Нейрондық желілердің негізгі түсінігі

model.summary() 

csv_logger = CSVLogger(log_file_path, append=False) 

early_stop = EarlyStopping('val_loss', patience=patience) 

reduce_lr  =  ReduceLROnPlateau('val_loss',  factor  =  0.1,  patience  = 

int(patience/4), verbose = 1) 

trained_models_path = base_path + '_mini_XCEPTION' 

model_names = trained_models_path + '.{epoch:02d}-{val_acc:.2f}.hdf5' 

model_checkpoint  =  ModelCheckpoint(model_names,  'val_loss',  verbose  =  1, 

save_best_only = True) 

callbacks = [model_checkpoint, csv_logger, early_stop, reduce_lr] 

faces, emotions = '

test’ 

num_samples, num_classes = emotions.shape 

xtrain,  xtest,  ytrain,  ytest  =  train_test_split(faces,  emotions,  test_size=0.2, 

shuffle = True) 

model.fit_generator(data_generator.flow(xtrain, ytrain, batch_size), 

                        steps_per_epoch=len(xtrain)  /  batch_size,  epochs=num_epochs,  

verbose=1, callbacks=callbacks, validation_data=(xtest,ytest)) 

from keras.preprocessing.image import img_to_array 

import imutils 

import cv2 

from keras.models import load_model 

import numpy as npdetection_model_path = 

emotion_model_path = 'models/ABS_XCEPTION.hdf5' 

face_detection = cv2.CascadeClassifier(detection_model_path) 

emotion_classifier = load_model(emotion_model_path, compile=False) 

EMOTIONS = ["angry" ,"disgust","scared", "happy", "sad", "surprised", 

"neutral"]  

cv2.namedWindow('Emotion!') 

camera = cv2.VideoCapture(0) 

while True: 

    frame = camera.read()[1] 

    frame = imutils.resize(frame,width=400) 

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 

    faces = 

face_detection.detectMultiScale(gray,scaleFactor=1.1,minNeighbors=5,minSize=30,

30),flags=cv2.CASCADE_SCALE_IMAGE)     

    canvas = np.zeros((250, 300, 3), dtype="uint8")