我在使用Keras（2.2.4）及TensorFlow（1.9.0）作为后端对单张图像进行预测时遇到了问题：
def enigne(data): img=data image_shape=img.shape num_train_samples = 4206 num_val_samples = 916 train_batch_size = 10 val_batch_size = 10 IMAGE_SIZE = 64 IMAGE_CHANNELS = 3 kernel_size = (3, 3) pool_size = (2, 2) first_filters = 32 second_filters = 128 image_resize=cv.resize(img,(64,64)) # 加载模型 model = Sequential() model.add(Conv2D(first_filters, kernel_size, activation='relu', input_shape=(64, 64, 3))) model.add(Conv2D(first_filters, kernel_size, activation='relu', kernel_regularizer=regularizers.l2(0.001)) model.add(Conv2D(second_filters, kernel_size, activation='relu', kernel_regularizer=regularizers.l2(0.001))) model.add(MaxPooling2D(pool_size=pool_size)) model.add(Dropout(dropout_conv)) model.add(Flatten()) model.add(Dense(256, activation="relu")) model.add(Dense(1, activation="sigmoid")) model.compile(Adam(lr=0.0001), loss='binary_crossentropy', metrics=['accuracy']) datagen = ImageDataGenerator(rescale=1.0 / 255) model.load_weights('stableweights.h5') y_pred_keras = model.predict_proba(image_resize) p = [] for i in y_pred_keras: for k in i: if k <= 0.421: p.append(0) else: p.append(1) return p我遇到了如下错误：
ValueError: Error when checking input: expected conv2d_input to have 4 dimensions, but got array with shape (64, 64, 3) 如何将图像转换为合适的维度以输入到Keras模型中？
回答：
Keras模型期望输入的是样本批次。因此，您需要将第一个轴设置为批次轴：
