首页多层感知机 python实现 tensorflow

多层感知机 python实现 tensorflow

时间: 2023-11-27 17:46:51 浏览: 116

以下是使用TensorFlow库在Python中实现多层感知机的代码示例： ```python # 导入必要的库 import tensorflow as tf import numpy as np from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Flatten, Dense, Activation import matplotlib.pyplot as plt # 下载数据集 mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() # 数据预处理 x_train, x_test = x_train / 255.0, x_test / 255.0 # 定义模型 model = Sequential([ Flatten(input_shape=(28, 28)), Dense(128, activation='relu'), Dense(10, activation='softmax') ]) # 编译模型 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # 训练模型 history = model.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test)) # 可视化训练过程 plt.plot(history.history['accuracy'], label='accuracy') plt.plot(history.history['val_accuracy'], label='val_accuracy') plt.xlabel('Epoch') plt.ylabel('Accuracy') plt.ylim([0.5, 1]) plt.legend(loc='lower right') # 评估模型 test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2) print('Test accuracy:', test_acc) ```

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