tensorflow.keras.sequential.add
时间: 2023-10-31 16:09:52 浏览: 53
The `add()` method in `tensorflow.keras.Sequential` class is used to add layers to the neural network model sequentially. Each layer is added one after the other in the order they are specified.
For example, to create a simple neural network with two hidden layers, we can use the `add()` method to add each layer to the model:
```python
import tensorflow as tf
model = tf.keras.Sequential()
# Adding the first hidden layer with 64 units and ReLU activation function
model.add(tf.keras.layers.Dense(64, activation='relu'))
# Adding the second hidden layer with 32 units and ReLU activation function
model.add(tf.keras.layers.Dense(32, activation='relu'))
# Adding the output layer with 10 units and softmax activation function
model.add(tf.keras.layers.Dense(10, activation='softmax'))
```
In this example, we first create an empty `Sequential` model and then use the `add()` method to add three layers to it. The first two layers are hidden layers with ReLU activation functions, and the last layer is the output layer with softmax activation function.