tf.keras.layers.Dense

函数原型

tf.keras.layers.Dense(
    units, activation=None, use_bias=True, kernel_initializer='glorot_uniform',
    bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None,
    activity_regularizer=None, kernel_constraint=None, bias_constraint=None,
    **kwargs
)

Dense: output = activation(dot(input, kernel) + bias)
此外，层属性不能在层被调用一次后被修改(除了trainable属性)。

案例：

# Create a `Sequential` model and add a Dense layer as the first layer.
model = tf.keras.models.Sequential()
model.add(tf.keras.Input(shape=(16,)))
model.add(tf.keras.layers.Dense(32, activation='relu'))
# Now the model will take as input arrays of shape (None, 16)
# and output arrays of shape (None, 32).
# Note that after the first layer, you don't need to specify
# the size of the input anymore:
model.add(tf.keras.layers.Dense(32))
model.output_shape

输出：（None， 32）

Input shape:
N-D tensor with shape: (batch_size, …, input_dim). The most common situation would be a 2D input with shape (batch_size, input_dim).

Output shape:
N-D tensor with shape: (batch_size, …, units). For instance, for a 2D input with shape (batch_size, input_dim), the output would have shape (batch_size, units).