用神经网络实现利用MNIST数据手写体识别和fanshion数据的衣物识别用pycharm中的tensoflow完成代码的实现

好的，这是一个比较复杂的任务，需要一些基本的机器学习和深度学习知识。以下是一个基于 TensorFlow 的代码示例，可以实现手写数字和时尚衣物的分类任务。

首先，我们需要导入必要的库和模块：

import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt

接下来，我们可以加载 MNIST 数据集和 Fashion MNIST 数据集：

mnist = keras.datasets.mnist
(x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = mnist.load_data()

fashion_mnist = keras.datasets.fashion_mnist
(x_train_fashion, y_train_fashion), (x_test_fashion, y_test_fashion) = fashion_mnist.load_data()

然后，我们需要对数据进行预处理，将像素值缩放到 0 到 1 之间，并将标签转换为 one-hot 编码：

x_train_mnist, x_test_mnist = x_train_mnist / 255.0, x_test_mnist / 255.0
x_train_fashion, x_test_fashion = x_train_fashion / 255.0, x_test_fashion / 255.0

y_train_mnist_onehot = keras.utils.to_categorical(y_train_mnist)
y_test_mnist_onehot = keras.utils.to_categorical(y_test_mnist)
y_train_fashion_onehot = keras.utils.to_categorical(y_train_fashion)
y_test_fashion_onehot = keras.utils.to_categorical(y_test_fashion)

接下来，我们可以定义模型。这里我们使用一个简单的卷积神经网络模型：

model_mnist = keras.models.Sequential([
    keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    keras.layers.MaxPooling2D((2, 2)),
    keras.layers.Conv2D(64, (3, 3), activation='relu'),
    keras.layers.MaxPooling2D((2, 2)),
    keras.layers.Conv2D(64, (3, 3), activation='relu'),
    keras.layers.Flatten(),
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])

model_fashion = keras.models.Sequential([
    keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    keras.layers.MaxPooling2D((2, 2)),
    keras.layers.Conv2D(64, (3, 3), activation='relu'),
    keras.layers.MaxPooling2D((2, 2)),
    keras.layers.Conv2D(64, (3, 3), activation='relu'),
    keras.layers.Flatten(),
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])

然后，我们可以编译模型并开始训练：

model_mnist.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

model_fashion.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

model_mnist.fit(x_train_mnist.reshape(-1, 28, 28, 1), y_train_mnist_onehot, epochs=5)
model_fashion.fit(x_train_fashion.reshape(-1, 28, 28, 1), y_train_fashion_onehot, epochs=5)

最后，我们可以评估模型的性能并进行预测：

model_mnist.evaluate(x_test_mnist.reshape(-1, 28, 28, 1), y_test_mnist_onehot)
model_fashion.evaluate(x_test_fashion.reshape(-1, 28, 28, 1), y_test_fashion_onehot)

predictions_mnist = model_mnist.predict(x_test_mnist.reshape(-1, 28, 28, 1))
predictions_fashion = model_fashion.predict(x_test_fashion.reshape(-1, 28, 28, 1))

希望这个示例代码可以帮助你开始实现手写数字和时尚衣物的分类任务。