Custom Conv2D in Keras ValueError: An operation has `None` for gradient with self.kernel
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I am converting this tools (ann4brains) from Caffe to Keras.
I already implemented the two custom types of 2D convolution (E2E and E2N).
I made the implementation based on the source code of _Conv, from Keras source code.1
The model compiles, but it fails during the fitting with the following message error:
ValueError: An operation has `None` for gradient. Please make sure that all of your ops have a gradient defined (i.e. are differentiable). Common ops without gradient: K.argmax, K.round, K.eval.
I googled and found this topic: Custom Keras Layer Troubles
The solution (remove the self.kernel = self.add_weight(...) worked for my case. But I am not feeling safe about this solution. Why should I remove this, if it is in the _Conv class? What will be my kernel if I comment this? Is there any other recommended solution?
Thanks!
Bellow more information about the case:
Keras version: 2.2.4
Input shape: (21, 21, 1)
Model Summary:
Layer (type) Output Shape Param #
=================================================================
conv_e2e_20 (ConvE2E) (None, 21, 21, 1, 32) 1376
_________________________________________________________________
conv_e2n_16 (ConvE2N) (None, 21, 1, 1, 64) 43072
_________________________________________________________________
flatten_28 (Flatten) (None, 1344) 0
_________________________________________________________________
dense_91 (Dense) (None, 128) 172160
_________________________________________________________________
dropout_84 (Dropout) (None, 128) 0
_________________________________________________________________
dense_92 (Dense) (None, 30) 3870
_________________________________________________________________
dropout_85 (Dropout) (None, 30) 0
_________________________________________________________________
dense_93 (Dense) (None, 30) 930
_________________________________________________________________
dropout_86 (Dropout) (None, 30) 0
_________________________________________________________________
dense_94 (Dense) (None, 2) 62
=================================================================
Total params: 221,470
Trainable params: 221,470
Non-trainable params: 0
_________________________________________________________________
E2E Layer
class ConvE2E(Layer):
def __init__(self,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
dilation_rate=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):
super(ConvE2E, self).__init__(**kwargs)
self.rank = 2
self.filters = filters
self.kernel_size = kernel_size
self.strides = conv_utils.normalize_tuple(strides, self.rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = K.common.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, self.rank,
'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(ndim=self.rank + 2)
def build(self, input_shape):
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
self.input_dim = input_shape[channel_axis]
kernel_shape = self.kernel_size + (self.input_dim, self.filters)
# self.kernel = self.add_weight(shape=kernel_shape,
# initializer=self.kernel_initializer,
# name='kernel',
# regularizer=self.kernel_regularizer,
# constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.filters,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
# Set input spec.
self.input_spec = InputSpec(ndim=self.rank + 2,
axes={channel_axis: self.input_dim})
self.built = True
def call(self, inputs):
kernel_h = self.kernel_size[0]
kernel_w = self.kernel_size[1]
kernel_size_h = conv_utils.normalize_tuple((kernel_h, 1), 2, 'kernel_size')
kernel_shape_h = kernel_size_h + (self.input_dim, self.filters)
kernel_dx1 = self.add_weight(shape=kernel_shape_h,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
kernel_size_w = conv_utils.normalize_tuple((1, kernel_w), 2, 'kernel_size')
kernel_shape_w = kernel_size_w + (self.input_dim, self.filters)
kernel_1xd = self.add_weight(shape=kernel_shape_w,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
outputs_dx1 = K.conv2d(inputs, kernel_dx1,strides=self.strides,
padding=self.padding, data_format=self.data_format,
dilation_rate=self.dilation_rate)
outputs_dx1_dxd = K.repeat_elements(outputs_dx1, kernel_w, 1)
outputs_1xd = K.conv2d(inputs, kernel_1xd, strides=self.strides,
padding=self.padding, data_format=self.data_format,
dilation_rate=self.dilation_rate)
outputs_1xd_dxd = K.repeat_elements(outputs_1xd, kernel_h, 2)
outputs = Add()([outputs_dx1_dxd, outputs_1xd_dxd])
if self.use_bias:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_last':
output_shape = (input_shape) + (self.filters,)
return output_shape
if self.data_format == 'channels_first':
output_shape = (input_shape[0], self.filters) + (input_shape[1:])
return output_shape
def get_config(self):
config = {
'rank': self.rank,
'filters': self.filters,
'kernel_size': self.kernel_size,
'strides': self.strides,
'padding': self.padding,
'data_format': self.data_format,
'dilation_rate': self.dilation_rate,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)
}
base_config = super(ConvE2E, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
E2N Layer
class ConvE2N(Layer):
def __init__(self,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
dilation_rate=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):
super(ConvE2N, self).__init__(**kwargs)
self.rank = 2
self.filters = filters
self.kernel_size = kernel_size
self.strides = conv_utils.normalize_tuple(strides, self.rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = K.common.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, self.rank,
'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(ndim=self.rank + 2)
def build(self, input_shape):
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
self.input_dim = input_shape[channel_axis]
kernel_shape = self.kernel_size + (self.input_dim, self.filters)
# self.kernel = self.add_weight(shape=kernel_shape,
# initializer=self.kernel_initializer,
# name='kernel',
# regularizer=self.kernel_regularizer,
# constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.filters,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
# Set input spec.
self.input_spec = InputSpec(ndim=self.rank + 2,
axes={channel_axis: self.input_dim})
self.built = True
def call(self, inputs):
kernel_h = self.kernel_size[0]
kernel_w = self.kernel_size[1]
kernel_size_w = conv_utils.normalize_tuple((1, kernel_w), 2, 'kernel_size')
kernel_shape_w = kernel_size_w + (self.input_dim, self.filters)
kernel_1xd = self.add_weight(shape=kernel_shape_w,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
outputs_1xd = K.conv2d(inputs, kernel_1xd, strides=self.strides,
padding=self.padding, data_format=self.data_format,
dilation_rate=self.dilation_rate)
outputs = outputs_1xd
if self.use_bias:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_last':
output_shape = (input_shape[0], self.kernel_size[0], 1, input_shape[-2], self.filters)
return output_shape
if self.data_format == 'channels_first':
output_shape = input_shape[0:2] + (self.kernel_size[0], 1, self.filters)
return output_shape
def get_config(self):
config = {
'rank': self.rank,
'filters': self.filters,
'kernel_size': self.kernel_size,
'strides': self.strides,
'padding': self.padding,
'data_format': self.data_format,
'dilation_rate': self.dilation_rate,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)
}
base_config = super(ConvE2N, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
python tensorflow machine-learning keras customization
asked Nov 22 '18 at 14:28
ANSantanaANSantana
112
add a comment |
0
I am converting this tools (ann4brains) from Caffe to Keras.
I already implemented the two custom types of 2D convolution (E2E and E2N).
I made the implementation based on the source code of _Conv, from Keras source code.1
The model compiles, but it fails during the fitting with the following message error:
ValueError: An operation has `None` for gradient. Please make sure that all of your ops have a gradient defined (i.e. are differentiable). Common ops without gradient: K.argmax, K.round, K.eval.
I googled and found this topic: Custom Keras Layer Troubles
The solution (remove the self.kernel = self.add_weight(...) worked for my case. But I am not feeling safe about this solution. Why should I remove this, if it is in the _Conv class? What will be my kernel if I comment this? Is there any other recommended solution?
Thanks!
Bellow more information about the case:
Keras version: 2.2.4
Input shape: (21, 21, 1)
Model Summary:
Layer (type) Output Shape Param #
=================================================================
conv_e2e_20 (ConvE2E) (None, 21, 21, 1, 32) 1376
_________________________________________________________________
conv_e2n_16 (ConvE2N) (None, 21, 1, 1, 64) 43072
_________________________________________________________________
flatten_28 (Flatten) (None, 1344) 0
_________________________________________________________________
dense_91 (Dense) (None, 128) 172160
_________________________________________________________________
dropout_84 (Dropout) (None, 128) 0
_________________________________________________________________
dense_92 (Dense) (None, 30) 3870
_________________________________________________________________
dropout_85 (Dropout) (None, 30) 0
_________________________________________________________________
dense_93 (Dense) (None, 30) 930
_________________________________________________________________
dropout_86 (Dropout) (None, 30) 0
_________________________________________________________________
dense_94 (Dense) (None, 2) 62
=================================================================
Total params: 221,470
Trainable params: 221,470
Non-trainable params: 0
_________________________________________________________________
E2E Layer
class ConvE2E(Layer):
def __init__(self,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
dilation_rate=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):
super(ConvE2E, self).__init__(**kwargs)
self.rank = 2
self.filters = filters
self.kernel_size = kernel_size
self.strides = conv_utils.normalize_tuple(strides, self.rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = K.common.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, self.rank,
'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(ndim=self.rank + 2)
def build(self, input_shape):
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
self.input_dim = input_shape[channel_axis]
kernel_shape = self.kernel_size + (self.input_dim, self.filters)
# self.kernel = self.add_weight(shape=kernel_shape,
# initializer=self.kernel_initializer,
# name='kernel',
# regularizer=self.kernel_regularizer,
# constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.filters,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
# Set input spec.
self.input_spec = InputSpec(ndim=self.rank + 2,
axes={channel_axis: self.input_dim})
self.built = True
def call(self, inputs):
kernel_h = self.kernel_size[0]
kernel_w = self.kernel_size[1]
kernel_size_h = conv_utils.normalize_tuple((kernel_h, 1), 2, 'kernel_size')
kernel_shape_h = kernel_size_h + (self.input_dim, self.filters)
kernel_dx1 = self.add_weight(shape=kernel_shape_h,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
kernel_size_w = conv_utils.normalize_tuple((1, kernel_w), 2, 'kernel_size')
kernel_shape_w = kernel_size_w + (self.input_dim, self.filters)
kernel_1xd = self.add_weight(shape=kernel_shape_w,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
outputs_dx1 = K.conv2d(inputs, kernel_dx1,strides=self.strides,
padding=self.padding, data_format=self.data_format,
dilation_rate=self.dilation_rate)
outputs_dx1_dxd = K.repeat_elements(outputs_dx1, kernel_w, 1)
outputs_1xd = K.conv2d(inputs, kernel_1xd, strides=self.strides,
padding=self.padding, data_format=self.data_format,
dilation_rate=self.dilation_rate)
outputs_1xd_dxd = K.repeat_elements(outputs_1xd, kernel_h, 2)
outputs = Add()([outputs_dx1_dxd, outputs_1xd_dxd])
if self.use_bias:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_last':
output_shape = (input_shape) + (self.filters,)
return output_shape
if self.data_format == 'channels_first':
output_shape = (input_shape[0], self.filters) + (input_shape[1:])
return output_shape
def get_config(self):
config = {
'rank': self.rank,
'filters': self.filters,
'kernel_size': self.kernel_size,
'strides': self.strides,
'padding': self.padding,
'data_format': self.data_format,
'dilation_rate': self.dilation_rate,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)
}
base_config = super(ConvE2E, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
E2N Layer
class ConvE2N(Layer):
def __init__(self,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
dilation_rate=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):
super(ConvE2N, self).__init__(**kwargs)
self.rank = 2
self.filters = filters
self.kernel_size = kernel_size
self.strides = conv_utils.normalize_tuple(strides, self.rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = K.common.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, self.rank,
'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(ndim=self.rank + 2)
def build(self, input_shape):
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
self.input_dim = input_shape[channel_axis]
kernel_shape = self.kernel_size + (self.input_dim, self.filters)
# self.kernel = self.add_weight(shape=kernel_shape,
# initializer=self.kernel_initializer,
# name='kernel',
# regularizer=self.kernel_regularizer,
# constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.filters,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
# Set input spec.
self.input_spec = InputSpec(ndim=self.rank + 2,
axes={channel_axis: self.input_dim})
self.built = True
def call(self, inputs):
kernel_h = self.kernel_size[0]
kernel_w = self.kernel_size[1]
kernel_size_w = conv_utils.normalize_tuple((1, kernel_w), 2, 'kernel_size')
kernel_shape_w = kernel_size_w + (self.input_dim, self.filters)
kernel_1xd = self.add_weight(shape=kernel_shape_w,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
outputs_1xd = K.conv2d(inputs, kernel_1xd, strides=self.strides,
padding=self.padding, data_format=self.data_format,
dilation_rate=self.dilation_rate)
outputs = outputs_1xd
if self.use_bias:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_last':
output_shape = (input_shape[0], self.kernel_size[0], 1, input_shape[-2], self.filters)
return output_shape
if self.data_format == 'channels_first':
output_shape = input_shape[0:2] + (self.kernel_size[0], 1, self.filters)
return output_shape
def get_config(self):
config = {
'rank': self.rank,
'filters': self.filters,
'kernel_size': self.kernel_size,
'strides': self.strides,
'padding': self.padding,
'data_format': self.data_format,
'dilation_rate': self.dilation_rate,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)
}
base_config = super(ConvE2N, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
python tensorflow machine-learning keras customization
asked Nov 22 '18 at 14:28
ANSantanaANSantana
112
add a comment |
0
0
0
I am converting this tools (ann4brains) from Caffe to Keras.
I already implemented the two custom types of 2D convolution (E2E and E2N).
I made the implementation based on the source code of _Conv, from Keras source code.1
The model compiles, but it fails during the fitting with the following message error:
ValueError: An operation has `None` for gradient. Please make sure that all of your ops have a gradient defined (i.e. are differentiable). Common ops without gradient: K.argmax, K.round, K.eval.
I googled and found this topic: Custom Keras Layer Troubles
The solution (remove the self.kernel = self.add_weight(...) worked for my case. But I am not feeling safe about this solution. Why should I remove this, if it is in the _Conv class? What will be my kernel if I comment this? Is there any other recommended solution?
Thanks!
Bellow more information about the case:
Keras version: 2.2.4
Input shape: (21, 21, 1)
Model Summary:
Layer (type) Output Shape Param #
=================================================================
conv_e2e_20 (ConvE2E) (None, 21, 21, 1, 32) 1376
_________________________________________________________________
conv_e2n_16 (ConvE2N) (None, 21, 1, 1, 64) 43072
_________________________________________________________________
flatten_28 (Flatten) (None, 1344) 0
_________________________________________________________________
dense_91 (Dense) (None, 128) 172160
_________________________________________________________________
dropout_84 (Dropout) (None, 128) 0
_________________________________________________________________
dense_92 (Dense) (None, 30) 3870
_________________________________________________________________
dropout_85 (Dropout) (None, 30) 0
_________________________________________________________________
dense_93 (Dense) (None, 30) 930
_________________________________________________________________
dropout_86 (Dropout) (None, 30) 0
_________________________________________________________________
dense_94 (Dense) (None, 2) 62
=================================================================
Total params: 221,470
Trainable params: 221,470
Non-trainable params: 0
_________________________________________________________________
E2E Layer
class ConvE2E(Layer):
def __init__(self,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
dilation_rate=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):
super(ConvE2E, self).__init__(**kwargs)
self.rank = 2
self.filters = filters
self.kernel_size = kernel_size
self.strides = conv_utils.normalize_tuple(strides, self.rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = K.common.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, self.rank,
'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(ndim=self.rank + 2)
def build(self, input_shape):
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
self.input_dim = input_shape[channel_axis]
kernel_shape = self.kernel_size + (self.input_dim, self.filters)
# self.kernel = self.add_weight(shape=kernel_shape,
# initializer=self.kernel_initializer,
# name='kernel',
# regularizer=self.kernel_regularizer,
# constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.filters,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
# Set input spec.
self.input_spec = InputSpec(ndim=self.rank + 2,
axes={channel_axis: self.input_dim})
self.built = True
def call(self, inputs):
kernel_h = self.kernel_size[0]
kernel_w = self.kernel_size[1]
kernel_size_h = conv_utils.normalize_tuple((kernel_h, 1), 2, 'kernel_size')
kernel_shape_h = kernel_size_h + (self.input_dim, self.filters)
kernel_dx1 = self.add_weight(shape=kernel_shape_h,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
kernel_size_w = conv_utils.normalize_tuple((1, kernel_w), 2, 'kernel_size')
kernel_shape_w = kernel_size_w + (self.input_dim, self.filters)
kernel_1xd = self.add_weight(shape=kernel_shape_w,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
outputs_dx1 = K.conv2d(inputs, kernel_dx1,strides=self.strides,
padding=self.padding, data_format=self.data_format,
dilation_rate=self.dilation_rate)
outputs_dx1_dxd = K.repeat_elements(outputs_dx1, kernel_w, 1)
outputs_1xd = K.conv2d(inputs, kernel_1xd, strides=self.strides,
padding=self.padding, data_format=self.data_format,
dilation_rate=self.dilation_rate)
outputs_1xd_dxd = K.repeat_elements(outputs_1xd, kernel_h, 2)
outputs = Add()([outputs_dx1_dxd, outputs_1xd_dxd])
if self.use_bias:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_last':
output_shape = (input_shape) + (self.filters,)
return output_shape
if self.data_format == 'channels_first':
output_shape = (input_shape[0], self.filters) + (input_shape[1:])
return output_shape
def get_config(self):
config = {
'rank': self.rank,
'filters': self.filters,
'kernel_size': self.kernel_size,
'strides': self.strides,
'padding': self.padding,
'data_format': self.data_format,
'dilation_rate': self.dilation_rate,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)
}
base_config = super(ConvE2E, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
E2N Layer
class ConvE2N(Layer):
def __init__(self,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
dilation_rate=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):
super(ConvE2N, self).__init__(**kwargs)
self.rank = 2
self.filters = filters
self.kernel_size = kernel_size
self.strides = conv_utils.normalize_tuple(strides, self.rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = K.common.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, self.rank,
'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(ndim=self.rank + 2)
def build(self, input_shape):
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
self.input_dim = input_shape[channel_axis]
kernel_shape = self.kernel_size + (self.input_dim, self.filters)
# self.kernel = self.add_weight(shape=kernel_shape,
# initializer=self.kernel_initializer,
# name='kernel',
# regularizer=self.kernel_regularizer,
# constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.filters,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
# Set input spec.
self.input_spec = InputSpec(ndim=self.rank + 2,
axes={channel_axis: self.input_dim})
self.built = True
def call(self, inputs):
kernel_h = self.kernel_size[0]
kernel_w = self.kernel_size[1]
kernel_size_w = conv_utils.normalize_tuple((1, kernel_w), 2, 'kernel_size')
kernel_shape_w = kernel_size_w + (self.input_dim, self.filters)
kernel_1xd = self.add_weight(shape=kernel_shape_w,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
outputs_1xd = K.conv2d(inputs, kernel_1xd, strides=self.strides,
padding=self.padding, data_format=self.data_format,
dilation_rate=self.dilation_rate)
outputs = outputs_1xd
if self.use_bias:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_last':
output_shape = (input_shape[0], self.kernel_size[0], 1, input_shape[-2], self.filters)
return output_shape
if self.data_format == 'channels_first':
output_shape = input_shape[0:2] + (self.kernel_size[0], 1, self.filters)
return output_shape
def get_config(self):
config = {
'rank': self.rank,
'filters': self.filters,
'kernel_size': self.kernel_size,
'strides': self.strides,
'padding': self.padding,
'data_format': self.data_format,
'dilation_rate': self.dilation_rate,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)
}
base_config = super(ConvE2N, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
python tensorflow machine-learning keras customization
asked Nov 22 '18 at 14:28
ANSantanaANSantana
112
I am converting this tools (ann4brains) from Caffe to Keras.
I already implemented the two custom types of 2D convolution (E2E and E2N).
I made the implementation based on the source code of _Conv, from Keras source code.1
The model compiles, but it fails during the fitting with the following message error:
ValueError: An operation has `None` for gradient. Please make sure that all of your ops have a gradient defined (i.e. are differentiable). Common ops without gradient: K.argmax, K.round, K.eval.
I googled and found this topic: Custom Keras Layer Troubles
The solution (remove the self.kernel = self.add_weight(...) worked for my case. But I am not feeling safe about this solution. Why should I remove this, if it is in the _Conv class? What will be my kernel if I comment this? Is there any other recommended solution?
Thanks!
Bellow more information about the case:
Keras version: 2.2.4
Input shape: (21, 21, 1)
Model Summary:
Layer (type) Output Shape Param #
=================================================================
conv_e2e_20 (ConvE2E) (None, 21, 21, 1, 32) 1376
_________________________________________________________________
conv_e2n_16 (ConvE2N) (None, 21, 1, 1, 64) 43072
_________________________________________________________________
flatten_28 (Flatten) (None, 1344) 0
_________________________________________________________________
dense_91 (Dense) (None, 128) 172160
_________________________________________________________________
dropout_84 (Dropout) (None, 128) 0
_________________________________________________________________
dense_92 (Dense) (None, 30) 3870
_________________________________________________________________
dropout_85 (Dropout) (None, 30) 0
_________________________________________________________________
dense_93 (Dense) (None, 30) 930
_________________________________________________________________
dropout_86 (Dropout) (None, 30) 0
_________________________________________________________________
dense_94 (Dense) (None, 2) 62
=================================================================
Total params: 221,470
Trainable params: 221,470
Non-trainable params: 0
_________________________________________________________________
E2E Layer
class ConvE2E(Layer):
def __init__(self,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
dilation_rate=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):
super(ConvE2E, self).__init__(**kwargs)
self.rank = 2
self.filters = filters
self.kernel_size = kernel_size
self.strides = conv_utils.normalize_tuple(strides, self.rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = K.common.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, self.rank,
'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(ndim=self.rank + 2)
def build(self, input_shape):
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
self.input_dim = input_shape[channel_axis]
kernel_shape = self.kernel_size + (self.input_dim, self.filters)
# self.kernel = self.add_weight(shape=kernel_shape,
# initializer=self.kernel_initializer,
# name='kernel',
# regularizer=self.kernel_regularizer,
# constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.filters,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
# Set input spec.
self.input_spec = InputSpec(ndim=self.rank + 2,
axes={channel_axis: self.input_dim})
self.built = True
def call(self, inputs):
kernel_h = self.kernel_size[0]
kernel_w = self.kernel_size[1]
kernel_size_h = conv_utils.normalize_tuple((kernel_h, 1), 2, 'kernel_size')
kernel_shape_h = kernel_size_h + (self.input_dim, self.filters)
kernel_dx1 = self.add_weight(shape=kernel_shape_h,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
kernel_size_w = conv_utils.normalize_tuple((1, kernel_w), 2, 'kernel_size')
kernel_shape_w = kernel_size_w + (self.input_dim, self.filters)
kernel_1xd = self.add_weight(shape=kernel_shape_w,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
outputs_dx1 = K.conv2d(inputs, kernel_dx1,strides=self.strides,
padding=self.padding, data_format=self.data_format,
dilation_rate=self.dilation_rate)
outputs_dx1_dxd = K.repeat_elements(outputs_dx1, kernel_w, 1)
outputs_1xd = K.conv2d(inputs, kernel_1xd, strides=self.strides,
padding=self.padding, data_format=self.data_format,
dilation_rate=self.dilation_rate)
outputs_1xd_dxd = K.repeat_elements(outputs_1xd, kernel_h, 2)
outputs = Add()([outputs_dx1_dxd, outputs_1xd_dxd])
if self.use_bias:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_last':
output_shape = (input_shape) + (self.filters,)
return output_shape
if self.data_format == 'channels_first':
output_shape = (input_shape[0], self.filters) + (input_shape[1:])
return output_shape
def get_config(self):
config = {
'rank': self.rank,
'filters': self.filters,
'kernel_size': self.kernel_size,
'strides': self.strides,
'padding': self.padding,
'data_format': self.data_format,
'dilation_rate': self.dilation_rate,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)
}
base_config = super(ConvE2E, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
E2N Layer
class ConvE2N(Layer):
def __init__(self,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
dilation_rate=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):
super(ConvE2N, self).__init__(**kwargs)
self.rank = 2
self.filters = filters
self.kernel_size = kernel_size
self.strides = conv_utils.normalize_tuple(strides, self.rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = K.common.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, self.rank,
'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(ndim=self.rank + 2)
def build(self, input_shape):
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
self.input_dim = input_shape[channel_axis]
kernel_shape = self.kernel_size + (self.input_dim, self.filters)
# self.kernel = self.add_weight(shape=kernel_shape,
# initializer=self.kernel_initializer,
# name='kernel',
# regularizer=self.kernel_regularizer,
# constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.filters,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
# Set input spec.
self.input_spec = InputSpec(ndim=self.rank + 2,
axes={channel_axis: self.input_dim})
self.built = True
def call(self, inputs):
kernel_h = self.kernel_size[0]
kernel_w = self.kernel_size[1]
kernel_size_w = conv_utils.normalize_tuple((1, kernel_w), 2, 'kernel_size')
kernel_shape_w = kernel_size_w + (self.input_dim, self.filters)
kernel_1xd = self.add_weight(shape=kernel_shape_w,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
outputs_1xd = K.conv2d(inputs, kernel_1xd, strides=self.strides,
padding=self.padding, data_format=self.data_format,
dilation_rate=self.dilation_rate)
outputs = outputs_1xd
if self.use_bias:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_last':
output_shape = (input_shape[0], self.kernel_size[0], 1, input_shape[-2], self.filters)
return output_shape
if self.data_format == 'channels_first':
output_shape = input_shape[0:2] + (self.kernel_size[0], 1, self.filters)
return output_shape
def get_config(self):
config = {
'rank': self.rank,
'filters': self.filters,
'kernel_size': self.kernel_size,
'strides': self.strides,
'padding': self.padding,
'data_format': self.data_format,
'dilation_rate': self.dilation_rate,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)
}
base_config = super(ConvE2N, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
python tensorflow machine-learning keras customization
python tensorflow machine-learning keras customization
asked Nov 22 '18 at 14:28
ANSantanaANSantana
112
asked Nov 22 '18 at 14:28
ANSantanaANSantana
112
asked Nov 22 '18 at 14:28
ANSantanaANSantana
112
asked Nov 22 '18 at 14:28
ANSantanaANSantana
112
asked Nov 22 '18 at 14:28
ANSantanaANSantana
112
112
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Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
