3d unet 代码
    3DUNet是一种用于医学图像分割的神经网络模型。其结构与2DUNet相似，但能够处理3D图像。在医学影像中应用广泛，如MRI，CT等。以下是3D UNet的代码实现，供参考。
    ``` python
    import tensorflow as tf
    def conv3d_block(input_tensor, n_filters, kernel_size=3, batchnorm=True):
    x = input_tensor
    for i in range(2):
    x = tf.keras.layers.Conv3D(filters=n_filters, kernel_size=(kernel_size, kernel_size, kernel_size), kernel_initializer='he_normal', padding='same')(x)
    if batchnorm:
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)
    return x
    def encoder_block(inputs, n_filters, pool_size, dropout_rate=0.3, batchnorm=True):
    f = conv3d_block(inputs, n_filters=n_filters, batchnorm=batchnorm)
    p = tf.keras.layers.MaxPooling3D(pool_size=(pool_size, pool_size, pool_size))(f)
    p = tf.keras.layers.Dropout(dropout_rate)(p)
    return f, p
    def decoder_block(inputs, conv_output, n_filters, kernel_size=3, strides=3, dropout_rate=0.3, batchnorm=True):
    u = tf.keras.layers.Conv3DTranspose(n_filters, (kernel_size, kernel_size, kernel_size),
strides=(strides, strides, strides), padding='same')(inputs)
    c = tf.atenate([u, conv_output])
    c = tf.keras.layers.Dropout(dropout_rate)(c)
    c = conv3d_block(c, n_filters=n_filters, kernel_size=kernel_size, batchnorm=batchnorm)
    return c
    def build_3d_unet(input_shape):
    inputs = tf.keras.layers.Input(input_shape)
    # Encoder
    f1, p1 = encoder_block(inputs, n_filters=32, pool_size=2, dropout_rate=0.3, batchnorm=True)
    f2, p2 = encoder_block(p1, n_filters=64, pool_size=2, dropout_rate=0.3, batchnorm=True)
    f3, p3 = encoder_block(p2, n_filters=128, pool_size=2, dropout_rate=0.3, batchnorm=True)
    f4, p4 = encoder_block(p3, n_filters=256, pool_size=2, dropout_rate=0.3, batchnorm=True)
    # Bridge
    b1 = conv3d_block(p4, n_filters=512, kernel_size=3, batchnorm=True)
    b2 = conv3d_block(b1, n_filters=512, kernel_size=3, batchnorm=True)
    # Decoder
    c1 = decoder_block(b2, conv_output=f4, n_filters=256, kernel_size=3, strides=2, dropout_rate=0.3, batchnorm=True)
    c2 = decoder_block(c1, conv_output=f3, n_filters=128, kernel_size=3, strides=2, dropout_rate=0.3, batchnorm=True)
    c3 = decoder_block(c2, conv_output=f2, n_filters=64, kernel_size=3, strides=2, dropout_rate=0.3, batchnorm=True)
    c4 = decoder_block(c3, conv_output=f1, n_filters=32, kernel_size=3, strides=2, dropout_rate=0.3, batchnorm=True)
    # Output
    outputs = tf.keras.layers.Conv3D(1, (1, 1, 1), activation='sigmoid')(c4)
decoder    model = dels.Model(inputs=[inputs], outputs=[outputs])
    return model
    ```
    以上是3D UNet的代码实现，由于篇幅限制，代码中省略了注释和具体的数据集处理部分。在使用时，需要根据具体的任务对代码进行修改和适配。

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