tensorflow v2.0入门教程——05多层感知机(MLP)

- 日理万妓 2023-06-06 12:25 15阅读 0赞

## 多层感知机 ##

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]
    
    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

## 调整超参数 ##

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)

## 梯度下降 ##

for batch_index in range(num_batches):
        X, y = data_loader.get_batch(batch_size)
        with tf.GradientTape() as tape:
            y_pred = model(X)
            loss = tf.keras.losses.sparse_categorical_crossentropy(y_true=y, y_pred=y_pred)
            loss = tf.reduce_mean(loss)
            print("batch %d: loss %f" % (batch_index, loss.numpy()))
        grads = tape.gradient(loss, model.variables)
        optimizer.apply_gradients(grads_and_vars=zip(grads, model.variables))

## 测试准确率 ##

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
        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())

test accuracy: 0.974600