给我一个在fashionmnist数据集进行分类的网络
时间: 2024-04-30 13:17:07 浏览: 22
好的,以下是一个基于卷积神经网络的fashionmnist分类器代码示例:
```
import tensorflow as tf
from tensorflow.keras import layers
# Load fashionmnist dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
# Normalize pixel values to be between 0 and 1
x_train = x_train / 255.0
x_test = x_test / 255.0
# Define the model
model = tf.keras.Sequential()
model.add(layers.Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Conv2D(64, kernel_size=(3, 3), activation="relu"))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(128, activation="relu"))
model.add(layers.Dense(10, activation="softmax"))
# Compile the model
model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
# Train the model
model.fit(x_train.reshape((-1, 28, 28, 1)), y_train, epochs=10, validation_data=(x_test.reshape((-1, 28, 28, 1)), y_test))
# Evaluate the model on test data
test_loss, test_acc = model.evaluate(x_test.reshape((-1, 28, 28, 1)), y_test)
print(f"Test loss: {test_loss}, Test accuracy: {test_acc}")
```
这个模型使用了两个卷积层和一个全连接层。它能够将28x28像素的fashionmnist图像分类成10个不同的类别。