多层感知机MLP （tensorflow 2.1）

和svm差不多，感知机应该算svm的前身吧

import tensorflow as tf
import numpy as np

class MNISTLoader():
    def __init__(self):
        mnist = tf.keras.datasets.mnist
        (self.train_data, self.train_label), (self.test_data, self.test_label) = mnist.load_data()
        # MNIST中的图像默认为uint8（0-255的数字）。以下代码将其归一化到0-1之间的浮点数，并在最后增加一维作为颜色通道
        self.train_data = np.expand_dims(self.train_data.astype(np.float32) / 255.0, axis=-1)      # [60000, 28, 28, 1]
        self.test_data = np.expand_dims(self.test_data.astype(np.float32) / 255.0, axis=-1)        # [10000, 28, 28, 1]
        self.train_label = self.train_label.astype(np.int32)    # [60000]
        self.test_label = self.test_label.astype(np.int32)      # [10000]
        self.num_train_data, self.num_test_data = self.train_data.shape[0], self.test_data.shape[0]

    def get_batch(self, batch_size):
        # 从数据集中随机取出batch_size个元素并返回
        index = np.random.randint(0, np.shape(self.train_data)[0], batch_size)
        return self.train_data[index, :], self.train_label[index]

# tf.keras.layers.Dense(
#     units, activation=None, use_bias=True, kernel_initializer='glorot_uniform',
#     bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None,
#     activity_regularizer=None, kernel_constraint=None, bias_constraint=None,
#     **kwargs
# )
# units: Positive integer, dimensionality of the output space.
# activation: Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x).

class MLP(tf.keras.Model):
    def __init__(self):
        super().__init__()
        self.flatten = tf.keras.layers.Flatten()    # Flatten层将除第一维（batch_size）以外的维度展平
        self.dense1 = tf.keras.layers.Dense(units=100, activation=tf.nn.relu)
        self.dense2 = tf.keras.layers.Dense(units=10)

    def call(self, inputs):         # [batch_size, 28, 28, 1]
        x = self.flatten(inputs)    # [batch_size, 784]
        x = self.dense1(x)          # [batch_size, 100]
        x = self.dense2(x)          # [batch_size, 10]
        output = tf.nn.softmax(x)
        return output

num_epochs = 5
batch_size = 50
learning_rate = 0.001

model = MLP()
data_loader = MNISTLoader()
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)

num_batches = int(data_loader.num_train_data // batch_size * num_epochs)
for batch_index in range(num_batches):
    X, y = data_loader.get_batch(batch_size) #从数据集中随机选一部分数据
    with tf.GradientTape() as tape:
        y_pred = model(X) #得到预测值
        loss = tf.keras.losses.sparse_categorical_crossentropy(y_true=y, y_pred=y_pred)#计算loss
        loss = tf.reduce_mean(loss)
        print("batch %d: loss %f" % (batch_index, loss.numpy()))
    grads = tape.gradient(loss, model.variables) #calc grads
    optimizer.apply_gradients(grads_and_vars=zip(grads, model.variables)) #update grads

#tf.keras.metrics.SparseCategoricalAccuracy是一个评估器
sparse_categorical_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()
num_batches = int(data_loader.num_test_data // batch_size)
for batch_index in range(num_batches):
    start_index, end_index = batch_index * batch_size, (batch_index + 1) * batch_size
    #model.predict 输入测试数据,输出预测结果
    y_pred = model.predict(data_loader.test_data[start_index: end_index])
    sparse_categorical_accuracy.update_state(y_true=data_loader.test_label[start_index: end_index], y_pred=y_pred)
print("test accuracy: %f" % sparse_categorical_accuracy.result())

参考链接：https://tf.wiki/zh/basic/models.html（这本书挺好的，实践力很强）