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搭建神经网络块是一种常见的做法#xff0c;它可以帮助你更好地组织和复用网络结构。神经网络块可以是一些相对独立的模块#xff0c;例如卷积块、全连接块等#xff0c;用于构建更复杂的网络架构。
代码实现
import numpy as np
import tensorflow as tf
from tens…概念
搭建神经网络块是一种常见的做法它可以帮助你更好地组织和复用网络结构。神经网络块可以是一些相对独立的模块例如卷积块、全连接块等用于构建更复杂的网络架构。
代码实现
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers# 定义一个卷积块
def convolutional_block(x, num_filters, kernel_size, pool_size):x layers.Conv2D(num_filters, kernel_size, activationrelu, paddingsame)(x)x layers.MaxPooling2D(pool_size)(x)return x# 构建神经网络模型
def build_model():inputs layers.Input(shape(28, 28, 1)) # 输入数据为28x28的灰度图像x convolutional_block(inputs, num_filters32, kernel_size(3, 3), pool_size(2, 2))x convolutional_block(x, num_filters64, kernel_size(3, 3), pool_size(2, 2))x layers.Flatten()(x)x layers.Dense(128, activationrelu)(x)outputs layers.Dense(10, activationsoftmax)(x) # 输出层10个类别model keras.Model(inputs, outputs)return model# 加载数据
(x_train, y_train), (x_test, y_test) keras.datasets.mnist.load_data()
x_train np.expand_dims(x_train, axis-1).astype(float32) / 255.0
x_test np.expand_dims(x_test, axis-1).astype(float32) / 255.0
y_train keras.utils.to_categorical(y_train, num_classes10)
y_test keras.utils.to_categorical(y_test, num_classes10)# 构建模型
model build_model()# 编译模型
model.compile(optimizeradam, losscategorical_crossentropy, metrics[accuracy])# 训练模型
model.fit(x_train, y_train, batch_size64, epochs10, validation_split0.1)# 评估模型
test_loss, test_accuracy model.evaluate(x_test, y_test)
print(Test Loss:, test_loss)
print(Test Accuracy:, test_accuracy)
