深度学习之利用TensorFlow实现简单的卷积神经网络（MNIST数据集）

卷积神经网络（Convolutional Neural Networks, CNN）是一类包含卷积计算且具有深度结构的前馈神经网络（Feedforward Neural Networks），是深度学习（Deep Learning）的代表算法之一。

示例代码：

#!/usr/bin/env python
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"]='2'
 
import tensorflow as tf
 
# 打印TensorFlow版本号
print(tf.__name__, tf.__version__)
 
# 下载MNIST数据集
(x_train_all, y_train_all), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
 
# 预处理数据 [0, 255] => [0.0, 1.0]
x_train_all, x_test = x_train_all / 255.0, x_test / 255.0
x_train_all = x_train_all.reshape((60000, 28, 28, 1))
x_test = x_test.reshape((10000, 28, 28, 1))
 
# 分割数据60000条数据，前5000条为验证数据，后55000条为训练数据
x_valid, x_train = x_train_all[:5000], x_train_all[5000:]
y_valid, y_train = y_train_all[:5000], y_train_all[5000:]
 
# 构建卷积神经网络模型
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding='same', activation='relu', input_shape=(28, 28, 1)))
model.add(tf.keras.layers.MaxPool2D(pool_size=2))
model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))
model.add(tf.keras.layers.MaxPool2D(pool_size=2))
model.add(tf.keras.layers.Conv2D(filters=128, kernel_size=3, padding='same', activation='relu'))
model.add(tf.keras.layers.MaxPool2D(pool_size=2))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
 
# 编译模型
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
 
# 训练模型
model.fit(x_train, y_train, epochs=10, validation_data=(x_valid, y_valid))
 
# 评估模型
model.evaluate(x_test, y_test)

运行结果如下：

tensorflow 2.0.0-rc0
Train on 55000 samples, validate on 5000 samples
Epoch 1/10

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55000/55000 [==============================] - 51s 936us/sample - loss: 0.1647 - accuracy: 0.9494 - val_loss: 0.0608 - val_accuracy: 0.9832
Epoch 2/10

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54560/55000 [============================>.] - ETA: 0s - loss: 0.0525 - accuracy: 0.9837
54624/55000 [============================>.] - ETA: 0s - loss: 0.0525 - accuracy: 0.9837
54688/55000 [============================>.] - ETA: 0s - loss: 0.0525 - accuracy: 0.9837
54752/55000 [============================>.] - ETA: 0s - loss: 0.0525 - accuracy: 0.9837
54816/55000 [============================>.] - ETA: 0s - loss: 0.0524 - accuracy: 0.9837
54880/55000 [============================>.] - ETA: 0s - loss: 0.0524 - accuracy: 0.9837
54944/55000 [============================>.] - ETA: 0s - loss: 0.0523 - accuracy: 0.9837
55000/55000 [==============================] - 49s 886us/sample - loss: 0.0523 - accuracy: 0.9837 - val_loss: 0.0444 - val_accuracy: 0.9896
................................................................................
10000/10000 [==============================] - 2s 229us/sample - loss: 0.0178 - accuracy: 0.9920
[Finished in 496.2s]

测试数据集的准确率从全连接层网络模型的98.16%上升到卷积神经网络模型的99.20%，挺牛逼的啊。。鉴定完毕~~~