I have trained my model(multiclass classification) of CNN using keras and now I want to evaluate the model on my test set of images.
What are the possible options for evaluating my model apart from the accuracy, precision and recall? I know how to get the precision and recall from a custom script. But I cannot find a way to get the confusion matrix for my 12 classes of images. Scikit-learn shows a way, but not for images. I am using model.fit_generator ()
Is there a way to create confusion matrix for all my classes or finding classification confidence on my classes? I am using Google Colab, though I can download the model and run it locally.
Any help would be appreciated.
Code:
train_data_path = 'dataset_cfps/train'
validation_data_path = 'dataset_cfps/validation'
#Parametres
img_width, img_height = 224, 224
vggface = VGGFace(model='resnet50', include_top=False, input_shape=(img_width, img_height, 3))
#vgg_model = VGGFace(include_top=False, input_shape=(224, 224, 3))
last_layer = vggface.get_layer('avg_pool').output
x = Flatten(name='flatten')(last_layer)
xx = Dense(256, activation = 'sigmoid')(x)
x1 = BatchNormalization()(xx)
x2 = Dropout(0.3)(x1)
y = Dense(256, activation = 'sigmoid')(x2)
yy = BatchNormalization()(y)
y1 = Dropout(0.6)(yy)
x3 = Dense(12, activation='sigmoid', name='classifier')(y1)
custom_vgg_model = Model(vggface.input, x3)
# Create the model
model = models.Sequential()
# Add the convolutional base model
model.add(custom_vgg_model)
model.summary()
#model = load_model('facenet_resnet_lr3_SGD_sameas1.h5')
def recall(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def precision(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
train_datagen = ImageDataGenerator(
rescale=1./255,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
validation_datagen = ImageDataGenerator(rescale=1./255)
# Change the batchsize according to your system RAM
train_batchsize = 32
val_batchsize = 32
train_generator = train_datagen.flow_from_directory(
train_data_path,
target_size=(img_width, img_height),
batch_size=train_batchsize,
class_mode='categorical')
validation_generator = validation_datagen.flow_from_directory(
validation_data_path,
target_size=(img_width, img_height),
batch_size=val_batchsize,
class_mode='categorical',
shuffle=True)
# Compile the model
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.SGD(lr=1e-3),
metrics=['acc', recall, precision])
# Train the model
history = model.fit_generator(
train_generator,
steps_per_epoch=train_generator.samples/train_generator.batch_size ,
epochs=100,
validation_data=validation_generator,
validation_steps=validation_generator.samples/validation_generator.batch_size,
verbose=1)
# Save the model
model.save('facenet_resnet_lr3_SGD_new_FC.h5')
Here's how to get the confusion matrix(or maybe statistics using scikit-learn) for all classes:
1.Predict classes
test_generator = ImageDataGenerator()
test_data_generator = test_generator.flow_from_directory(
test_data_path, # Put your path here
target_size=(img_width, img_height),
batch_size=32,
shuffle=False)
test_steps_per_epoch = numpy.math.ceil(test_data_generator.samples / test_data_generator.batch_size)
predictions = model.predict_generator(test_data_generator, steps=test_steps_per_epoch)
# Get most likely class
predicted_classes = numpy.argmax(predictions, axis=1)
2.Get ground-truth classes and class-labels
true_classes = test_data_generator.classes
class_labels = list(test_data_generator.class_indices.keys())
3. Use scikit-learn to get statistics
report = metrics.classification_report(true_classes, predicted_classes, target_names=class_labels)
print(report)
You can read more here
EDIT: If the above does not work, have a look at this video Create confusion matrix for predictions from Keras model. Probably look through the comments if you have an issue. Or Make predictions with a Keras CNN Image Classifier