如何在tf.keras.layers中添加Dropout层

在tf.keras高级API中添加Dropout层的方式与早期的Tensorflow+keras的方式有所不同。典型的案例如下，直接在tf.keras.layers.Sequential中相应的位置添加tf.keras.layers.Dropout(ratio)即可，使用非常方便：

dropout_model = tf.keras.Sequential([tf.keras.layers.Dense(512, activation='elu', input_shape=(FEATURES,)),tf.keras.layers.Dropout(0.5),tf.keras.layers.Dense(512, activation='elu'),tf.keras.layers.Dropout(0.5),tf.keras.layers.Dense(512, activation='elu'),tf.keras.layers.Dropout(0.5),tf.keras.layers.Dense(512, activation='elu'),tf.keras.layers.Dropout(0.5),tf.keras.layers.Dense(1)
])

案例参考如下链接：https://tensorflow.google.cn/tutorials/keras/overfit_and_underfit

tf.keras.layers.Dropout的官方介绍归纳如下：

tf.keras.layers.Dropout(rate, noise_shape=None, seed=None, **kwargs
)

Arguments:

• rate: Float between 0 and 1. Fraction of the input units to drop.
• noise_shape: 1D integer tensor representing the shape of the binary dropout mask that will be multiplied with the input. For instance, if your inputs have shape (batch_size, timesteps, features) and you want the dropout mask to be the same for all timesteps, you can use noise_shape=(batch_size, 1, features).
• seed: A Python integer to use as random seed.

Call arguments:

• inputs: Input tensor (of any rank).
• training: Python boolean indicating whether the layer should behave in training mode (adding dropout) or in inference mode (doing nothing).

参考： https://tensorflow.google.cn/api_docs/python/tf/keras/layers/Dropout?hl=en#used-in-the-notebooks