I'm trying to learn multi-label classification using Keras. The dataset is Robotic Instrument (for kidney surgery) classification with 11 classes. I built a model for a multi-label classification problem and able to evaluate model performance. However, I have problem to claculate test accuracy and confusion matrix. Can anyone please share a code snippt that works. Thanks in advance to everyone!
name_label_dictionary = {
1: "instrument-shaft",
2: "instrument-clasper",
3: "instrument-wrist",
4: "kidney-parenchyma",
5: "covered-kidney",
6: "thread",
7: "clamps",
8: "suturing-needle",
9: "suction-instrument",
10: "small-intestine",
11: "ultrasound-probe"}
image_dataframe = pd.read_csv('data/train.csv')
encoder = MultiLabelBinarizer()
encoder.fit_transform([(1,),(2,),(3,),(4,),(5,),(6,),(7,),(8,),(9,),(10,), (11,)])
samples = list(zip(image_dataframe['Id'],image_dataframe['Target']))
train_samples, validation_samples = train_test_split(samples,test_size=0.15)
def generator(data,batch_size=8):
images_path_length = len(data)
while 1:
for off in range(0,images_path_length,batch_size):
images_list = data[off:off+batch_size]
rgb_arr=[]
label=[]
for j in images_list:
img=[]
img = np.array(Image.open("data/train/"+j[0]+"_resized.png"))/255
rgb_arr.append(img)
label.append(encoder.transform([tuple(map(int,j[1].split()))]))
yield np.array(rgb_arr),np.array(label).reshape(len(label),11)
train_images_gen = generator(train_samples)
val_images_gen = generator(validation_samples)
Here is the Keras code to train the data.
model = InceptionV3(include_top = True, weights = None, classes=11)
model.layers.pop()
x = model.layers[-1].output
x = Dense(11, activation='sigmoid', name='predictions')(x)
train_model = Model(input=model.input,output=x)
train_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
train_model.fit_generator(train_images_gen,
epochs=10,
verbose=1,
steps_per_epoch=len(train_samples)/8,
validation_data=val_images_gen,
validation_steps=len(validation_samples)/8)
score = train_model.evaluate_generator(val_images_gen, steps=50)
print ('Validation Score: ', score[0])
print ('Validation Accuracy: ',score[1])
At the next part, I generate the test set and its corresponding labels similar to train and validation set.
test_image_dataframe = pd.read_csv('data/test.csv')
test_samples = list(zip(test_image_dataframe['Id'], test_image_dataframe['Target']))
def test_generator(data,batch_size=8):
images_path_length = len(data)
while 1:
for off in range(0,images_path_length,batch_size):
images_list = data[off:off+batch_size]
rgb_arr=[]
label=[]
for j in images_list:
img=[]
img = np.array(Image.open("data/test/"+j[0]+"_resized.png"))/255
rgb_arr.append(img)
label.append(encoder.transform([tuple(map(int,j[1].split()))]))
yield np.array(rgb_arr),np.array(label).reshape(len(label),11)
and at the end predict the labels:
results = train_model.predict_generator(test_images_gen,steps=len(test_samples)/8,verbose=1)
predictions=[]
for i in results:
label_predict=np.arange(11)[i >=0.2]
predictions.append(' '.join(str(l) for l in label_predict))
print(predictions)
output of predictions is :
['0 1 2 4 5',
'0 1 2 4',
'0 1 2 4',
'0 1 2 3 4',
'0 1 2 3 4',
'0 1 2 3 4',
'0 1 2 3 4',
'0 1 2 3 4',
'0 1 2 3 4',
'1 2 3 4',
'0 1 2 3 4',
'0 1 2 3 4',
'0 1 2 3 4',
output of results is:
array([[9.96413887e-01, 1.00000000e+00, 9.80881095e-01, ...,
8.43488611e-04, 9.98612493e-03, 1.06460014e-07],
[9.88547325e-01, 9.99999881e-01, 9.43120480e-01, ...,
4.49081184e-03, 7.23437071e-02, 1.84218152e-05],
[9.99697685e-01, 1.00000000e+00, 9.99999642e-01, ...,
1.14535843e-03, 1.99837261e-03, 6.23652944e-03],
...,
[9.99984503e-01, 1.00000000e+00, 9.99968529e-01, ...,
4.65406990e-03, 5.77826202e-01, 3.62675011e-01],
