cifar-10 每张图片的大小为 32×32，而 AlexNet 要求图片的输入是 224×224（也有说 227×227 的，这是 224×224 的图片进行大小为 2 的 zero padding 的结果），所以一种做法是将 cifar-10 数据集的图片 resize 到 224×224。
此时遇到的问题是，cifar-10 resize 到 224×224 时，32G 内存都将无法完全加载所有数据，在归一化那一步（即每个像素点除以 255）就将发生 OOM（out of memory）。
那么此时的做法有：
1）将 resize 作为模型的一部分，如设置一个 layer 来对一个 batch 的图像进行 resize，这样 32×32 的 cifar-10 仍然可以完全加载到内存中； 2）一种通用的方法，每次只加载一部分数据到内存中，其余数据等到需要的时候再加载到内存。注：本文 AlexNet 结构与 PyTorch 中一致。
方法 1：加上一个 Lambda 层，对输入图片进行 resize
import tensorflow as tffrom tensorflow.keras import layersfrom tensorflow.python.keras import backend as KK.clear_session()config = tf.ConfigProto()config.gpu_options.allow_growth = True # 不全部占满显存, 按需分配K.set_session(tf.Session(config=config))# 超参数learning_rate = 0.001epochs = 120batch_size = 32cifar10 = tf.keras.datasets.cifar10(x_train, y_train), (x_test, y_test) = cifar10.load_data()x_train = x_train.astype(np.float32)x_test = x_test.astype(np.float32)x_train = x_train / 255x_test = x_test / 255model = tf.keras.models.Sequential([ # Lambda 层，对输入图片进行 resize，以下是将图片扩大了 7 倍 # resize 时，默认使用最近邻插值，想要用其它插值方式，需要直接修改 K.resize_images 方法的源代码。 layers.Lambda(lambda img: K.resize_images(img, 7, 7, data_format='channels_last'), input_shape=(32, 32, 3)), layers.ZeroPadding2D(padding=(2, 2)), layers.Conv2D(64, (11, 11), strides=(4, 4), padding='valid', activation='relu', kernel_initializer='he_uniform'), layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2)), layers.Conv2D(192, (5, 5), strides=(1, 1), padding='same', activation='relu', kernel_initializer='he_uniform'), layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2)), layers.Conv2D(384, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='he_uniform'), layers.Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='he_uniform'), layers.Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='he_uniform'), layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2)), layers.Flatten(), layers.Dense(4096, activation='relu', kernel_initializer='he_uniform'), layers.Dropout(drop_rate), layers.Dense(4096, activation='relu', kernel_initializer='he_uniform'), layers.Dropout(drop_rate), layers.Dense(num_classes, activation='softmax', kernel_initializer='he_uniform')])model.summary()model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size, verbose=2, validation_data=(x_val, y_val))
方法 2：使用 tensorflow.keras.utils.Sequence，构造一个 data generator
import tensorflow as tffrom tensorflow.keras import layersfrom tensorflow.python.keras import backend as Kfrom tensorflow.keras.utils import Sequencefrom sklearn.model_selection import StratifiedShuffleSplitimport cv2import osimport numpy as npimport h5pyimport timeclass CIFAR10Sequence(Sequence): def __init__(self, x_set, y_set, batch_size): """ :param x_set: hdf5 :param y_set: hdf5 :param batch_size: int """ self.x, self.y = x_set, y_set self.batch_size = batch_size def __len__(self): return int(np.ceil(len(self.x) / float(self.batch_size))) def __getitem__(self, idx): batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size] batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size] batch_x = batch_x.astype(np.float32) batch_x = batch_x / 255 return batch_x, batch_ydef _resized_data(): """ 将 resize 后的 cifar-10 保存到 'data/cifar-10.h5' 图片大小: [224, 224, 3] :return: None """ cifar10 = tf.keras.datasets.cifar10 (x_train, y_train), (x_test, y_test) = cifar10.load_data() start_time = time.clock() x_train = np.array([cv2.resize(img, (224, 224), interpolation=cv2.INTER_CUBIC) for img in x_train]) x_test = np.array([cv2.resize(img, (224, 224), interpolation=cv2.INTER_CUBIC) for img in x_test]) # initialize x_val = np.array([]) y_val = np.array([]) sss = StratifiedShuffleSplit(n_splits=1, test_size=0.1, random_state=32) for train_index, val_index in sss.split(x_train, y_train): print("TRAIN:", train_index, "VAL:", val_index) x_train, x_val = x_train[train_index], x_train[val_index] y_train, y_val = y_train[train_index], y_train[val_index] end_time = time.clock() print('Time consuming of resizing: ', (end_time - start_time)) # 写文件 filename = 'data/cifar-10.h5' h5f = h5py.File(filename, 'w') h5f.create_dataset('x_train', data=x_train) h5f.create_dataset('y_train', data=y_train) h5f.create_dataset('x_val', data=x_val) h5f.create_dataset('y_val', data=y_val) h5f.create_dataset('x_test', data=x_test) h5f.create_dataset('y_test', data=y_test) h5f.close()def load_resized_data(filename='data/cifar-10.h5'): if not os.path.exists(filename): _resized_data() # 不要关闭 h5 文件，否则将无法读取数据，这一步并不会直接将数据加载到内存中 # h5 文件支持切片读取，而且也很快 h5f = h5py.File(filename, 'r') x_train = h5f['x_train'] y_train = h5f['y_train'] x_val = h5f['x_val'] y_val = h5f['y_val'] x_test = h5f['x_test'] y_test = h5f['y_test'] return (x_train, y_train), (x_val, y_val), (x_test, y_test)K.clear_session()config = tf.ConfigProto()config.gpu_options.allow_growth = True # 不全部占满显存, 按需分配K.set_session(tf.Session(config=config))# 超参数learning_rate = 0.001epochs = 120batch_size = 32(x_train, y_train), (x_val, y_val), (x_test, y_test) = load_resized_data()x_val = x_val.astype(np.float32)x_test = x_test.astype(np.float32)x_val = x_val / 255x_test = x_test / 255model = tf.keras.models.Sequential([ layers.ZeroPadding2D(padding=(2, 2), input_shape=(224, 224, 3)), layers.Conv2D(64, (11, 11), strides=(4, 4), padding='valid', activation='relu', kernel_initializer='he_uniform'), layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2)), layers.Conv2D(192, (5, 5), strides=(1, 1), padding='same', activation='relu', kernel_initializer='he_uniform'), layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2)), layers.Conv2D(384, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='he_uniform'), layers.Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='he_uniform'), layers.Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='he_uniform'), layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2)), layers.Flatten(), layers.Dense(4096, activation='relu', kernel_initializer='he_uniform'), layers.Dropout(drop_rate), layers.Dense(4096, activation='relu', kernel_initializer='he_uniform'), layers.Dropout(drop_rate), layers.Dense(num_classes, activation='softmax', kernel_initializer='he_uniform')])model.summary()model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])# shuffle 默认为 True, 意味着在训练一个 epoch 之后，CIFAR10Sequence 的 idx 会随机选择，而不是顺序选择，这样在 batch-level 进行了随机，一个 batch 内的样本顺序是固定的model.fit_generator(CIFAR10Sequence(x_train, y_train, batch_size=batch_size), # steps_per_epoch=int(np.ceil(len(x_train)/batch_size)), epochs=epochs, verbose=2, callbacks=None, validation_data=(x_val[:], y_val[:]))