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SSRN_IN.py
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# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
from keras.models import Sequential, Model
from keras.layers import Convolution2D, MaxPooling2D, Conv3D, MaxPooling3D, ZeroPadding3D
from keras.layers import Activation, Dropout, Flatten, Dense, BatchNormalization, Input
from keras.utils.np_utils import to_categorical
from sklearn.decomposition import PCA
from keras.optimizers import Adam, SGD, Adadelta, RMSprop, Nadam
import keras.callbacks as kcallbacks
from keras.regularizers import l2
import time
import collections
from sklearn import metrics, preprocessing
from Utils import zeroPadding, normalization, doPCA, modelStatsRecord, averageAccuracy, ssrn_SS_IN
def indexToAssignment(index_, Row, Col, pad_length):
new_assign = {}
for counter, value in enumerate(index_):
assign_0 = value // Col + pad_length
assign_1 = value % Col + pad_length
new_assign[counter] = [assign_0, assign_1]
return new_assign
def assignmentToIndex( assign_0, assign_1, Row, Col):
new_index = assign_0 * Col + assign_1
return new_index
def selectNeighboringPatch(matrix, pos_row, pos_col, ex_len):
selected_rows = matrix[range(pos_row-ex_len,pos_row+ex_len+1), :]
selected_patch = selected_rows[:, range(pos_col-ex_len, pos_col+ex_len+1)]
return selected_patch
def sampling(proptionVal, groundTruth): #divide dataset into train and test datasets
labels_loc = {}
train = {}
test = {}
m = max(groundTruth)
for i in range(m):
indices = [j for j, x in enumerate(groundTruth.ravel().tolist()) if x == i + 1]
np.random.shuffle(indices)
labels_loc[i] = indices
nb_val = int(proptionVal * len(indices))
train[i] = indices[:-nb_val]
test[i] = indices[-nb_val:]
# whole_indices = []
train_indices = []
test_indices = []
for i in range(m):
# whole_indices += labels_loc[i]
train_indices += train[i]
test_indices += test[i]
np.random.shuffle(train_indices)
np.random.shuffle(test_indices)
return train_indices, test_indices
def res4_model_ss():
model_res4 = ssrn_SS_IN.ResnetBuilder.build_resnet_8((1, img_rows, img_cols, img_channels), nb_classes)
RMS = RMSprop(lr=0.0003)
# Let's train the model using RMSprop
model_res4.compile(loss='categorical_crossentropy', optimizer=RMS, metrics=['accuracy'])
return model_res4
mat_data = sio.loadmat('/home/zilong/SSRN/datasets/IN/Indian_pines_corrected.mat')
data_IN = mat_data['indian_pines_corrected']
mat_gt = sio.loadmat('/home/zilong/SSRN/datasets/IN/Indian_pines_gt.mat')
gt_IN = mat_gt['indian_pines_gt']
print (data_IN.shape)
#new_gt_IN = set_zeros(gt_IN, [1,4,7,9,13,15,16])
new_gt_IN = gt_IN
batch_size = 16
nb_classes = 16
nb_epoch = 200 #400
img_rows, img_cols = 7, 7 #27, 27
patience = 200
INPUT_DIMENSION_CONV = 200
INPUT_DIMENSION = 200
# 20%:10%:70% data for training, validation and testing
TOTAL_SIZE = 10249
VAL_SIZE = 1025
TRAIN_SIZE = 2055
TEST_SIZE = TOTAL_SIZE - TRAIN_SIZE
VALIDATION_SPLIT = 0.8 # 20% for trainnig and 80% for validation and testing
# TRAIN_NUM = 10
# TRAIN_SIZE = TRAIN_NUM * nb_classes
# TEST_SIZE = TOTAL_SIZE - TRAIN_SIZE
# VAL_SIZE = TRAIN_SIZE
img_channels = 200
PATCH_LENGTH = 3 #Patch_size (13*2+1)*(13*2+1)
data = data_IN.reshape(np.prod(data_IN.shape[:2]),np.prod(data_IN.shape[2:]))
gt = new_gt_IN.reshape(np.prod(new_gt_IN.shape[:2]),)
data = preprocessing.scale(data)
# scaler = preprocessing.MaxAbsScaler()
# data = scaler.fit_transform(data)
data_ = data.reshape(data_IN.shape[0], data_IN.shape[1],data_IN.shape[2])
whole_data = data_
padded_data = zeroPadding.zeroPadding_3D(whole_data, PATCH_LENGTH)
ITER = 1
CATEGORY = 16
train_data = np.zeros((TRAIN_SIZE, 2*PATCH_LENGTH + 1, 2*PATCH_LENGTH + 1, INPUT_DIMENSION_CONV))
test_data = np.zeros((TEST_SIZE, 2*PATCH_LENGTH + 1, 2*PATCH_LENGTH + 1, INPUT_DIMENSION_CONV))
KAPPA_RES_SS4 = []
OA_RES_SS4 = []
AA_RES_SS4 = []
TRAINING_TIME_RES_SS4 = []
TESTING_TIME_RES_SS4 = []
ELEMENT_ACC_RES_SS4 = np.zeros((ITER, CATEGORY))
#seeds = [1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229]
seeds = [1334]
for index_iter in xrange(ITER):
print("# %d Iteration" % (index_iter + 1))
# save the best validated model
best_weights_RES_path_ss4 = '/home/zilong/SSRN/models/Indian_best_RES_3D_SS4_10_' + str(
index_iter + 1) + '.hdf5'
np.random.seed(seeds[index_iter])
# train_indices, test_indices = sampleFixNum.samplingFixedNum(TRAIN_NUM, gt)
train_indices, test_indices = sampling(VALIDATION_SPLIT, gt)
# TRAIN_SIZE = len(train_indices)
# print (TRAIN_SIZE)
#
# TEST_SIZE = TOTAL_SIZE - TRAIN_SIZE - VAL_SIZE
# print (TEST_SIZE)
y_train = gt[train_indices] - 1
y_train = to_categorical(np.asarray(y_train))
y_test = gt[test_indices] - 1
y_test = to_categorical(np.asarray(y_test))
# print ("Validation data:")
# collections.Counter(y_test_raw[-VAL_SIZE:])
# print ("Testing data:")
# collections.Counter(y_test_raw[:-VAL_SIZE])
train_assign = indexToAssignment(train_indices, whole_data.shape[0], whole_data.shape[1], PATCH_LENGTH)
for i in range(len(train_assign)):
train_data[i] = selectNeighboringPatch(padded_data, train_assign[i][0], train_assign[i][1], PATCH_LENGTH)
test_assign = indexToAssignment(test_indices, whole_data.shape[0], whole_data.shape[1], PATCH_LENGTH)
for i in range(len(test_assign)):
test_data[i] = selectNeighboringPatch(padded_data, test_assign[i][0], test_assign[i][1], PATCH_LENGTH)
x_train = train_data.reshape(train_data.shape[0], train_data.shape[1], train_data.shape[2], INPUT_DIMENSION_CONV)
x_test_all = test_data.reshape(test_data.shape[0], test_data.shape[1], test_data.shape[2], INPUT_DIMENSION_CONV)
x_val = x_test_all[-VAL_SIZE:]
y_val = y_test[-VAL_SIZE:]
x_test = x_test_all[:-VAL_SIZE]
y_test = y_test[:-VAL_SIZE]
# SS Residual Network 4 with BN
model_res4_SS_BN = res4_model_ss()
earlyStopping6 = kcallbacks.EarlyStopping(monitor='val_loss', patience=patience, verbose=1, mode='auto')
saveBestModel6 = kcallbacks.ModelCheckpoint(best_weights_RES_path_ss4, monitor='val_loss', verbose=1,
save_best_only=True,
mode='auto')
tic6 = time.clock()
print(x_train.shape, x_test.shape)
history_res4_SS_BN = model_res4_SS_BN.fit(
x_train.reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2], x_train.shape[3], 1), y_train,
validation_data=(x_val.reshape(x_val.shape[0], x_val.shape[1], x_val.shape[2], x_val.shape[3], 1), y_val),
batch_size=batch_size,
nb_epoch=nb_epoch, shuffle=True, callbacks=[earlyStopping6, saveBestModel6])
toc6 = time.clock()
tic7 = time.clock()
loss_and_metrics_res4_SS_BN = model_res4_SS_BN.evaluate(
x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2], x_test.shape[3], 1), y_test,
batch_size=batch_size)
toc7 = time.clock()
print('3D RES_SS4 without BN Training Time: ', toc6 - tic6)
print('3D RES_SS4 without BN Test time:', toc7 - tic7)
print('3D RES_SS4 without BN Test score:', loss_and_metrics_res4_SS_BN[0])
print('3D RES_SS4 without BN Test accuracy:', loss_and_metrics_res4_SS_BN[1])
print(history_res4_SS_BN.history.keys())
pred_test_res4 = model_res4_SS_BN.predict(
x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2], x_test.shape[3], 1)).argmax(axis=1)
collections.Counter(pred_test_res4)
gt_test = gt[test_indices] - 1
overall_acc_res4 = metrics.accuracy_score(pred_test_res4, gt_test[:-VAL_SIZE])
confusion_matrix_res4 = metrics.confusion_matrix(pred_test_res4, gt_test[:-VAL_SIZE])
each_acc_res4, average_acc_res4 = averageAccuracy.AA_andEachClassAccuracy(confusion_matrix_res4)
kappa = metrics.cohen_kappa_score(pred_test_res4, gt_test[:-VAL_SIZE])
KAPPA_RES_SS4.append(kappa)
OA_RES_SS4.append(overall_acc_res4)
AA_RES_SS4.append(average_acc_res4)
TRAINING_TIME_RES_SS4.append(toc6 - tic6)
TESTING_TIME_RES_SS4.append(toc7 - tic7)
ELEMENT_ACC_RES_SS4[index_iter, :] = each_acc_res4
print("3D RESNET_SS4 without BN training finished.")
print("# %d Iteration" % (index_iter + 1))
modelStatsRecord.outputStats(KAPPA_RES_SS4, OA_RES_SS4, AA_RES_SS4, ELEMENT_ACC_RES_SS4,
TRAINING_TIME_RES_SS4, TESTING_TIME_RES_SS4,
history_res4_SS_BN, loss_and_metrics_res4_SS_BN, CATEGORY,
'/home/zilong/SSRN/records/IN_train_SS_10.txt',
'/home/zilong/SSRN/records/IN_train_SS_element_10.txt')