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classification_cnn_LAG.py
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classification_cnn_LAG.py
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import os
import glob
import cv2
import datetime
import numpy as np
import pandas as pd
import time
import warnings
from sklearn.cross_validation import KFold
from sklearn.metrics import log_loss
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.optimizers import SGD
from keras.callbacks import EarlyStopping
from keras.utils import np_utils
class LAG_CNN():
def __init__(self):
# Read train data
train_data = []
train_id = []
train_target = []
start_time = time.time()
print('Read train images')
folders = ['DOL', 'other']
for fld in folders:
index = folders.index(fld)
print('Load folder {} (Index: {})'.format(fld, index))
path = os.path.join('/Users/Kevin/Desktop/fish_model/red_crop_train', fld, '*.jpg')
files = glob.glob(path)
for fl in files:
flbase = os.path.basename(fl)
img = get_im_cv2(fl)
train_data.append(img)
train_id.append(flbase)
train_target.append(index)
print('Read train data time: {} seconds'.format(round(time.time() - start_time, 2)))
# Normalize train data
print('Convert to numpy...')
train_data = np.array(train_data, dtype=np.uint8)
train_target = np.array(train_target, dtype=np.uint8)
print('Reshape...')
train_data = train_data.transpose((0, 3, 1, 2))
print('Convert to float...')
train_data = train_data.astype('float32')
train_data = train_data / 255
train_target = np_utils.to_categorical(train_target, 8)
print('Train shape:', train_data.shape)
print(train_data.shape[0], 'train samples')
# CNN Model Building
model = Sequential()
model.add(ZeroPadding2D((1, 1), input_shape=(3, 48, 48), dim_ordering='th'))
model.add(Convolution2D(4, 3, 3, activation='relu', dim_ordering='th'))
model.add(ZeroPadding2D((1, 1), dim_ordering='th'))
model.add(Convolution2D(4, 3, 3, activation='relu', dim_ordering='th'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), dim_ordering='th'))
model.add(ZeroPadding2D((1, 1), dim_ordering='th'))
model.add(Convolution2D(8, 3, 3, activation='relu', dim_ordering='th'))
model.add(ZeroPadding2D((1, 1), dim_ordering='th'))
model.add(Convolution2D(8, 3, 3, activation='relu', dim_ordering='th'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), dim_ordering='th'))
model.add(Flatten())
model.add(Dense(96, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(16, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
sgd = SGD(lr=1e-2, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy')
# CNN Training
nfolds = 20
batch_size = 16
nb_epoch = 30
random_state = 51
yfull_train = dict()
kf = KFold(len(train_id), n_folds=nfolds, shuffle=True, random_state=random_state)
num_fold = 0
sum_score = 0
models = []
for train_index, test_index in kf:
X_train = train_data[train_index]
Y_train = train_target[train_index]
X_valid = train_data[test_index]
Y_valid = train_target[test_index]
num_fold += 1
print('Start KFold number {} from {}'.format(num_fold, nfolds))
print('Split train: ', len(X_train), len(Y_train))
print('Split valid: ', len(X_valid), len(Y_valid))
callbacks = [EarlyStopping(monitor='val_loss', patience=3, verbose=0),]
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
shuffle=True, verbose=2, validation_data=(X_valid, Y_valid),
callbacks=callbacks)
predictions_valid = model.predict(X_valid.astype('float32'), batch_size=batch_size, verbose=2)
score = log_loss(Y_valid, predictions_valid)
print('Score log_loss: ', score)
sum_score += score * len(test_index)
# Store valid predictions
for i in range(len(test_index)):
yfull_train[test_index[i]] = predictions_valid[i]
models.append(model)
score = sum_score/len(train_data)
print("Log_loss train independent avg: ", score)
info_string = 'loss_' + str(score) + '_folds_' + str(nfolds) + '_ep_' + str(nb_epoch)
# Read test data
path = os.path.join('/Users/Kevin/Desktop/fish_model/red_crop_test', '*.jpg')
files = sorted(glob.glob(path))
test_data = []
test_id = []
for fl in files:
flbase = os.path.basename(fl)
img = get_im_cv2(fl)
test_data.append(img)
test_id.append(flbase)
# Nomilize test data
start_time = time.time()
test_data, test_id = load_test()
test_data = np.array(test_data, dtype=np.uint8)
test_data = test_data.transpose((0, 3, 1, 2))
test_data = test_data.astype('float32')
test_data = test_data / 255
print('Test shape:', test_data.shape)
print(test_data.shape[0], 'test samples')
print('Read and process test data time: {} seconds'.format(round(time.time() - start_time, 2)))
# CNN Prediction
batch_size = 16
num_fold = 0
yfull_test = []
test_id = []
nfolds = len(models)
for i in range(nfolds):
model = models[i]
num_fold += 1
print('Start KFold number {} from {}'.format(num_fold, nfolds))
test_prediction = model.predict(test_data, batch_size=batch_size, verbose=2)
yfull_test.append(test_prediction)
test_res = merge_several_folds_mean(yfull_test, nfolds)
info_string = 'loss_' + info_string \
+ '_folds_' + str(nfolds)
create_submission(test_res, test_id, info_string)
def get_im_cv2(self,path):
img = cv2.imread(path)
resized = cv2.resize(img, (48, 48), cv2.INTER_LINEAR)
return resized
def create_submission(self, predictions, test_id, info):
result1 = pd.DataFrame(predictions, columns=['LAG', 'other'])
result1.loc[:, 'image'] = pd.Series(test_id, index=result1.index)
now = datetime.datetime.now()
sub_file = 'submission_' + info + '_' + str(now.strftime("%Y-%m-%d-%H-%M")) + '.csv'
result1.to_csv(sub_file, index=False)
def dict_to_list(self, d):
ret = []
for i in d.items():
ret.append(i[1])
return ret
def merge_several_folds_mean(self, data, nfolds):
a = np.array(data[0])
for i in range(1, nfolds):
a += np.array(data[i])
a /= nfolds
return a.tolist()
def get_validation_predictions(self, train_data, predictions_valid):
pv = []
for i in range(len(train_data)):
pv.append(predictions_valid[i])
return pv
if __name__=='__main__':
LAG_CNN()