Merge pull request #39516 from lgeiger:resubmit-keras-grouped-conv
PiperOrigin-RevId: 313770233 Change-Id: I8842ffe4c2e8a2293e6b708c915b7fbb98a923de
This commit is contained in:
TensorFlower Gardener
commit
e0dc15cec6
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@ -78,6 +78,11 @@ class Conv(Layer):
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the dilation rate to use for dilated convolution.
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Currently, specifying any `dilation_rate` value != 1 is
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incompatible with specifying any `strides` value != 1.
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groups: A positive integer specifying the number of groups in which the
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input is split along the channel axis. Each group is convolved
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separately with `filters / groups` filters. The output is the
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concatenation of all the `groups` results along the channel axis.
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Input channels and `filters` must both be divisible by `groups`.
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activation: Activation function to use.
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If you don't specify anything, no activation is applied.
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use_bias: Boolean, whether the layer uses a bias.
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@ -100,13 +105,15 @@ class Conv(Layer):
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name: A string, the name of the layer.
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"""
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def __init__(self, rank,
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def __init__(self,
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rank,
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filters,
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kernel_size,
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strides=1,
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padding='valid',
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data_format=None,
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dilation_rate=1,
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groups=1,
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activation=None,
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use_bias=True,
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kernel_initializer='glorot_uniform',
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@ -128,6 +135,11 @@ class Conv(Layer):
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if filters is not None and not isinstance(filters, int):
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filters = int(filters)
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self.filters = filters
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self.groups = groups or 1
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if filters is not None and filters % self.groups != 0:
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raise ValueError(
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'The number of filters must be evenly divisible by the number of '
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'groups. Received: groups={}, filters={}'.format(groups, filters))
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self.kernel_size = conv_utils.normalize_tuple(
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kernel_size, rank, 'kernel_size')
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if not all(self.kernel_size):
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@ -155,7 +167,14 @@ class Conv(Layer):
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def build(self, input_shape):
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input_shape = tensor_shape.TensorShape(input_shape)
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input_channel = self._get_input_channel(input_shape)
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kernel_shape = self.kernel_size + (input_channel, self.filters)
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if input_channel % self.groups != 0:
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raise ValueError(
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'The number of input channels must be evenly divisible by the number '
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'of groups. Received groups={}, but the input has {} channels '
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'(full input shape is {}).'.format(self.groups, input_channel,
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input_shape))
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kernel_shape = self.kernel_size + (input_channel // self.groups,
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self.filters)
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self.kernel = self.add_weight(
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name='kernel',
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@ -250,22 +269,38 @@ class Conv(Layer):
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def get_config(self):
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config = {
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'filters': self.filters,
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'kernel_size': self.kernel_size,
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'strides': self.strides,
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'padding': self.padding,
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'data_format': self.data_format,
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'dilation_rate': self.dilation_rate,
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'activation': activations.serialize(self.activation),
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'use_bias': self.use_bias,
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'kernel_initializer': initializers.serialize(self.kernel_initializer),
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'bias_initializer': initializers.serialize(self.bias_initializer),
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'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
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'bias_regularizer': regularizers.serialize(self.bias_regularizer),
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'filters':
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self.filters,
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'kernel_size':
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self.kernel_size,
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'strides':
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self.strides,
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'padding':
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self.padding,
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'data_format':
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self.data_format,
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'dilation_rate':
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self.dilation_rate,
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'groups':
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self.groups,
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'activation':
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activations.serialize(self.activation),
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'use_bias':
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self.use_bias,
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'kernel_initializer':
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initializers.serialize(self.kernel_initializer),
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'bias_initializer':
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initializers.serialize(self.bias_initializer),
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'kernel_regularizer':
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regularizers.serialize(self.kernel_regularizer),
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'bias_regularizer':
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regularizers.serialize(self.bias_regularizer),
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'activity_regularizer':
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regularizers.serialize(self.activity_regularizer),
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'kernel_constraint': constraints.serialize(self.kernel_constraint),
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'bias_constraint': constraints.serialize(self.bias_constraint)
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'kernel_constraint':
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constraints.serialize(self.kernel_constraint),
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'bias_constraint':
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constraints.serialize(self.bias_constraint)
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}
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base_config = super(Conv, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@ -371,6 +406,11 @@ class Conv1D(Conv):
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the dilation rate to use for dilated convolution.
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Currently, specifying any `dilation_rate` value != 1 is
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incompatible with specifying any `strides` value != 1.
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groups: A positive integer specifying the number of groups in which the
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input is split along the channel axis. Each group is convolved
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separately with `filters / groups` filters. The output is the
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concatenation of all the `groups` results along the channel axis.
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Input channels and `filters` must both be divisible by `groups`.
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activation: Activation function to use.
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If you don't specify anything, no activation is applied (
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see `keras.activations`).
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@ -413,6 +453,7 @@ class Conv1D(Conv):
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padding='valid',
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data_format='channels_last',
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dilation_rate=1,
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groups=1,
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activation=None,
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use_bias=True,
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kernel_initializer='glorot_uniform',
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@ -431,6 +472,7 @@ class Conv1D(Conv):
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padding=padding,
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data_format=data_format,
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dilation_rate=dilation_rate,
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groups=groups,
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activation=activations.get(activation),
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use_bias=use_bias,
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kernel_initializer=initializers.get(kernel_initializer),
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@ -517,6 +559,11 @@ class Conv2D(Conv):
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all spatial dimensions.
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Currently, specifying any `dilation_rate` value != 1 is
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incompatible with specifying any stride value != 1.
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groups: A positive integer specifying the number of groups in which the
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input is split along the channel axis. Each group is convolved
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separately with `filters / groups` filters. The output is the
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concatenation of all the `groups` results along the channel axis.
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Input channels and `filters` must both be divisible by `groups`.
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activation: Activation function to use.
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If you don't specify anything, no activation is applied (
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see `keras.activations`).
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@ -566,6 +613,7 @@ class Conv2D(Conv):
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padding='valid',
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data_format=None,
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dilation_rate=(1, 1),
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groups=1,
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activation=None,
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use_bias=True,
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kernel_initializer='glorot_uniform',
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@ -584,6 +632,7 @@ class Conv2D(Conv):
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padding=padding,
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data_format=data_format,
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dilation_rate=dilation_rate,
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groups=groups,
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activation=activations.get(activation),
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use_bias=use_bias,
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kernel_initializer=initializers.get(kernel_initializer),
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@ -655,6 +704,11 @@ class Conv3D(Conv):
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all spatial dimensions.
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Currently, specifying any `dilation_rate` value != 1 is
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incompatible with specifying any stride value != 1.
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groups: A positive integer specifying the number of groups in which the
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input is split along the channel axis. Each group is convolved
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separately with `filters / groups` filters. The output is the
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concatenation of all the `groups` results along the channel axis.
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Input channels and `filters` must both be divisible by `groups`.
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activation: Activation function to use.
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If you don't specify anything, no activation is applied (
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see `keras.activations`).
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@ -710,6 +764,7 @@ class Conv3D(Conv):
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padding='valid',
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data_format=None,
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dilation_rate=(1, 1, 1),
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groups=1,
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activation=None,
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use_bias=True,
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kernel_initializer='glorot_uniform',
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@ -728,6 +783,7 @@ class Conv3D(Conv):
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padding=padding,
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data_format=data_format,
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dilation_rate=dilation_rate,
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groups=groups,
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activation=activations.get(activation),
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use_bias=use_bias,
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kernel_initializer=initializers.get(kernel_initializer),
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@ -26,6 +26,9 @@ from tensorflow.python.eager import context
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from tensorflow.python.eager import def_function
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from tensorflow.python.framework import tensor_spec
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import nn
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from tensorflow.python.ops import random_ops
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from tensorflow.python.keras import keras_parameterized
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from tensorflow.python.keras import testing_utils
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from tensorflow.python.platform import test
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@ -47,20 +50,41 @@ class Conv1DTest(keras_parameterized.TestCase):
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expected_output_shape=expected_output_shape)
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@parameterized.named_parameters(
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('padding_valid', {'padding': 'valid'}, (None, 5, 2)),
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('padding_same', {'padding': 'same'}, (None, 7, 2)),
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('padding_same_dilation_2', {'padding': 'same', 'dilation_rate': 2},
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(None, 7, 2)),
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('padding_same_dilation_3', {'padding': 'same', 'dilation_rate': 3},
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(None, 7, 2)),
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('padding_causal', {'padding': 'causal'}, (None, 7, 2)),
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('strides', {'strides': 2}, (None, 3, 2)),
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('dilation_rate', {'dilation_rate': 2}, (None, 3, 2)),
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('padding_valid', {
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'padding': 'valid'
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}, (None, 5, 2)),
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('padding_same', {
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'padding': 'same'
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}, (None, 7, 2)),
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('padding_same_dilation_2', {
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'padding': 'same',
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'dilation_rate': 2
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}, (None, 7, 2)),
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('padding_same_dilation_3', {
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'padding': 'same',
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'dilation_rate': 3
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}, (None, 7, 2)),
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('padding_causal', {
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'padding': 'causal'
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}, (None, 7, 2)),
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('strides', {
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'strides': 2
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}, (None, 3, 2)),
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('dilation_rate', {
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'dilation_rate': 2
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}, (None, 3, 2)),
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# Only runs on GPU with CUDA, groups are not supported on CPU.
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# https://github.com/tensorflow/tensorflow/issues/29005
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('group', {
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'groups': 3,
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'filters': 6
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}, (None, 5, 6), True),
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)
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def test_conv1d(self, kwargs, expected_output_shape):
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kwargs['filters'] = 2
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def test_conv1d(self, kwargs, expected_output_shape, requires_gpu=False):
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kwargs['filters'] = kwargs.get('filters', 2)
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kwargs['kernel_size'] = 3
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self._run_test(kwargs, expected_output_shape)
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if not requires_gpu or test.is_gpu_available(cuda_only=True):
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self._run_test(kwargs, expected_output_shape)
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def test_conv1d_regularizers(self):
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kwargs = {
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@ -148,20 +172,38 @@ class Conv2DTest(keras_parameterized.TestCase):
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expected_output_shape=expected_output_shape)
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@parameterized.named_parameters(
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('padding_valid', {'padding': 'valid'}, (None, 5, 4, 2)),
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('padding_same', {'padding': 'same'}, (None, 7, 6, 2)),
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('padding_same_dilation_2', {'padding': 'same', 'dilation_rate': 2},
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(None, 7, 6, 2)),
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('strides', {'strides': (2, 2)}, (None, 3, 2, 2)),
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('dilation_rate', {'dilation_rate': (2, 2)}, (None, 3, 2, 2)),
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('padding_valid', {
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'padding': 'valid'
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}, (None, 5, 4, 2)),
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('padding_same', {
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'padding': 'same'
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}, (None, 7, 6, 2)),
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('padding_same_dilation_2', {
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'padding': 'same',
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'dilation_rate': 2
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}, (None, 7, 6, 2)),
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('strides', {
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'strides': (2, 2)
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}, (None, 3, 2, 2)),
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('dilation_rate', {
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'dilation_rate': (2, 2)
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}, (None, 3, 2, 2)),
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# Only runs on GPU with CUDA, channels_first is not supported on CPU.
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# TODO(b/62340061): Support channels_first on CPU.
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('data_format', {'data_format': 'channels_first'}),
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('data_format', {
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'data_format': 'channels_first'
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}, None, True),
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# Only runs on GPU with CUDA, groups are not supported on CPU.
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# https://github.com/tensorflow/tensorflow/issues/29005
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('group', {
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'groups': 3,
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'filters': 6
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}, (None, 5, 4, 6), True),
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)
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def test_conv2d(self, kwargs, expected_output_shape=None):
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kwargs['filters'] = 2
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def test_conv2d(self, kwargs, expected_output_shape=None, requires_gpu=False):
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kwargs['filters'] = kwargs.get('filters', 2)
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kwargs['kernel_size'] = (3, 3)
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if 'data_format' not in kwargs or test.is_gpu_available(cuda_only=True):
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if not requires_gpu or test.is_gpu_available(cuda_only=True):
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self._run_test(kwargs, expected_output_shape)
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def test_conv2d_regularizers(self):
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@ -208,7 +250,7 @@ class Conv2DTest(keras_parameterized.TestCase):
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@keras_parameterized.run_all_keras_modes
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class Conv3DTest(keras_parameterized.TestCase):
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def _run_test(self, kwargs, expected_output_shape):
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def _run_test(self, kwargs, expected_output_shape, validate_training=True):
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num_samples = 2
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stack_size = 3
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num_row = 7
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@ -220,22 +262,41 @@ class Conv3DTest(keras_parameterized.TestCase):
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keras.layers.Conv3D,
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kwargs=kwargs,
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input_shape=(num_samples, depth, num_row, num_col, stack_size),
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expected_output_shape=expected_output_shape)
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expected_output_shape=expected_output_shape,
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validate_training=validate_training)
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@parameterized.named_parameters(
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('padding_valid', {'padding': 'valid'}, (None, 3, 5, 4, 2)),
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('padding_same', {'padding': 'same'}, (None, 5, 7, 6, 2)),
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('strides', {'strides': (2, 2, 2)}, (None, 2, 3, 2, 2)),
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('dilation_rate', {'dilation_rate': (2, 2, 2)}, (None, 1, 3, 2, 2)),
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('padding_valid', {
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'padding': 'valid'
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}, (None, 3, 5, 4, 2)),
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('padding_same', {
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'padding': 'same'
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}, (None, 5, 7, 6, 2)),
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('strides', {
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'strides': (2, 2, 2)
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}, (None, 2, 3, 2, 2)),
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('dilation_rate', {
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'dilation_rate': (2, 2, 2)
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}, (None, 1, 3, 2, 2)),
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# Only runs on GPU with CUDA, channels_first is not supported on CPU.
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# TODO(b/62340061): Support channels_first on CPU.
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('data_format', {'data_format': 'channels_first'}),
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('data_format', {
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'data_format': 'channels_first'
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}, None, True),
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# Only runs on GPU with CUDA, groups are not supported on CPU.
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# https://github.com/tensorflow/tensorflow/issues/29005
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('group', {
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'groups': 3,
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'filters': 6
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}, (None, 3, 5, 4, 6), True),
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)
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def test_conv3d(self, kwargs, expected_output_shape=None):
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kwargs['filters'] = 2
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def test_conv3d(self, kwargs, expected_output_shape=None, requires_gpu=False):
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kwargs['filters'] = kwargs.get('filters', 2)
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kwargs['kernel_size'] = (3, 3, 3)
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if 'data_format' not in kwargs or test.is_gpu_available(cuda_only=True):
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self._run_test(kwargs, expected_output_shape)
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# train_on_batch currently fails with XLA enabled on GPUs
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test_training = 'groups' not in kwargs or not test_util.is_xla_enabled()
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if not requires_gpu or test.is_gpu_available(cuda_only=True):
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self._run_test(kwargs, expected_output_shape, test_training)
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def test_conv3d_regularizers(self):
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kwargs = {
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@ -298,6 +359,57 @@ class Conv3DTest(keras_parameterized.TestCase):
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input_data=input_data)
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class GroupedConvTest(keras_parameterized.TestCase):
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@parameterized.named_parameters(
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('Conv1D', keras.layers.Conv1D),
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('Conv2D', keras.layers.Conv2D),
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('Conv3D', keras.layers.Conv3D),
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)
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def test_group_conv_incorrect_use(self, layer):
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with self.assertRaisesRegexp(ValueError, 'The number of filters'):
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layer(16, 3, groups=3)
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with self.assertRaisesRegexp(ValueError, 'The number of input channels'):
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layer(16, 3, groups=4).build((32, 12, 12, 3))
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@parameterized.named_parameters(
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('Conv1D', keras.layers.Conv1D, (32, 12, 32)),
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('Conv2D', keras.layers.Conv2D, (32, 12, 12, 32)),
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('Conv3D', keras.layers.Conv3D, (32, 12, 12, 12, 32)),
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)
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def test_group_conv(self, layer_cls, input_shape):
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if test.is_gpu_available(cuda_only=True):
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with test_util.use_gpu():
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inputs = random_ops.random_uniform(shape=input_shape)
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layer = layer_cls(16, 3, groups=4, use_bias=False)
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layer.build(input_shape)
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input_slices = array_ops.split(inputs, 4, axis=-1)
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weight_slices = array_ops.split(layer.kernel, 4, axis=-1)
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expected_outputs = array_ops.concat([
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nn.convolution_v2(inputs, weights)
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for inputs, weights in zip(input_slices, weight_slices)
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],
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axis=-1)
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self.assertAllClose(layer(inputs), expected_outputs, rtol=1e-5)
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|
||||
def test_group_conv_depthwise(self):
|
||||
if test.is_gpu_available(cuda_only=True):
|
||||
with test_util.use_gpu():
|
||||
inputs = random_ops.random_uniform(shape=(3, 27, 27, 32))
|
||||
|
||||
layer = keras.layers.Conv2D(32, 3, groups=32, use_bias=False)
|
||||
layer.build((3, 27, 27, 32))
|
||||
|
||||
weights_dw = array_ops.reshape(layer.kernel, [3, 3, 32, 1])
|
||||
expected_outputs = nn.depthwise_conv2d(
|
||||
inputs, weights_dw, strides=[1, 1, 1, 1], padding='VALID')
|
||||
|
||||
self.assertAllClose(layer(inputs), expected_outputs, rtol=1e-5)
|
||||
|
||||
|
||||
@keras_parameterized.run_all_keras_modes
|
||||
class Conv1DTransposeTest(keras_parameterized.TestCase):
|
||||
|
||||
|
|
|
|||
|
|
@ -1242,12 +1242,12 @@ class Convolution(object):
|
|||
spatial_dims = range(
|
||||
num_batch_dims + 1, num_spatial_dims + num_batch_dims + 1)
|
||||
|
||||
if not input_channels_dim.is_compatible_with(
|
||||
filter_shape[num_spatial_dims]):
|
||||
raise ValueError(
|
||||
"Number of input channels does not match corresponding dimension of "
|
||||
"filter, {} != {}".format(input_channels_dim,
|
||||
filter_shape[num_spatial_dims]))
|
||||
filter_dim = tensor_shape.dimension_at_index(filter_shape, num_spatial_dims)
|
||||
if not (input_channels_dim % filter_dim).is_compatible_with(0):
|
||||
raise ValueError("The number of input channels is not divisible by the "
|
||||
"corresponding number of output filters. Received: "
|
||||
"input channels={}, output filters={}".format(
|
||||
input_channels_dim, filter_dim))
|
||||
|
||||
strides, dilation_rate = _get_strides_and_dilation_rate(
|
||||
num_spatial_dims, strides, dilation_rate)
|
||||
|
|
@ -2051,9 +2051,9 @@ def conv2d_v2(input, # pylint: disable=redefined-builtin
|
|||
|
||||
Must have `strides[0] = strides[3] = 1`. For the most common case of the same
|
||||
horizontal and vertical strides, `strides = [1, stride, stride, 1]`.
|
||||
|
||||
|
||||
Usage Example:
|
||||
|
||||
|
||||
>>> x_in = np.array([[
|
||||
... [[2], [1], [2], [0], [1]],
|
||||
... [[1], [3], [2], [2], [3]],
|
||||
|
|
@ -3551,7 +3551,7 @@ def softmax(logits, axis=None, name=None, dim=None):
|
|||
Tensor.
|
||||
RuntimeError: If a registered conversion function returns an invalid
|
||||
value.
|
||||
|
||||
|
||||
"""
|
||||
axis = deprecation.deprecated_argument_lookup("axis", axis, "dim", dim)
|
||||
if axis is None:
|
||||
|
|
|
|||
|
|
@ -114,7 +114,7 @@ tf_class {
|
|||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'1\', \'valid\', \'channels_last\', \'1\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'groups\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'1\', \'valid\', \'channels_last\', \'1\', \'1\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
|
|
|||
|
|
@ -114,7 +114,7 @@ tf_class {
|
|||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1)\', \'valid\', \'None\', \'(1, 1)\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'groups\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1)\', \'valid\', \'None\', \'(1, 1)\', \'1\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
|
|
|||
|
|
@ -114,7 +114,7 @@ tf_class {
|
|||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1, 1)\', \'valid\', \'None\', \'(1, 1, 1)\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'groups\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1, 1)\', \'valid\', \'None\', \'(1, 1, 1)\', \'1\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
|
|
|||
|
|
@ -114,7 +114,7 @@ tf_class {
|
|||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'1\', \'valid\', \'channels_last\', \'1\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'groups\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'1\', \'valid\', \'channels_last\', \'1\', \'1\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
|
|
|||
|
|
@ -114,7 +114,7 @@ tf_class {
|
|||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1)\', \'valid\', \'None\', \'(1, 1)\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'groups\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1)\', \'valid\', \'None\', \'(1, 1)\', \'1\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
|
|
|||
|
|
@ -114,7 +114,7 @@ tf_class {
|
|||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1, 1)\', \'valid\', \'None\', \'(1, 1, 1)\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'groups\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1, 1)\', \'valid\', \'None\', \'(1, 1, 1)\', \'1\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
|
|
|||
|
|
@ -114,7 +114,7 @@ tf_class {
|
|||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'1\', \'valid\', \'channels_last\', \'1\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'groups\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'1\', \'valid\', \'channels_last\', \'1\', \'1\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
|
|
|||
|
|
@ -114,7 +114,7 @@ tf_class {
|
|||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1)\', \'valid\', \'None\', \'(1, 1)\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'groups\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1)\', \'valid\', \'None\', \'(1, 1)\', \'1\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
|
|
|||
|
|
@ -114,7 +114,7 @@ tf_class {
|
|||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1, 1)\', \'valid\', \'None\', \'(1, 1, 1)\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'groups\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1, 1)\', \'valid\', \'None\', \'(1, 1, 1)\', \'1\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
|
|
|||
|
|
@ -114,7 +114,7 @@ tf_class {
|
|||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'1\', \'valid\', \'channels_last\', \'1\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'groups\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'1\', \'valid\', \'channels_last\', \'1\', \'1\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
|
|
|||
|
|
@ -114,7 +114,7 @@ tf_class {
|
|||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1)\', \'valid\', \'None\', \'(1, 1)\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'groups\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1)\', \'valid\', \'None\', \'(1, 1)\', \'1\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
|
|
|||
|
|
@ -114,7 +114,7 @@ tf_class {
|
|||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1, 1)\', \'valid\', \'None\', \'(1, 1, 1)\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'groups\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1, 1)\', \'valid\', \'None\', \'(1, 1, 1)\', \'1\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
|
|
|||
Loading…
Reference in New Issue