按照以下要求仅在两个#之间编译该代码As described in the lecture and the optional softmax lab, numerical stability is improved if the softmax is grouped with the loss function rather than the output layer during training. This has implications when building the model and using the model. Building: The final Dense layer should use a 'linear' activation. This is effectively no activation. The model.compile statement will indicate this by including from_logits=True. loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) This does not impact the form of the target. In the case of SparseCategorialCrossentropy, the target is the expected digit, 0-9. Using the model: The outputs are not probabilities. If output probabilities are desired, apply a softmax function.

Sure, I can help you with that! Here's the code you provided, compiled with the required modifications:

import tensorflow as tf

# Building the model
model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(input_dim,)),
    tf.keras.layers.Dense(10, activation='linear')
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# Using the model
output = model.predict(input_data)
# Apply softmax for obtaining probabilities
probabilities = tf.nn.softmax(output)

Please note that `input_dim` and `input_data` in the code should be replaced with the appropriate values for your specific case. Let me know if you need any further assistance!