人工智能算法实现mnist手写数字识别

【实例截图】

【源码目录】

mnist实验报告

├── ML_MNIST
│   ├── CNN.py
│   ├── FCN.py
│   └── RNN.py
└── mnist实验报告.doc

1 directory, 4 files

【核心代码】

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
print("x_train.shape", x_train.shape)
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)  # 给数据增加一个维度，使数据和网络结构匹配
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
print("x_train.shape", x_train.shape)

2.设置模型参数
model = tf.keras.models.Sequential([

    tf.keras.layers.Flatten(),

    tf.keras.layers.Dense(128, activation='relu'),

    tf.keras.layers.Dense(10, activation='softmax')

])

model.compile(optimizer='adam',

              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),

              metrics=['sparse_categorical_accuracy'])

3.打印记录相关信息
history = model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1 )
model.summary()

4.绘制图像

# 显示训练集和验证集的acc和loss曲线

acc = history.history['sparse_categorical_accuracy']

val_acc = history.history['val_sparse_categorical_accuracy']

loss = history.history['loss']

val_loss = history.history['val_loss']

plt.subplot(1, 2, 1)

plt.plot(acc, label='Training Accuracy')

plt.plot(val_acc, label='Validation Accuracy')

plt.title('Training and Validation Accuracy')

plt.legend()

plt.subplot(1, 2, 2)

plt.plot(loss, label='Training Loss')

plt.plot(val_loss, label='Validation Loss')

plt.title('Training and Validation Loss')

plt.legend()

plt.show()