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Add testable docstrings to conv2d.

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PiperOrigin-RevId: 283990710
Change-Id: I79039b1b2fb5b09099dec9c184314ebdcfc2b903
This commit is contained in:
Yanhui Liang 2019-12-05 09:32:27 -08:00 committed by TensorFlower Gardener
parent c04ca8b858
commit f575856f03
1 changed files with 51 additions and 8 deletions
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59
tensorflow/python/keras/layers/convolutional.py
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@ -449,6 +449,33 @@ class Conv2D(Conv):
e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures
in `data_format="channels_last"`.
Examples:
>>> # Small convolutional model for 128x128 RGB images with `channels_last`
>>> input_shape = (32, 128, 128, 3)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv2D(
... 2, 3, activation='relu', input_shape=input_shape)(x)
>>> print(y.shape)
(32, 126, 126, 2)
>>> # With `dilation_rate` as 2.
>>> input_shape = (32, 128, 128, 3)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv2D(
... 2, 3, activation='relu', dilation_rate=2, input_shape=input_shape)(x)
>>> print(y.shape)
(32, 124, 124, 2)
>>> # With `padding` as "same".
>>> input_shape = (32, 128, 128, 3)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv2D(
... 2, 3, activation='relu', padding="same", input_shape=input_shape)(x)
>>> print(y.shape)
(32, 128, 128, 2)
Arguments:
filters: Integer, the dimensionality of the output space
(i.e. the number of output filters in the convolution).
@ -481,17 +508,25 @@ class Conv2D(Conv):
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`).
(ie. "linear" activation: `a(x) = x`). Check `keras.activations` for
available activation functions.
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix.
bias_initializer: Initializer for the bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix. Check
`keras.initializers` for available initializers.
bias_initializer: Initializer for the bias vector. Check
`keras.initializers` for available initializers.
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix.
bias_regularizer: Regularizer function applied to the bias vector.
the `kernel` weights matrix. Check
`keras.regularizers` for available regularizers.
bias_regularizer: Regularizer function applied to the bias vector. Check
`keras.regularizers` for available regularizers.
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.
the output of the layer (its "activation"). Check
`keras.regularizers` for available regularizers.
kernel_constraint: Constraint function applied to the kernel matrix. Check
`keras.constraints` for available constraints.
bias_constraint: Constraint function applied to the bias vector. Check
`keras.constraints` for available constraints.
Input shape:
4D tensor with shape:
@ -505,6 +540,14 @@ class Conv2D(Conv):
or 4D tensor with shape:
`(samples, 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.
"""
def __init__(self,