tf.keras.layers.Conv2D——tf2.1 Document

参考自tf2.1官方文档:
https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D
(笔者时间紧迫，暂时只将常用的参数记录，某若有闲时再补)

---

2D convolution layer (e.g. spatial convolution over images).
该函数用于指明Keras中的2D卷积层.

tf.keras.layers.Conv2D( # 别被参数吓着，常用的就那么几个
    filters, 
    kernel_size, 
    strides=(1, 1), 
    padding='valid', 
    data_format=None,
    dilation_rate=(1, 1), 
    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
)

This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs.
该层创建一个卷积核，该卷积核与该层输入进行卷积以产生输出张量。

If use_bias is True, a bias vector is created and added to the outputs. Finally, if activation is not None, it is applied to the outputs as well.

如果use_bias为True，则会创建一个偏差矢量并将其添加到输出中。最后，如果 activation不是None，那么它也将应用于输出。

When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers, does not include the sample axis).
e.g. input_shape=(128, 128, 3) for 128x128 RGB pictures in data_format="channels_last".

当将此层用作模型的第一层时，请提供关键字参数input_shape (整数元组，不包括采样轴)
例如，若指定data_format="channels_last"，则input_shape=(128, 128, 3)用于中的128x128 RGB图片。

---

Arguments:

• filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
• $filters$: 整型，输出空间的维数（即卷积中输出filter的数量）

• kernel_size: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
• $kernel\_size$: 一个整数，或者2个元素的元组/列表，指定2D卷积窗口的高度和宽度。
  当时单个整数时，给所有卷积核的宽高指定相同的值。

• strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.

• $strides$: 一个整数，或2个整数的元组/列表，指定沿高度和宽度方向卷积的步长。可以是单个整数，给两个方向的步长指定相同的值。
  指定任何步长 != 1与指定任何dilation_rate值 != 1 不兼容。

• padding: one of “valid” or “same” (case-insensitive).
• $padding$:可以选择 $"valid"$或 $"same"$（不区分大小写）。

• data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be “channels_last”.

• dilation_rate: an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any stride value != 1.

• activation: Activation function to use. If you don’t specify anything, no activation is applied (ie. “linear” activation: a(x) = x).
• $activation$：指定要使用的激活函数。如果参数不指定激活函数，则将不使用激活函数，即"线性激活函数"： $a(x) = x$

• use_bias: Boolean, whether the layer uses a bias vector.
• $use\_bias$: 布尔值，指定该层是否使用偏置向量
• kernel_initializer: Initializer for the kernel weights matrix.
• bias_initializer: Initializer for the bias vector.
• kernel_regularizer: Regularizer function applied to the kernel weights matrix.
• bias_regularizer: Regularizer function applied to the bias vector.
• activity_regularizer: Regularizer function applied to the output of the layer (its “activation”)…
• kernel_constraint: Constraint function applied to the kernel matrix.
• bias_constraint: Constraint function applied to the bias vector.