51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
import tensorflow as tf
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
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# 加载Fashion-MNIST数据集
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fashion_mnist = tf.keras.datasets.fashion_mnist
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(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()
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# 数据预处理
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X_train = X_train / 255.0 # 将像素值缩放到0-1之间
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X_test = X_test / 255.0
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# 如果使用卷积神经网络（CNN），需要调整数据形状
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X_train = X_train.reshape((X_train.shape[0], 28, 28, 1))
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X_test = X_test.reshape((X_test.shape[0], 28, 28, 1))
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# 标签数据保持不变
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print(f'Training data shape: {X_train.shape}, Training labels shape: {y_train.shape}')
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print(f'Test data shape: {X_test.shape}, Test labels shape: {y_test.shape}')
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# 构建模型
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model = Sequential([
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Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
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MaxPooling2D((2, 2)),
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Conv2D(64, (3, 3), activation='relu'),
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MaxPooling2D((2, 2)),
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Flatten(),
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Dense(128, activation='relu'),
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Dense(10, activation='softmax') # 输出层使用softmax进行10分类
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])
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# # 编译模型
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# model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
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#
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# # 训练模型
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# model.fit(X_train, y_train, epochs=15, batch_size=32, validation_split=0.2)
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early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)
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lr_schedule = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-3 * 10**(epoch / 20))
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model.compile(optimizer=tf.keras.optimizers.Adam(), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
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history = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.2, callbacks=[early_stopping, lr_schedule])
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# 评估模型
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loss, accuracy = model.evaluate(X_test, y_test)
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print(f'Test Loss: {loss}')
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print(f'Test Accuracy: {accuracy}')
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model.save('fashion_mnist_model.h5')
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