shape[3] | ||
|
|
||
| X_train = X_train.astype('float32') | ||
| X_test = X_test.astype('float32') | ||
| X_train /= 255 | ||
| X_test /= 255 | ||
|
|
||
| # convert class vectors to binary class matrices | ||
| Y_train = np_utils.to_categorical(y_train, num_classes) | ||
| Y_test = np_utils.to_categorical(y_test, num_classes) | ||
|
|
||
| for loss_function in loss_functions: | ||
| # for num_classes in num_classes_arr: # num classes loop | ||
|
|
||
| model = Sequential() | ||
| #conv-spatial batch norm - relu #1 | ||
| model.add(ZeroPadding2D((2,2),input_shape=(3,64,64))) | ||
| model.add(Convolution2D(64,5,5,subsample=(2,2),W_regularizer=WeightRegularizer(l1=1e-5,l2=1e-5))) | ||
| model.add(BatchNormalization(epsilon=1e-06, mode=0, axis=1, momentum=0.9)) | ||
| model.add(Activation('relu')) | ||
|
|
||
| #conv-spatial batch norm - relu #2 | ||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(64,3,3,subsample=(1,1))) | ||
| model.add(BatchNormalization(epsilon=1e-06, mode=0, axis=1, momentum=0.9)) | ||
| model.add(Activation('relu')) | ||
|
|
||
| #conv-spatial batch norm - relu #3 | ||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(128,3,3,subsample=(2,2))) | ||
| model.add(BatchNormalization(epsilon=1e-06, mode=0, axis=1, momentum=0.9)) | ||
| model.add(Activation('relu')) | ||
| model.add(Dropout(0.25)) | ||
|
|
||
| #conv-spatial batch norm - relu #4 | ||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(128,3,3,subsample=(1,1))) | ||
| model.add(BatchNormalization(epsilon=1e-06, mode=0, axis=1, momentum=0.9)) | ||
| model.add(Activation('relu')) | ||
|
|
||
| #conv-spatial batch norm - relu #5 | ||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(256,3,3,subsample=(2,2))) | ||
| model.add(BatchNormalization(epsilon=1e-06, mode=0, axis=1, momentum=0.9)) | ||
| model.add(Activation('relu')) | ||
|
|
||
| #conv-spatial batch norm - relu #6 | ||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(256,3,3,subsample=(1,1))) | ||
| model.add(BatchNormalization(epsilon=1e-06, mode=0, axis=1, momentum=0.9)) | ||
| model.add(Activation('relu')) | ||
| model.add(Dropout(0.25)) | ||
|
|
||
| #conv-spatial batch norm - relu #7 | ||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(512,3,3,subsample=(2,2))) | ||
| model.add(BatchNormalization(epsilon=1e-06, mode=0, axis=1, momentum=0.9)) | ||
| model.add(Activation('relu')) | ||
|
|
||
| #conv-spatial batch norm - relu #8 | ||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(512,3,3,subsample=(1,1))) | ||
| model.add(BatchNormalization(epsilon=1e-06, mode=0, axis=1, momentum=0.9)) | ||
| model.add(Activation('relu')) | ||
|
|
||
|
|
||
| #conv-spatial batch norm - relu #9 | ||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(1024,3,3,subsample=(2,2))) | ||
| model.add(BatchNormalization(epsilon=1e-06, mode=0, axis=1, momentum=0.9)) | ||
| model.add(Activation('relu')) | ||
| model.add(Dropout(0.25)) | ||
|
|
||
| #Affine-spatial batch norm -relu #10 | ||
| model.add(Flatten()) | ||
| model.add(Dense(512,W_regularizer=WeightRegularizer(l1=1e-4,l2=1e-4))) | ||
| model.add(BatchNormalization(epsilon=1e-06, mode=0, axis=1, momentum=0.9)) | ||
| model.add(Activation('relu')) | ||
| model.add(Dropout(0.5)) | ||
|
|
||
| sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True) | ||
|
|
||
| model.compile(loss=loss_function, | ||
| optimizer=sgd, | ||
| metrics=['accuracy']) | ||
|
|
||
| print() | ||
| print() | ||
| print('===========================') | ||
| print('Testing: ' + loss_function + ' with ' + str(num_classes) + ' classes') | ||
| print('===========================') | ||
| print() | ||
|
|
||
| if loss_function=='categorical_crossentropy': | ||
| #affine layer w/ softmax activation added | ||
| model.add(Dense(num_classes,activation='softmax',W_regularizer=WeightRegularizer(l1=1e-5,l2=1e-5))) | ||
| else: | ||
| if loss_function=='hinge': | ||
| #SGD params | ||
| sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) | ||
| model.add(Dense(num_classes,W_regularizer=WeightRegularizer(l1=1e-5,l2=1e-5), init='glorot_uniform')) | ||
|
|
||
| model.summary() | ||
| # plot(model, to_file='model_'+loss_function+'_.png') | ||
|
|
||
| model.compile(loss=loss_function, | ||
| optimizer=sgd, | ||
| metrics=['accuracy']) | ||
|
|
||
|
|
||
| datagen = ImageDataGenerator( | ||
| featurewise_center=True, # set input mean to 0 over the dataset | ||
| samplewise_center=False, # set each sample mean to 0 | ||
| featurewise_std_normalization=True, # 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=10., # 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 | ||
|
|
||
|
|
||
| fpath = 'loss-' + loss_function + '-' + str(num_classes) | ||
| datagen.fit(X_train) | ||
|
|
||
| # df=datagen.flow(X_train, Y_train, batch_size=batch_size, shuffle=True) | ||
|
|
||
| model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size, shuffle=True, save_to_dir='./datagen/', save_prefix='datagen-',save_format='png'), # To save the images created by the generator | ||
| samples_per_epoch=num_samples, nb_epoch=nb_epoch, | ||
| verbose=1, validation_data=(X_test,Y_test), #df,nb_val_samples=len(X_train)-num_samples, | ||
| callbacks=[Plotter(show_regressions=False, save_to_filepath=fpath, show_plot_window=False)]) | ||
|
|
||
| # model.fit(X_train, Y_train, batch_size=64, nb_epoch=nb_epoch, | ||
| # verbose=1, validation_data=(X_test, Y_test), | ||
| # callbacks=[Plotter(show_regressions=False, save_to_filepath=fpath, show_plot_window=False)]) | ||
|
|
||
|
|
||
|
|
||
| score = model.evaluate(X_test, Y_test, verbose=1) | ||
| print('Test score:', score[0]) | ||
| print('Test accuracy:', score[1]) | ||
|
|
||
| pathWeights='model'+loss_function+'.h5' | ||
| model.save_weights(pathWeights) |