def MCC_CM_calculator(validation_labels_linear, predicted_labels_linear):
#Return MCC and confusion matrix
MCC = multimcc(validation_labels_linear, predicted_labels_linear)
MCC = round(MCC,3)
MCC_line = "MCC=" + str(MCC)
CM = confusion_matrix(validation_labels_linear, predicted_labels_linear)
CM_lines = ";p_E;p_G;p_L\n"
for i in range(len(CM[0])):
if i == 0:
l = "r_E"
elif i == 1:
l = "r_G"
elif i == 2:
l = "r_L"
CM_lines += l + ";"
for j in CM[0][i]:
CM_lines += str(j) + ";"
CM_lines += "\n"
return MCC_line, CM_lines
def MCC_CM_calculator(validation_labels_linear, predicted_labels_linear):
#Return MCC and confusion matrix
MCC = multimcc(validation_labels_linear, predicted_labels_linear)
MCC = round(MCC, 3)
MCC_line = "MCC=" + str(MCC)
CM = confusion_matrix(validation_labels_linear, predicted_labels_linear)
CM_lines = ";p_E;p_G;p_L\n"
for i in range(len(CM[0])):
if i == 0:
l = "r_E"
elif i == 1:
l = "r_G"
elif i == 2:
l = "r_L"
CM_lines += l + ";"
for j in CM[0][i]:
CM_lines += str(j) + ";"
CM_lines += "\n"
return MCC_line, CM_lines
def MCC_CM_calculator(validation_labels_linear, predicted_labels_linear):
'''Return MCC and confusion matrix'''
#print(len(validation_labels_linear), validation_labels_linear.shape)
#print(len(predicted_labels_linear), predicted_labels_linear.shape)
MCC = multimcc(validation_labels_linear, predicted_labels_linear)
MCC = round(MCC,3)
MCC_line = "MCC=" + str(MCC) + "\n"
CM = confusion_matrix(validation_labels_linear, predicted_labels_linear)
CM_lines = ""
for i in range(len(CM)):
for j in CM[i]:
CM_lines += str(j) + ";"
CM_lines += "\n"
return MCC_line, CM_lines
def MCC_CM_calculator(validation_labels_linear, predicted_labels_linear):
'''Return MCC and confusion matrix'''
#print(len(validation_labels_linear), validation_labels_linear.shape)
#print(len(predicted_labels_linear), predicted_labels_linear.shape)
MCC = multimcc(validation_labels_linear, predicted_labels_linear)
MCC = round(MCC, 3)
MCC_line = "MCC=" + str(MCC) + "\n"
CM = confusion_matrix(validation_labels_linear, predicted_labels_linear)
CM_lines = ""
for i in range(len(CM)):
for j in CM[i]:
CM_lines += str(j) + ";"
CM_lines += "\n"
return MCC_line, CM_lines
def main():
WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5'
WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'
parser = myArgumentParser(description='Run a prediction experiment using pretrained VGG16, specified on the deepstreet DataSet.',
fromfile_prefix_chars='@')
parser.add_argument('--gpu', type=int, default=0, help='GPU Device (default: %(default)s)')
parser.add_argument('--output_dir', type=str, default="./experiment_output/",help='Output directory')
parser.add_argument('--input_dir', type=str, default="./",help='Input directory')
parser.add_argument('--debug', type=bool, default=False, help='Debug mode')
args = parser.parse_args()
GPU = args.gpu
OUTDIR = args.output_dir+"/"
INDIR = args.input_dir+"/"
DEBUG = args.debug
if not os.path.exists(OUTDIR):
os.makedirs(OUTDIR)
if DEBUG:
validation_data_dir = INDIR + "small_dataset/val/"
else:
#validation_data_dir = "dataset/val/"
validation_data_dir = INDIR + "val/"
if os.path.exists(INDIR + validation_data_dir + ".DS_Store"):
os.remove(INDIR + validation_data_dir + ".DS_Store")
#set dimensions of the images
img_rows, img_cols = 224, 224
if K.image_data_format() == 'channels_first':
shape_ord = (3, img_rows, img_cols)
else: # channel_last
shape_ord = (img_rows, img_cols, 3)
vgg16_model = vgg16.VGG16(weights=None, include_top=False, input_tensor=Input(shape_ord))
vgg16_model.summary()
#add last fully-connected layers
x = Flatten(input_shape=vgg16_model.output.shape)(vgg16_model.output)
x = Dense(4096, activation='relu', name='ft_fc1')(x)
x = Dropout(0.5)(x)
x = BatchNormalization()(x)
predictions = Dense(43, activation='softmax')(x)
model = Model(inputs=vgg16_model.input, outputs=predictions)
#compile the model
model.compile(optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
loss='categorical_crossentropy', metrics=['accuracy'])
#load validation images and create labels list
validation_filenames = os.listdir(validation_data_dir)
validation_filenames.sort()
validation_images = []
validation_labels = []
for name in validation_filenames:
if name.endswith(".ppm"):
validation_images.append(validation_data_dir + name)
label = name.split("_")[0]
label_int = int(label)
labels_array = [0]*43
labels_array[label_int] = 1
validation_labels.append(labels_array)
else:
validation_filenames.remove(name)
print("Validation Filenames loaded.")
validation = np.array(load_im2(validation_images, img_cols, img_rows))
print("Validation images loaded.")
model.load_weights("experiment_output/vgg16_deepstreet_training1.h5")
predicted_labels = model.predict(validation)
print("Labels predicted.")
#write summary file
prediction_summary = open(OUTDIR + "vgg16_deepstreet_t_prediction_summary_deepstreet_v.txt", "w")
prediction_summary.write("\t".join(['FILENAME', 'REAL_LABEL', 'PREDICTED_LABELS']) + '\n')
predicted_labels_linear = []
validation_labels_linear = []
#make linear labels list
for lbl in validation_labels:
for i,val in enumerate(lbl):
if val == 1:
validation_labels_linear.append(i)
for i in range(len(predicted_labels)):
cls_prob = predicted_labels[i] #percentage of belonging for i image
predicted_label_index = np.argmax(cls_prob) #get the index of the class with higher probability
line = [validation_images[i], str(validation_labels_linear[i]), str(predicted_label_index), str(round(cls_prob[predicted_label_index],3))]
s = ""
for i in range(42):
s += "{}:{}; ".format(i,round(cls_prob[i],3))
#s += str(i) + ":" + str(round(cls_prob[i],3)) + "; "
s += "42:{}".format(round(cls_prob[42],3))
#s += "42:" + str(round(cls_prob[42],3))
line.append(s)
predicted_labels_linear.append(np.argmax(cls_prob))
prediction_summary.write(";".join(line) + "\n")
prediction_summary.flush()
validation_labels_linear = np.array(validation_labels_linear)
predicted_labels_linear = np.array(predicted_labels_linear)
#calculate MCC
MCC = multimcc(validation_labels_linear, predicted_labels_linear)
print(MCC)
prediction_summary.write("MCC = {}".format(MCC))
prediction_summary.flush()
prediction_summary.close()
#compute confusion matrix and save the image
conf_matrix = confusion_matrix(validation_labels_linear,predicted_labels_linear)[0]
plt.matshow(conf_matrix)
plt.colorbar()
plt.savefig("confusion_matrix.png")
end = timer()
print("Total time: ", end - start)
if lbl[1] == 1:
validation_labels_linear.append(1)
if lbl[2] == 1:
validation_labels_linear.append(2)
validation_labels_linear = np.array(validation_labels_linear)
predicted_labels_linear = np.array(predicted_labels_linear)
MCC = multimcc(validation_labels_linear, predicted_labels_linear)
mcc_list.append(MCC)
perc_white.append(p)
prediction_summary.write("MCC=" + str(MCC) + "\n")
prediction_summary.flush()
prediction_summary.write(
str(confusion_matrix(validation_labels_linear,
predicted_labels_linear)))
prediction_summary.flush()
prediction_summary.close()
mcc_list = np.array(mcc_list)
perc_white = np.array(perc_white)
fig = plt.figure()
ax = fig.add_subplot(111)
mcc_list_round = [round(i, 3) for i in mcc_list]
j_old = 0.0
for i, j in zip(perc_white, mcc_list_round):
if j_old != j:
ax.annotate(str(j), xy=(i, j + 0.01))
j_old = j
elif cl == 1 and j == 2:
real_label = "Late"
line = [validation_images[i], real_label, str(round(cls_prob[0],3)), str(round(cls_prob[1],3)), str(round(cls_prob[2],3))]
predicted_labels_linear.append(np.argmax(cls_prob))
prediction_summary.write(";".join(line)+"\n")
prediction_summary.flush()
validation_labels_linear = []
for lbl in validation_labels:
if lbl[0] == 1:
validation_labels_linear.append(0)
if lbl[1] == 1:
validation_labels_linear.append(1)
if lbl[2] == 1:
validation_labels_linear.append(2)
validation_labels_linear = np.array(validation_labels_linear)
predicted_labels_linear = np.array(predicted_labels_linear)
MCC = multimcc(validation_labels_linear, predicted_labels_linear)
prediction_summary.write("MCC=" + str(MCC) + "\n")
prediction_summary.flush()
prediction_summary.write(str(confusion_matrix(validation_labels_linear, predicted_labels_linear)))
prediction_summary.flush()
prediction_summary.close()