I'm new in deep learning, so I'm trying to learn more and more about everything, so I tried to execute a code from github about liveness face detection,so when I did train that model with mobilenet I did get this graphs ans this results, so how can I extract Informations and interpret them :model accuracy
and I get this confusion matrix with the classification report : Confusion Matrix
[[1436 234]
[ 14 1461]]
Classification Report
precision recall f1-score support
0 0.99 0.86 0.92 1670
1 0.86 0.99 0.92 1475
accuracy 0.92 3145
macro avg 0.93 0.93 0.92 3145
weighted avg 0.93 0.92 0.92 3145
and here it's the code that I executed:
import keras
from keras import backend as K
from keras.layers.core import Dense, Activation
from keras.optimizers import Adam
from keras.metrics import categorical_crossentropy
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing import image
from keras.models import Model
from keras.applications import imagenet_utils
from keras.layers import Dense,GlobalAveragePooling2D
from keras.applications import MobileNet
from keras.applications.mobilenet import preprocess_input
import numpy as np
import matplotlib.pyplot as plt
from keras.optimizers import Adam
from sklearn.metrics import classification_report, confusion_matrix
INIT_LR = 1e-5
BS = 32
EPOCHS = 20
# MobileNetV2
base_model = keras.applications.mobilenet_v2.MobileNetV2(input_shape=None, alpha=1.0, include_top=False, weights='imagenet', input_tensor=None, pooling=None, classes=2)
x=base_model.output # a luat outputul inainte de FC
x=GlobalAveragePooling2D()(x)
x=Dense(512,activation='relu')(x) #dense layer 3
preds=Dense(2,activation='softmax')(x) #final layer with softmax activation
# specify the inputs
# specify the outputs
# now a model has been created based on our architecture
new_model=Model(inputs=base_model.input, outputs=preds)
# check the model architecture
for i,layer in enumerate(new_model.layers):
print(i,layer.name)
# We are not using weights pre-trained on ImageNet.
for layer in new_model.layers:
layer.trainable=True
train_datagen=ImageDataGenerator()
train_generator=train_datagen.flow_from_directory('../db_faces/train',
target_size=(64,64),
color_mode='rgb',
batch_size=BS,
class_mode='binary',
shuffle=True)
validation_generator=train_datagen.flow_from_directory('../db_faces/test',
target_size=(64,64),
color_mode='rgb',
batch_size=BS,
class_mode='binary',
shuffle=False)
# Adam optimizer
adam_opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
# loss function will be binary cross entropy
# evaluation metric will be accuracy
new_model.compile(optimizer=adam_opt, loss="sparse_categorical_crossentropy", metrics=['accuracy'])
step_size_train = train_generator.n//train_generator.batch_size
step_size_validation = validation_generator.samples // validation_generator.batch_size
H = new_model.fit(train_generator,
steps_per_epoch=step_size_train,
validation_data = validation_generator,
validation_steps = step_size_validation,
epochs=EPOCHS)
# save the network to disk
print("[INFO] serializing network and save it to disk...")
new_model.save('liveness.model')
print("[INFO] Class indices")
labels = (train_generator.class_indices)
print(labels)
# summarize history for accuracy
plt.plot(H.history['accuracy'])
plt.plot(H.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(H.history['loss'])
plt.plot(H.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
Y_pred = new_model.predict(validation_generator, validation_generator.samples // BS + 1)
y_pred = np.argmax(Y_pred, axis=1)
print('Confusion Matrix')
print(confusion_matrix(validation_generator.classes, y_pred))
print('Classification Report')
target_names = ['Fake', 'Real']
print(classification_report(validation_generator.classes, y_pred))
