版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/qq_31456593/article/details/88916046
Tensorflow 2.0 教程持续更新 ：https://blog.csdn.net/qq_31456593/article/details/88606284
完整tensorflow2.0教程代码请看tensorflow2.0：中文教程tensorflow2_tutorials_chinese(欢迎star)
入门教程：
TensorFlow 2.0 教程- Keras 快速入门
TensorFlow 2.0 教程-keras 函数api
TensorFlow 2.0 教程-使用keras训练模型
TensorFlow 2.0 教程-用keras构建自己的网络层
TensorFlow 2.0 教程-keras模型保存和序列化
tensorflow2教程-CNN变体网络
1.载入数据
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train = x_train.reshape((-1,28,28,1))
x_test = x_test.reshape((-1,28,28,1))
print(x_train.shape, ' ', y_train.shape)
print(x_test.shape, ' ', y_test.shape)
(60000, 28, 28, 1) (60000,)
(10000, 28, 28, 1) (10000,)
2.简单的深度网络
如AlexNet,VggNet
x_shape = x_train.shape
deep_model = keras.Sequential(
[
layers.Conv2D(input_shape=((x_shape[1], x_shape[2], x_shape[3])),
filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),
layers.Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),
layers.MaxPool2D(pool_size=(2,2)),
layers.Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),
layers.Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),
layers.MaxPool2D(pool_size=(2,2)),
layers.Flatten(),
layers.Dense(32, activation='relu'),
layers.Dense(10, activation='softmax')
])
deep_model.compile(optimizer=keras.optimizers.Adam(),
loss=keras.losses.SparseCategoricalCrossentropy(),
metrics=['accuracy'])
deep_model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 28, 28, 32) 320
_________________________________________________________________
conv2d_1 (Conv2D) (None, 28, 28, 32) 9248
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 14, 14, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 14, 14, 32) 9248
_________________________________________________________________
conv2d_3 (Conv2D) (None, 14, 14, 32) 9248
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 7, 7, 32) 0
_________________________________________________________________
flatten (Flatten) (None, 1568) 0
_________________________________________________________________
dense (Dense) (None, 32) 50208
_________________________________________________________________
dense_1 (Dense) (None, 10) 330
=================================================================
Total params: 78,602
Trainable params: 78,602
Non-trainable params: 0
_________________________________________________________________
history = deep_model.fit(x_train, y_train, batch_size=64, epochs=5, validation_split=0.1)
Train on 54000 samples, validate on 6000 samples
Epoch 1/5
54000/54000 [==============================] - 72s 1ms/sample - loss: 0.2774 - accuracy: 0.9280 - val_loss: 0.0612 - val_accuracy: 0.9822
Epoch 2/5
54000/54000 [==============================] - 73s 1ms/sample - loss: 0.0646 - accuracy: 0.9802 - val_loss: 0.0516 - val_accuracy: 0.9850
Epoch 3/5
54000/54000 [==============================] - 69s 1ms/sample - loss: 0.0471 - accuracy: 0.9856 - val_loss: 0.0466 - val_accuracy: 0.9883
Epoch 4/5
54000/54000 [==============================] - 70s 1ms/sample - loss: 0.0385 - accuracy: 0.9879 - val_loss: 0.0614 - val_accuracy: 0.9843
Epoch 5/5
54000/54000 [==============================] - 69s 1ms/sample - loss: 0.0317 - accuracy: 0.9897 - val_loss: 0.0463 - val_accuracy: 0.9867
deep_model.evaluate(x_test, y_test)
10000/10000 [==============================] - 2s 219us/sample - loss: 0.0445 - accuracy: 0.9863
[0.04454196666887728, 0.9863]
import matplotlib.pyplot as plt
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.legend(['training', 'valivation'], loc='upper left')
plt.show()
result = deep_model.evaluate(x_test, y_test)
10000/10000 [==============================] - 2s 219us/sample - loss: 0.0445 - accuracy: 0.9863
3.添加了其它功能层的深度卷积
x_shape = x_train.shape
deep_model = keras.Sequential(
[
layers.Conv2D(input_shape=((x_shape[1], x_shape[2], x_shape[3])),
filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),
layers.BatchNormalization(),
layers.Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),
layers.BatchNormalization(),
layers.MaxPool2D(pool_size=(2,2)),
layers.Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),
layers.BatchNormalization(),
layers.BatchNormalization(),
layers.Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),
layers.MaxPool2D(pool_size=(2,2)),
layers.Flatten(),
layers.Dense(32, activation='relu'),
layers.Dropout(0.2),
layers.Dense(10, activation='softmax')
])
deep_model.compile(optimizer=keras.optimizers.Adam(),
loss=keras.losses.SparseCategoricalCrossentropy(),
metrics=['accuracy'])
deep_model.summary()
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_4 (Conv2D) (None, 28, 28, 32) 320
_________________________________________________________________
batch_normalization_v2 (Batc (None, 28, 28, 32) 128
_________________________________________________________________
conv2d_5 (Conv2D) (None, 28, 28, 32) 9248
_________________________________________________________________
batch_normalization_v2_1 (Ba (None, 28, 28, 32) 128
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 14, 14, 32) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 14, 14, 32) 9248
_________________________________________________________________
batch_normalization_v2_2 (Ba (None, 14, 14, 32) 128
_________________________________________________________________
batch_normalization_v2_3 (Ba (None, 14, 14, 32) 128
_________________________________________________________________
conv2d_7 (Conv2D) (None, 14, 14, 32) 9248
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 7, 7, 32) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 1568) 0
_________________________________________________________________
dense_2 (Dense) (None, 32) 50208
_________________________________________________________________
dropout (Dropout) (None, 32) 0
_________________________________________________________________
dense_3 (Dense) (None, 10) 330
=================================================================
Total params: 79,114
Trainable params: 78,858
Non-trainable params: 256
_________________________________________________________________
history = deep_model.fit(x_train, y_train, batch_size=64, epochs=5, validation_split=0.1)
Train on 54000 samples, validate on 6000 samples
Epoch 1/5
54000/54000 [==============================] - 120s 2ms/sample - loss: 0.2683 - accuracy: 0.9163 - val_loss: 0.0470 - val_accuracy: 0.9880
Epoch 2/5
54000/54000 [==============================] - 114s 2ms/sample - loss: 0.0979 - accuracy: 0.9697 - val_loss: 0.0444 - val_accuracy: 0.9853
Epoch 3/5
54000/54000 [==============================] - 118s 2ms/sample - loss: 0.0718 - accuracy: 0.9780 - val_loss: 0.0358 - val_accuracy: 0.9903
Epoch 4/5
54000/54000 [==============================] - 115s 2ms/sample - loss: 0.0559 - accuracy: 0.9825 - val_loss: 0.0463 - val_accuracy: 0.9887
Epoch 5/5
54000/54000 [==============================] - 115s 2ms/sample - loss: 0.0504 - accuracy: 0.9839 - val_loss: 0.0315 - val_accuracy: 0.9922
import matplotlib.pyplot as plt
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.legend(['training', 'valivation'], loc='upper left')
plt.show()
result = deep_model.evaluate(x_test, y_test)
10000/10000 [==============================] - 4s 365us/sample - loss: 0.0288 - accuracy: 0.9909
4.NIN网络
Min等人在 2013年（https://arxiv.org/abs/1312.4400）提出了减少模型中参数数量的方法之一
即“网络中的网络（NIN）”或“1X1卷积”
方法很简单 - 在其他卷积层之后添加卷积层
具有降低图像空间的维度（深度）的效果，有效地减少了参数的数量
GoogleNet 中就用到了NIN结构
x_shape = x_train.shape
deep_model = keras.Sequential(
[
layers.Conv2D(input_shape=((x_shape[1], x_shape[2], x_shape[3])),
filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),
layers.BatchNormalization(),
layers.Conv2D(filters=16, kernel_size=(1,1), strides=(1,1), padding='valid', activation='relu'),
layers.BatchNormalization(),
layers.MaxPool2D(pool_size=(2,2)),
layers.Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),
layers.BatchNormalization(),
layers.Conv2D(filters=16, kernel_size=(1,1), strides=(1,1), padding='valid', activation='relu'),
layers.BatchNormalization(),
layers.MaxPool2D(pool_size=(2,2)),
layers.Flatten(),
layers.Dense(32, activation='relu'),
layers.Dropout(0.2),
layers.Dense(10, activation='softmax')
])
deep_model.compile(optimizer=keras.optimizers.Adam(),
loss=keras.losses.SparseCategoricalCrossentropy(),
metrics=['accuracy'])
deep_model.summary()
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_8 (Conv2D) (None, 28, 28, 32) 320
_________________________________________________________________
batch_normalization_v2_4 (Ba (None, 28, 28, 32) 128
_________________________________________________________________
conv2d_9 (Conv2D) (None, 28, 28, 16) 528
_________________________________________________________________
batch_normalization_v2_5 (Ba (None, 28, 28, 16) 64
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 14, 14, 16) 0
_________________________________________________________________
conv2d_10 (Conv2D) (None, 14, 14, 32) 4640
_________________________________________________________________
batch_normalization_v2_6 (Ba (None, 14, 14, 32) 128
_________________________________________________________________
conv2d_11 (Conv2D) (None, 14, 14, 16) 528
_________________________________________________________________
batch_normalization_v2_7 (Ba (None, 14, 14, 16) 64
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 7, 7, 16) 0
_________________________________________________________________
flatten_2 (Flatten) (None, 784) 0
_________________________________________________________________
dense_4 (Dense) (None, 32) 25120
_________________________________________________________________
dropout_1 (Dropout) (None, 32) 0
_________________________________________________________________
dense_5 (Dense) (None, 10) 330
=================================================================
Total params: 31,850
Trainable params: 31,658
Non-trainable params: 192
_________________________________________________________________
history = deep_model.fit(x_train, y_train, batch_size=64, epochs=5, validation_split=0.1)
Train on 54000 samples, validate on 6000 samples
Epoch 1/5
54000/54000 [==============================] - 62s 1ms/sample - loss: 0.2729 - accuracy: 0.9147 - val_loss: 0.0657 - val_accuracy: 0.9818
Epoch 2/5
54000/54000 [==============================] - 63s 1ms/sample - loss: 0.0872 - accuracy: 0.9739 - val_loss: 0.0437 - val_accuracy: 0.9865
Epoch 3/5
54000/54000 [==============================] - 59s 1ms/sample - loss: 0.0657 - accuracy: 0.9800 - val_loss: 0.0404 - val_accuracy: 0.9890
Epoch 4/5
54000/54000 [==============================] - 49s 913us/sample - loss: 0.0535 - accuracy: 0.9834 - val_loss: 0.0622 - val_accuracy: 0.9830
Epoch 5/5
54000/54000 [==============================] - 49s 913us/sample - loss: 0.0441 - accuracy: 0.9860 - val_loss: 0.0435 - val_accuracy: 0.9892
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.legend(['training', 'valivation'], loc='upper left')
plt.show()
result = deep_model.evaluate(x_test, y_test)
10000/10000 [==============================] - 2s 196us/sample - loss: 0.0335 - accuracy: 0.9887