Getting Precision,Recall,Sensitivity and Specificity in keras CNN - keras

Question

I have created a CNN that does binary classification on images. The CNN is seen below:
def neural_network():
classifier = Sequential()
# Adding a first convolutional layer
classifier.add(Convolution2D(48, 3, input_shape = (320, 320, 3), activation = 'relu'))
classifier.add(MaxPooling2D())
# Adding a second convolutional layer
classifier.add(Convolution2D(48, 3, activation = 'relu'))
classifier.add(MaxPooling2D())
#Flattening
classifier.add(Flatten())
#Full connected
classifier.add(Dense(256, activation = 'relu'))
#Full connected
classifier.add(Dense(1, activation = 'sigmoid'))
# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
classifier.summary()
train_datagen = ImageDataGenerator(rescale = 1./255,
horizontal_flip = True,
vertical_flip=True,
brightness_range=[0.5, 1.5])
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('/content/drive/My Drive/data_sep/train',
target_size = (320, 320),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('/content/drive/My Drive/data_sep/validate',
target_size = (320, 320),
batch_size = 32,
class_mode = 'binary')
es = EarlyStopping(
monitor="val_accuracy",
patience=15,
mode="max",
baseline=None,
restore_best_weights=True,
)
filepath = "/content/drive/My Drive/data_sep/weightsbestval.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]
history = classifier.fit(training_set,
epochs = 50,
validation_data = test_set,
callbacks= callbacks_list
)
best_score = max(history.history['val_accuracy'])
return best_score
The images in the folders are organized in the following way:
-train
-healthy
-patient
-validation
-healthy
-patient
Is there a way to calculate the metrics Precision,Recall,Sensitivity and Specificity or at least the true positives,true negatives,false positive and false negatives from this code?

Answer 1

from sklearn.metrics import classification_report
test_set = test_datagen.flow_from_directory('/content/drive/My Drive/data_sep/validate',
target_size = (320, 320),
batch_size = 32,
class_mode = 'binary')
predictions = model.predict_generator(
test_set,
steps = np.math.ceil(test_set.samples / test_set.batch_size),
)
predicted_classes = np.argmax(predictions, axis=1)
true_classes = test_set.classes
class_labels = list(test_set.class_indices.keys())
report = classification_report(true_classes, predicted_classes, target_names=class_labels)
accuracy = metrics.accuracy_score(true_classes, predicted_classes)
& if you do print(report) ,it will print everything
And if your whole data files are not divisible by your batch size, then use
test_set = test_datagen.flow_from_directory('/content/drive/My Drive/data_sep/validate',
target_size = (320, 320),
batch_size = 1,
class_mode = 'binary')