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TensorFlow 1 version
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View source on GitHub
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2D convolution layer (e.g. spatial convolution over images).
tf.keras.layers.Conv2D(
filters, kernel_size, strides=(1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1), groups=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
)
Used in the notebooks
| Used in the guide | Used in the tutorials |
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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, does not include the sample axis),
e.g. input_shape=(128, 128, 3) for 128x128 RGB pictures
in data_format="channels_last".
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)
Arguments | |
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filters
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Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). |
kernel_size
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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
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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.
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padding
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one of "valid" or "same" (case-insensitive).
"valid" means no padding. "same" results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
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data_format
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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.
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dilation_rate
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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.
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groups
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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.
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activation
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Activation function to use. If you don't specify anything, no
activation is applied (see keras.activations).
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use_bias
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Boolean, whether the layer uses a bias vector. |
kernel_initializer
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Initializer for the kernel weights matrix (see
keras.initializers).
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bias_initializer
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Initializer for the bias vector (see
keras.initializers).
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kernel_regularizer
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Regularizer function applied to the kernel weights
matrix (see keras.regularizers).
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bias_regularizer
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Regularizer function applied to the bias vector (see
keras.regularizers).
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activity_regularizer
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Regularizer function applied to the output of the
layer (its "activation") (see keras.regularizers).
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kernel_constraint
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Constraint function applied to the kernel matrix (see
keras.constraints).
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bias_constraint
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Constraint function applied to the bias vector (see
keras.constraints).
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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 | |
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A tensor of rank 4+ representing
activation(conv2d(inputs, kernel) + bias).
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Raises | |
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ValueError
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if padding is "causal".
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ValueError
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when both strides > 1 and dilation_rate > 1.
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