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run_cnn_k_mymil_new.py
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from __future__ import print_function
import inbreast
import keras.backend as K
from roc_auc import RocAucScoreOp, PrecisionOp, RecallOp, F1Op
from roc_auc import AUCEpoch, PrecisionEpoch, RecallEpoch, F1Epoch, LossEpoch, ACCEpoch
#from keras.preprocessing.image import ImageDataGenerator
from image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization, SpatialDropout2D
from keras.layers import advanced_activations
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import SGD, Adam, RMSprop
from keras.utils import np_utils
import numpy as np
from keras.callbacks import ModelCheckpoint
from keras.regularizers import l1l2
import inbreast
#import googlenet
from convnetskeras.convnets import preprocess_image_batch, convnet
np.random.seed(1)
#srng = RandomStreams(1)
fold = 0# 4
valfold = 2
lr = 5e-5
nb_epoch = 500
batch_size = 80
l2factor = 1e-5
l1factor = 0#2e-7
usedream = False
weighted = False #True
noises = 50
data_augmentation = True
modelname = 'alexnet' # miccai16, alexnet, levynet, googlenet
pretrain = True
mymil=True
mymilk = 1
savename = modelname+'new_fd'+str(fold)+'_vf'+str(valfold)+'_lr'+str(lr)+'_l2'+str(l2factor)+'_l1'\
+str(l1factor)+'_ep'+str(nb_epoch)+'_bs'+str(batch_size)+'_w'+str(weighted)+'_dr'+str(usedream)+str(noises)+str(pretrain)+'_mymil'+str(mymil)+str(mymilk)
print(savename)
nb_classes = 2
# input image dimensions
img_rows, img_cols = 227, 227
# the CIFAR10 images are RGB
img_channels = 1
# the data, shuffled and split between train and test sets
trX, y_train, teX, y_test, teteX, y_test_test = inbreast.loaddataenhance(fold, 5, valfold=valfold)
trY = y_train.reshape((y_train.shape[0],1))
teY = y_test.reshape((y_test.shape[0],1))
teteY = y_test_test.reshape((y_test_test.shape[0],1))
print('tr, val, te pos num and shape')
print(trY.sum(), teY.sum(), teteY.sum(), trY.shape[0], teY.shape[0], teteY.shape[0])
ratio = trY.sum()*1./trY.shape[0]*1.
print('tr ratio'+str(ratio))
weights = np.array((ratio, 1-ratio))
#trYori = np.concatenate((1-trY, trY), axis=1)
#teY = np.concatenate((1-teY, teY), axis=1)
#teteY = np.concatenate((1-teteY, teteY), axis=1)
X_train = trX.reshape(-1, img_channels, img_rows, img_cols)
X_test = teX.reshape(-1, img_channels, img_rows, img_cols)
X_test_test = teteX.reshape(-1, img_channels, img_rows, img_cols)
print('tr, val, te mean, std')
print(X_train.mean(), X_test.mean(), X_test_test.mean())
# convert class vectors to binary class matrices
Y_train = np.zeros((y_train.shape[0], 6*6))
Y_test = np.zeros((y_test.shape[0], 6*6))
Y_test_test = np.zeros((y_test_test.shape[0], 6*6))
for i in xrange(6*6-mymilk,6*6):
#Y_train[:,i,0] = 1-y_train
Y_train[:,i] = y_train
#Y_test[:,i,0] = 1-y_test
Y_test[:,i] = y_test
#Y_test_test[:,i,0] = 1-y_test_test
Y_test_test[:,i] = y_test_test
for i in xrange(6*6-mymilk):
#Y_train[:,i,0] = np.zeros_like(y_train)
Y_train[:,i] = np.zeros_like(y_train)
#Y_test[:,i,0] = np.zeros_like(y_test)
Y_test[:,i] = np.zeros_like(y_test)
#Y_test_test[:,i,0] = np.zeros_like(y_test_test)
Y_test_test[:,i] = np.zeros_like(y_test_test)
#ratio = Y_train[:,:,1].sum()*1./(trY.shape[0]*6*6)*1.
ratio = Y_train[:,:].sum()*1./(trY.shape[0]*6*6)*1.
weights = np.array((ratio, 1-ratio))
print('tr my mil ratio'+str(ratio))
#Y_train = np_utils.to_categorical(y_train, nb_classes)
#Y_test = np_utils.to_categorical(y_test, nb_classes)
#Y_test_test = np_utils.to_categorical(y_test_test, nb_classes)
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'val samples')
print(X_test_test.shape[0], 'test samples')
model = Sequential()
if modelname == 'alexnet':
X_train_extend = np.zeros((X_train.shape[0],3, 227, 227))
for i in xrange(X_train.shape[0]):
rex = np.resize(X_train[i,:,:,:], (227, 227))
X_train_extend[i,0,:,:] = rex
X_train_extend[i,1,:,:] = rex
X_train_extend[i,2,:,:] = rex
X_train = X_train_extend
X_test_extend = np.zeros((X_test.shape[0], 3,227, 227))
for i in xrange(X_test.shape[0]):
rex = np.resize(X_test[i,:,:,:], (227, 227))
X_test_extend[i,0,:,:] = rex
X_test_extend[i,1,:,:] = rex
X_test_extend[i,2,:,:] = rex
X_test = X_test_extend
X_test_test_extend = np.zeros((X_test_test.shape[0], 3, 227, 227))
for i in xrange(X_test_test.shape[0]):
rex = np.resize(X_test_test[i,:,:,:], (227,227))
X_test_test_extend[i,0,:,:] = rex
X_test_test_extend[i,1,:,:] = rex
X_test_test_extend[i,2,:,:] = rex
X_test_test = X_test_test_extend
if pretrain: # 227*227
alexmodel = convnet('alexnet', weights_path='alexnet_weights.h5', heatmap=False, l1=l1factor, l2=l2factor)
model = convnet('alexnet', outdim=2, l1=l1factor, l2=l2factor, usemymil=mymil, k=mymilk)
for layer, mylayer in zip(alexmodel.layers, model.layers):
print(layer.name, mylayer.name)
if mylayer.name == 'mil_1':
break
else:
weightsval = layer.get_weights()
print(len(weightsval))
mylayer.set_weights(weightsval)
else:
model = convnet('alexnet', outdim=2, l1=l1factor,l2=l2factor, usemymil=mymil, k=mymilk)
# let's train the model using SGD + momentum (how original).
sgd = Adam(lr=lr) #SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy', #categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])#, AUCEpoch,PrecisionEpoch,RecallEpoch,F1Epoch])
print(model.summary())
#filepath = savename+'-{epoch:02d}-{val_loss:.2f}-{val_acc:.2f}.hdf5' #-{val_auc:.2f}-\
#{val_prec:.2f}-{val_reca:.2f}-{val_f1:.2f}.hdf5'
#checkpoint0 = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='max')
#checkpoint1 = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
checkpoint0 = LossEpoch(savename, validation_data=(X_test, Y_test), interval=1, mymil=mymil)
checkpoint1 = ACCEpoch(savename, validation_data=(X_test, Y_test), interval=1, mymil=mymil)
checkpoint2 = AUCEpoch(savename, validation_data=(X_test, Y_test), interval=1, mymil=mymil)
checkpoint3 = PrecisionEpoch(savename, validation_data=(X_test, Y_test), interval=1, mymil=mymil)
checkpoint4 = RecallEpoch(savename, validation_data=(X_test, Y_test), interval=1, mymil=mymil)
checkpoint5 = F1Epoch(savename, validation_data=(X_test, Y_test), interval=1, mymil=mymil)
#checkpoint2 = ModelCheckpoint(filepath, monitor='val_auc', verbose=1, save_best_only=True, mode='max')
#checkpoint3 = ModelCheckpoint(filepath, monitor='val_prec', verbose=1, save_best_only=True, mode='max')
#checkpoint4 = ModelCheckpoint(filepath, monitor='val_reca', verbose=1, save_best_only=True, mode='max')
#checkpoint5 = ModelCheckpoint(filepath, monitor='val_f1', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint0, checkpoint1, checkpoint2, checkpoint3, checkpoint4, checkpoint5]
#callbacks_list = [AUCEpoch, PrecisionEpoch, RecallEpoch, F1Epoch, checkpoint0, checkpoint1]
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,
nb_epoch=nb_epoch,
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=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=45.0, # 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,
zerosquare=True,
zerosquareh=noises,
zerosquarew=noises,
zerosquareintern=0.0) # randomly flip images
# 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()
if weighted:
model.fit_generator(datagen.flow(X_train, Y_train,
batch_size=batch_size),
samples_per_epoch=X_train.shape[0],
nb_epoch=nb_epoch,
validation_data=(X_test, Y_test),
callbacks=callbacks_list,
class_weight=[weights[0], weights[1]])
else:
#print(Y_train.shape)
model.fit_generator(datagen.flow(X_train, Y_train,
batch_size=batch_size),
samples_per_epoch=X_train.shape[0],
nb_epoch=nb_epoch,
validation_data=(X_test, Y_test),
callbacks=callbacks_list)