Question

我在30,000张图像上训练了CNN，并希望加载该模型。该模型名为“ emotion_recognition_model.h5”。每当我尝试调用模型时，都会开始对其进行重新训练。我如何在不进行重新培训的情况下加载它？这是训练模型的代码：

from __future__ import print_function
import keras
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization
from keras.layers import Conv2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
import os
from keras.models import Sequential
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers.advanced_activations import ELU
from keras.layers.core import Activation, Flatten, Dropout, Dense
from keras.optimizers import RMSprop, SGD, Adam
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from keras import regularizers
from keras.regularizers import l1

num_classes = 7
img_rows, img_cols = 48, 48
batch_size = 512

train_data_dir = "/Users/../Behavior/images/train"
validation_data_dir = "/Users/../Behavior/images/validation"


val_datagen = ImageDataGenerator(rescale=1./255)
train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=30,
    shear_range=0.3,
    zoom_range=0.3,
    horizontal_flip=True,
    fill_mode='nearest'
)
train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(48, 48),
    batch_size=batch_size,
    color_mode="grayscale",
    class_mode="categorical"
)
validation_generator = val_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(48, 48),
    batch_size=batch_size,
    color_mode="grayscale",
    class_mode="categorical"
)

print(validation_generator.class_indices)

class_labels = validation_generator.class_indices
class_labels = {v: k for k, v in class_labels.items()}
classes = list(class_labels.values())
print(class_labels)

model = Sequential()

model.add(Conv2D(32, kernel_size=(3, 3), activation='relu',kernel_regularizer=regularizers.l2(0.0001),input_shape=(48,48,1)))
model.add(BatchNormalization())

model.add(Conv2D(64, kernel_size=(3, 3), activation='relu',kernel_regularizer=regularizers.l2(0.0001)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(128, kernel_size=(3, 3), activation='relu', kernel_regularizer=regularizers.l2(0.0001)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(128, kernel_size=(3, 3), activation='relu', kernel_regularizer=regularizers.l2(0.0001)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))


model.add(Conv2D(7, kernel_size=(1, 1), activation='relu', kernel_regularizer=regularizers.l2(0.0001)))
model.add(BatchNormalization())

model.add(Conv2D(7, kernel_size=(4, 4), activation='relu', kernel_regularizer=regularizers.l2(0.0001)))
model.add(BatchNormalization())

model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(7, activation='softmax'))

model.add(Activation("softmax"))

filepath = os.path.join("./emotion_detector_models/model_v6_{epoch}.hdf5")

checkpoint = keras.callbacks.ModelCheckpoint("best_model.hdf5",
                                             monitor='val_accuracy',
                                             verbose=1,
                                             save_best_only=True,
                                             mode='max')
callbacks = [checkpoint]
model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.0001, decay=1e-6), metrics=['accuracy'])
nb_train_samples = 28709
nb_validation_samples = 3589
epochs = 150
model_info = model.fit(
            train_generator,
            steps_per_epoch=nb_train_samples // batch_size,
            epochs=epochs,
            callbacks=callbacks,
            validation_data=validation_generator,
            validation_steps=nb_validation_samples // batch_size)


model.save('emotion_recognition_model.h5')
print("Saved model")

使用上面的代码进行

培训大约需要24个小时以上，因此我希望能够简单地加载模型。这是我尝试加载模型的代码：

from emotion_recognition_model_file import train_generator, class_labels
from keras.models import load_model
import cv2
import numpy as np
from time import sleep
from keras.preprocessing.image import img_to_array

face_classifier = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")

try:
    classifier = load_model("/Users/TomSmith/Desktop/Vrify/DELPHI/Behavior/emotion_recognition_model.h5")
except:
    try:
        classifier = load_model("/Users/TomSmith/Desktop/Vrify/DELPHI/Behavior/best_model.hdf5")
    except:
        print('e')

eye_cascade = cv2.CascadeClassifier("/Users/TomSmith/Desktop/Vrify/DELPHI/Behavior/haarcascade_eye.xml")


def face_detector(img):
    # Convert image to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_classifier.detectMultiScale(gray, 1.3, 5)
    if faces == ():
        return (0, 0, 0, 0), np.zeros((48, 48), np.uint8), img

    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
        roi_gray = gray[y:y + h, x:x + w]
        roi_color = img[y:y + h, x:x + w]
        eyes = eye_cascade.detectMultiScale(roi_gray)
        for (ex, ey, ew, eh) in eyes:
            cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)

    try:
        roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)

    except:
        return (x,w,y,h), np.zeros((48, 48), np.uint8), img
    return (x,w,y,h), roi_gray, img

cap = cv2.VideoCapture(0)

while True:

    ret, frame = cap.read()
    rect, face, image = face_detector(frame)
    if np.sum([face]) != 0.0:
        roi = face.astype("float") / 255.0
        roi = img_to_array(roi)
        roi = np.expand_dims(roi, axis=0)

        # make a prediction on the ROI, then lookup the class
        preds = classifier.predict(roi)[0]
        label = class_labels[preds.argmax()]
        label_position = (rect[0] + int((rect[1] / 2)), rect[2] + 25)
        cv2.putText(image, label, label_position, cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 3)
    else:
        cv2.putText(image, "No Face Found", (20, 60), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 3)

    cv2.imshow('All', image)
    if cv2.waitKey(1) == 13:  # 13 is the Enter Key
        break

cap.release()
cv2.destroyAllWindows()

我还将为cv2代码添加线程，但是我不能简单地加载模型。我该怎么办？

编辑：我知道它如何进行再培训的屏幕截图

Answer 1

我想知道您想问“训练后如何保存模型吗？”，对吗？我看到您使用Keras（您选择Tensorflow作为其后端）来训练模型。所以也许您可以使用

from keras.model import load_model
# save model,assuming "model" is the name of the your instance,give a name as parameter
model.save("name.h5")
#load model
model = load_model("name.h5")

也许您也可以使用Tensorflow而非Keras提供的一些方法来满足您的需求。我不确定。希望对您有所帮助，祝您好运！

如何简单地加载ML模型而不进行重新训练？

1 个答案: