尝试使用 Keras 训练模型，得到一个 ValueError 说形状不兼容

Question

我正在尝试创建一个手语翻译器，它使用网络摄像头检测手势并在屏幕上显示相应的字母或数字。

我正在学习教程并尝试使用 Keras 训练模型，但出现以下错误：

ValueError: (500,44) and (500,40) are incompatible

编码：

import numpy as np
import pickle
import cv2, os
from glob import glob
from tensorflow.keras import optimizers
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from keras.callbacks import ModelCheckpoint
from tensorflow.keras import backend as K
K.image_data_format()

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

def get_image_size():
    img = cv2.imread('gestures/1/100.jpg', 0)
    return img.shape

def get_num_of_classes():
    return len(glob('gestures/*'))

image_x, image_y = get_image_size()

def cnn_model():
    num_of_classes = get_num_of_classes()
    model = Sequential()
    model.add(Conv2D(16, (2,2), input_shape=(image_x, image_y, 1), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'))
    model.add(Conv2D(32, (3,3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(3, 3), strides=(3, 3), padding='same'))
    model.add(Conv2D(64, (5,5), activation='relu'))
    model.add(MaxPooling2D(pool_size=(5, 5), strides=(5, 5), padding='same'))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.2))
    model.add(Dense(num_of_classes, activation='softmax'))
    sgd = optimizers.SGD(lr=1e-2)
    class_mode='categorical'
    model.compile(loss='categorical_crossentropy', 
    optimizer=sgd, metrics=['accuracy'])
    filepath="cnn_model_keras2.h5"
    checkpoint1 = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
    callbacks_list = [checkpoint1]
    from keras.utils import plot_model
    plot_model(model, to_file='model.png', show_shapes=True)
    return model, callbacks_list

def train():
    with open("train_images", "rb") as f:
        train_images = np.array(pickle.load(f))
    with open("train_labels", "rb") as f:
        train_labels = np.array(pickle.load(f), dtype=np.int32)

    with open("val_images", "rb") as f:
        val_images = np.array(pickle.load(f))
    with open("val_labels", "rb") as f:
        val_labels = np.array(pickle.load(f), dtype=np.int32)

    train_images = np.reshape(train_images, (train_images.shape[0], image_x, image_y, 1))
    val_images = np.reshape(val_images, (val_images.shape[0], image_x, image_y, 1))
    train_labels = np_utils.to_categorical(train_labels)
    val_labels = np_utils.to_categorical(val_labels)

    print(val_labels.shape)

    model, callbacks_list = cnn_model()
    model.summary()
    model.fit(train_images, train_labels, validation_data=(val_images, val_labels), epochs=20, batch_size=500, callbacks=callbacks_list)
    scores = model.evaluate(val_images, val_labels, verbose=0)
    print("CNN Error: %.2f%%" % (100-scores[1]*100))
    #model.save('cnn_model_keras2.h5')

train()
K.clear_session();

Answer 1

问题可能在于您如何拆分训练和验证数据集。 抛出此错误是因为训练类的数量 (44) 和验证类的数量 (40) 不匹配。

您可能希望确保所有类都在验证集中表示，或者利用 to_categorical 中的num_classes参数来确保它们都在 one-hot 编码中表示。

    train_labels = np_utils.to_categorical(train_labels, num_classes=44)
    val_labels = np_utils.to_categorical(val_labels, num_classes=44)

https://www.tensorflow.org/api_docs/python/tf/keras/utils/to_categorical