给专业人员的 TensorFlow 2 入门教程

Init signature: layers.Conv2D(*args, **kwargs)
Docstring:
2D convolution layer (e.g. spatial convolution over images).

This layer creates a convolution kernel that is convolved
with the layer input to produce a tensor of
outputs. 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.
When using this layer as the first layer in a model,
provide the keyword argument input_shape
(tuple of integers or None, does not include the sample axis),
e.g. input_shape=(128, 128, 3) for 128x128 RGB pictures
in data_format="channels_last". You can use None when
a dimension has variable size.
Examples:
>>> # The inputs are 28x28 RGB images with channels_last and the batch
>>> # size is 4.
>>> input_shape = (4, 28, 28, 3)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv2D(
… 2, 3, activation=’relu’, input_shape=input_shape[1:])(x)
>>> print(y.shape)
(4, 26, 26, 2)
>>> # With dilation_rate as 2.
>>> input_shape = (4, 28, 28, 3)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv2D(
… 2, 3, activation=’relu’, dilation_rate=2, input_shape=input_shape[1:])(x)
>>> print(y.shape)
(4, 24, 24, 2)
>>> # With padding as “same”.
>>> input_shape = (4, 28, 28, 3)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv2D(
… 2, 3, activation=’relu’, padding=”same”, input_shape=input_shape[1:])(x)
>>> print(y.shape)
(4, 28, 28, 2)
>>> # With extended batch shape [4, 7]:
>>> input_shape = (4, 7, 28, 28, 3)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv2D(
… 2, 3, activation=’relu’, input_shape=input_shape[2:])(x)
>>> print(y.shape)
(4, 7, 26, 26, 2)
Args:
  filters: Integer, the dimensionality of the output space (i.e. the number of
    output filters in the convolution).
  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.
  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.
  padding: one of "valid" or "same" (case-insensitive).
    "valid" means no padding. "same" results in padding with zeros evenly
    to the left/right or up/down of the input. When padding="same" and
    strides=1, the output has the same size as the input.
  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_size, height, width, channels) while
    channels_first corresponds to inputs with shape (batch_size, 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.
  groups: A positive integer specifying the number of groups in which the
    input is split along the channel axis. Each group is convolved separately
    with filters/ groups filters. The output is the concatenation of all
    the groups results along the channel axis. Input channels and filters
    must both be divisible by groups.
  activation: Activation function to use. If you don’t specify anything, no
    activation is applied (see keras.activations).
  use_bias: Boolean, whether the layer uses a bias vector.
  kernel_initializer: Initializer for the kernel weights matrix (see
    keras.initializers). Defaults to ‘glorot_uniform’.
  bias_initializer: Initializer for the bias vector (see
    keras.initializers). Defaults to ‘zeros’.
  kernel_regularizer: Regularizer function applied to the kernel weights
    matrix (see keras.regularizers).
  bias_regularizer: Regularizer function applied to the bias vector (see
    keras.regularizers).
  activity_regularizer: Regularizer function applied to the output of the
    layer (its “activation”) (see keras.regularizers).
  kernel_constraint: Constraint function applied to the kernel matrix (see
    keras.constraints).
  bias_constraint: Constraint function applied to the bias vector (see
    keras.constraints).
Input shape:
  4+D tensor with shape: batch_shape + (channels, rows, cols) if
    data_format='channels_first'
  or 4+D tensor with shape: batch_shape + (rows, cols, channels) if
    data_format='channels_last'.
Output shape:
  4+D tensor with shape: batch_shape + (filters, new_rows, new_cols) if
  data_format='channels_first' or 4+D tensor with shape: batch_shape +     (new_rows, new_cols, filters) if data_format='channels_last'.  rows
    and cols values might have changed due to padding.
Returns:
  A tensor of rank 4+ representing
  activation(conv2d(inputs, kernel) + bias).
Raises:
  ValueError: if padding is "causal".
  ValueError: when both strides> 1 and dilation_rate> 1.
File:           ~/venv/venv-py38-tf2/lib/python3.8/site-packages/keras/layers/convolutional.py
Type:           type
Subclasses:     Conv2DTranspose, Conv2D