版本：Tensordlow2.1
运行环境：Google Colaboratory

import osos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import keras_applications
import tensorflow as tf
from tensorflow.keras import datasets, Sequential, layers
from tensorflow.keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Dropout
import matplotlib.pyplot as plt# datasets download
(x, y), (x_val, y_val) = datasets.cifar10.load_data()
print(x.shape, y.shape)def preprocess(x, y):""":param x::param y::return:"""x = tf.cast(x, dtype=tf.float32) / 255.x = tf.reshape(x, [-1, 32, 32, 3])y = tf.one_hot(tf.cast(y, dtype=tf.int32), depth=10)y = tf.reshape(y, [-1, 10])return x, ytrain_db = tf.data.Dataset.from_tensor_slices((x, y))
val_db = tf.data.Dataset.from_tensor_slices((x_val, y_val))
train_db = train_db.batch(256).map(preprocess)
val_db = val_db.map(preprocess)
sample = next(iter(train_db))
print(sample[0].shape, sample[1].shape)model = Sequential([# unit1Conv2D(filters=64, kernel_size=(3, 3), activation='relu', padding='same', name='Block1_conv1',input_shape=(32, 32, 3)),Conv2D(filters=64, kernel_size=(3, 3), activation='relu', padding='same', name='Block1_conv2'),MaxPool2D(pool_size=(2, 2), strides=(2, 2), name='Block1_pool', padding='same'),# unit2Conv2D(filters=128, kernel_size=(3, 3), activation='relu', padding='same', name='Block2_conv1'),Conv2D(filters=123, kernel_size=(3, 3), activation='relu', padding='same', name='Block2_conv2'),MaxPool2D(pool_size=(2, 2), strides=(2, 2), name='Block2_pool', padding='same'),# unit3Conv2D(filters=256, kernel_size=(3, 3), activation='relu', padding='same', name='Block3_conv1'),Conv2D(filters=256, kernel_size=(3, 3), activation='relu', padding='same', name='Block3_conv2'),MaxPool2D(pool_size=(2, 2), strides=(2, 2), name='Block3_pool', padding='same'),# unit4Conv2D(filters=512, kernel_size=(3, 3), activation='relu', padding='same', name='Block4_conv1'),Conv2D(filters=512, kernel_size=(3, 3), activation='relu', padding='same', name='Block4_conv2'),MaxPool2D(pool_size=(2, 2), strides=(2, 2), name='Block4_pool', padding='same'),# unit5Conv2D(filters=512, kernel_size=(3, 3), activation='relu', padding='same', name='Block5_conv1'),Conv2D(filters=512, kernel_size=(3, 3), activation='relu', padding='same', name='Block5_conv2'),MaxPool2D(pool_size=(2, 2), strides=(2, 2), name='Block5_pool', padding='same'),
])model.add(Flatten())  # 扁平化
model.add(Dense(4096, activation='relu'))
model.add(Dropout(rate=0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(rate=0.5))
model.add(Dense(10, activation='softmax'))
model.summary()model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(train_db, epochs=20, validation_data=val_db, shuffle=True)plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('AlexNet')
plt.xlabel('epoch')
plt.ylabel('loss')
plt.legend(['Train', 'loss'])
plt.show()

输出：

Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
170500096/170498071 [==============================] - 2s 0us/step
(50000, 32, 32, 3) (50000, 1)
(256, 32, 32, 3) (256, 10)
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
Block1_conv1 (Conv2D)        (None, 32, 32, 64)        1792      
_________________________________________________________________
Block1_conv2 (Conv2D)        (None, 32, 32, 64)        36928     
_________________________________________________________________
Block1_pool (MaxPooling2D)   (None, 16, 16, 64)        0         
_________________________________________________________________
Block2_conv1 (Conv2D)        (None, 16, 16, 128)       73856     
_________________________________________________________________
Block2_conv2 (Conv2D)        (None, 16, 16, 123)       141819    
_________________________________________________________________
Block2_pool (MaxPooling2D)   (None, 8, 8, 123)         0         
_________________________________________________________________
Block3_conv1 (Conv2D)        (None, 8, 8, 256)         283648    
_________________________________________________________________
Block3_conv2 (Conv2D)        (None, 8, 8, 256)         590080    
_________________________________________________________________
Block3_pool (MaxPooling2D)   (None, 4, 4, 256)         0         
_________________________________________________________________
Block4_conv1 (Conv2D)        (None, 4, 4, 512)         1180160   
_________________________________________________________________
Block4_conv2 (Conv2D)        (None, 4, 4, 512)         2359808   
_________________________________________________________________
Block4_pool (MaxPooling2D)   (None, 2, 2, 512)         0         
_________________________________________________________________
Block5_conv1 (Conv2D)        (None, 2, 2, 512)         2359808   
_________________________________________________________________
Block5_conv2 (Conv2D)        (None, 2, 2, 512)         2359808   
_________________________________________________________________
Block5_pool (MaxPooling2D)   (None, 1, 1, 512)         0         
_________________________________________________________________
flatten (Flatten)            (None, 512)               0         
_________________________________________________________________
dense (Dense)                (None, 4096)              2101248   
_________________________________________________________________
dropout (Dropout)            (None, 4096)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 4096)              16781312  
_________________________________________________________________
dropout_1 (Dropout)          (None, 4096)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 10)                40970     
=================================================================
Total params: 28,311,237
Trainable params: 28,311,237
Non-trainable params: 0
_________________________________________________________________
Epoch 1/20
196/196 [==============================] - 101s 517ms/step - loss: 2.3025 - accuracy: 0.1031 - val_loss: 2.3024 - val_accuracy: 0.1233
Epoch 2/20
196/196 [==============================] - 100s 509ms/step - loss: 2.3023 - accuracy: 0.1174 - val_loss: 2.3021 - val_accuracy: 0.1120
Epoch 3/20
196/196 [==============================] - 100s 512ms/step - loss: 2.3020 - accuracy: 0.1268 - val_loss: 2.3018 - val_accuracy: 0.1225
Epoch 4/20
196/196 [==============================] - 100s 510ms/step - loss: 2.3017 - accuracy: 0.1415 - val_loss: 2.3014 - val_accuracy: 0.1603
Epoch 5/20
196/196 [==============================] - 100s 510ms/step - loss: 2.3013 - accuracy: 0.1579 - val_loss: 2.3009 - val_accuracy: 0.1721
Epoch 6/20
196/196 [==============================] - 101s 514ms/step - loss: 2.3007 - accuracy: 0.1676 - val_loss: 2.3002 - val_accuracy: 0.1944
Epoch 7/20
196/196 [==============================] - 100s 510ms/step - loss: 2.2999 - accuracy: 0.1765 - val_loss: 2.2991 - val_accuracy: 0.1984
Epoch 8/20
196/196 [==============================] - 100s 510ms/step - loss: 2.2987 - accuracy: 0.1815 - val_loss: 2.2974 - val_accuracy: 0.2104
Epoch 9/20
196/196 [==============================] - 101s 513ms/step - loss: 2.2968 - accuracy: 0.1867 - val_loss: 2.2946 - val_accuracy: 0.2222
Epoch 10/20
196/196 [==============================] - 100s 510ms/step - loss: 2.2936 - accuracy: 0.1895 - val_loss: 2.2897 - val_accuracy: 0.2388
Epoch 11/20
196/196 [==============================] - 100s 510ms/step - loss: 2.2878 - accuracy: 0.1881 - val_loss: 2.2800 - val_accuracy: 0.2178
Epoch 12/20
196/196 [==============================] - 101s 514ms/step - loss: 2.2741 - accuracy: 0.1829 - val_loss: 2.2539 - val_accuracy: 0.1891
Epoch 13/20
196/196 [==============================] - 100s 510ms/step - loss: 2.2216 - accuracy: 0.1727 - val_loss: 2.1703 - val_accuracy: 0.1859
Epoch 14/20
196/196 [==============================] - 100s 510ms/step - loss: 2.1630 - accuracy: 0.1827 - val_loss: 2.1334 - val_accuracy: 0.2134
Epoch 15/20
196/196 [==============================] - 101s 515ms/step - loss: 2.1330 - accuracy: 0.1907 - val_loss: 2.0949 - val_accuracy: 0.2240
Epoch 16/20
196/196 [==============================] - 100s 510ms/step - loss: 2.1070 - accuracy: 0.2051 - val_loss: 2.0692 - val_accuracy: 0.2306
Epoch 17/20
196/196 [==============================] - 100s 511ms/step - loss: 2.0787 - accuracy: 0.2187 - val_loss: 2.0274 - val_accuracy: 0.2538
Epoch 18/20
196/196 [==============================] - 101s 514ms/step - loss: 2.0499 - accuracy: 0.2319 - val_loss: 1.9917 - val_accuracy: 0.2707
Epoch 19/20
196/196 [==============================] - 100s 510ms/step - loss: 2.0226 - accuracy: 0.2437 - val_loss: 1.9686 - val_accuracy: 0.2793
Epoch 20/20
196/196 [==============================] - 100s 510ms/step - loss: 1.9990 - accuracy: 0.2541 - val_loss: 1.9402 - val_accuracy: 0.2896

可能是由于训练的时间较短，所以效果并不明显；