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training3.py
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# coding: utf-8
# In[1]:
from sklearn.metrics import log_loss
import h5py
import pandas as pd
from keras.optimizers import SGD, Adagrad
from sklearn import cross_validation
import boto3
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from vgg16_model2 import VGG_16
from get_images_v2 import get_images
from read_drivers import create_training_test_lists
# In[ ]:
data_augmentation=True
# In[ ]:
def train_model(driver_imgs_list, width=224, height=224, channels=3, nb_epochs=1,
n_folds=3, path='./vgg16_weights.h5'):
subjects, label, trainList = create_training_test_lists(driver_imgs_list)
lkf = cross_validation.LabelKFold(subjects, n_folds=n_folds) # Instantiate Label K Fold iterator
lpl = cross_validation.LeavePLabelOut(subjects, p=1)
print('Loading model...')
model = VGG_16(width=width, height=height, channels=channels)
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
ada = Adagrad()
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
for i, (train_index, test_index) in enumerate(lpl):
print('Setting up training and test samples for fold #: ', i)
trainList_train, trainList_test = trainList[train_index], trainList[test_index]
label_train, label_test = label[train_index], label[test_index]
print('Getting X_train and Y_train for fold #: ', i)
X_train, Y_train = get_images(label=label_train, trainList=trainList_train, directory= './imgs/train/',
width=width, height=height, channels=channels )
print('Getting X_test and Y_test for fold #: ', i)
X_test, Y_test = get_images(label=label_test, trainList=trainList_test, directory= './imgs/train/',
width=width, height=height, channels=channels )
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
if X_train.max() > 1:
print('Dividing X_train by 255...')
X_train /= 255
if X_test.max() >1:
print('Dividing X_test by 255...')
X_test /= 255
if not data_augmentation:
model.fit(X_train, Y_train, validation_data=[X_test, Y_test], shuffle=True, verbose=1,
nb_epoch=nb_epochs, batch_size=16)
else:
print('Using real-time data augmentation...')
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=True, # set each sample mean to 0
featurewise_std_normalization=False, # 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,
width_shift_range=0,
height_shift_range=0,
horizontal_flip=False,
vertical_flip=False
)
datagen.fit(X_train)
model.fit_generator(datagen.flow(X_train, Y_train, batch_size=16),
samples_per_epoch=X_train.shape[0],
nb_epoch=nb_epochs, validation_data=(X_test, Y_test))
print('Saving model weights for model on fold: ', i)
model.save_weights('model_weights_vgg_fold_'+i*'.h5', overwrite=True)
print('Saving final model weights...')
model.save_weights('model_weights_vgg_trained.h5', overwrite=True)
print('Getting X and Y for final predictions...')
X, Y = get_images(label=label_train, trainList=trainList_train, directory= './imgs/train/',
width=width, height=height, channels=channels )
y_predicted = model.predict(X)
multi_logloss = log_loss(y, y_predicted)
return multi_logloss
multiclass_logloss = train_model(driver_imgs_list='driver_imgs_list.csv', width=224, height=224, channels=3,
nb_epochs=2) # Start iterative training, and return
# The logloss by img size
# In[ ]:
if __name__=='__main__':
get_ipython().system('jupyter nbconvert --to python training2.ipynb')
# In[ ]:
print('Logloss is: ', multiclass_logloss)
# In[ ]:
del X, y, X_train, X_test, Y_train, Y_test
# In[ ]:
#model = VGG_16(weights_path=('model_weights_vgg.h5')
#model.load_weights('model_weights_vgg.h5')
# In[ ]:
X_valid, imgLabels = get_dask_images(directory='./imgs/test/', width=width, height=height)
# In[ ]:
def create_prediction_matrix(model, X, target_classes, img_filenames):
predictions = model.predict(X)
classes = sorted(set(target_classes))
print('Shape of predictions output is: ', predictions.shape)
print('Shape of target_classes is: ', len(classes))
print('Shape of image filenames is: ', img_filenames)
predictions_df = pd.DataFrame(predictions, columns = classes, index = img_filenames)
predictions_df.index.name='img'
return predictions_df
predictions = create_prediction_matrix(model, X_valid, Y, imgLabels)
predictions.to_csv('submission3.csv')
# In[ ]:
s3 = boto3.resource
s3_client.upload_file('./submission3.csv', 'kaggle-competitions',
'StateFarmDistractedDriver/submission3.csv')
s3_client.upload_file('./model_weights_vgg_trained.h5', 'kaggle-competitions',
'StateFarmDistractedDriver/model_weights_vgg_trained.h5')
# In[ ]: