How to implement Maxout activation for Conv1D output
I am new to DL and re-coding the lasagne CNN to Keras(TF)
the part of the layer is Conv1D then Maxout , with feature maxout
The Maxout function removed from keras2.0
I take reference to stack overflow and git-hub
to write customize lambda function
def Maxout(x, num_unit=None):
input_shape = x.get_shape().as_list()
ch = input_shape[-1]
num_unit = int(ch / 2)
assert ch is not None and ch % num_unit == 0
x = K.backend.reshape(x, (-1, ch // int(num_unit) , int(num_unit)))
x = K.backend.max(x, axis=1,keepdims=True)
input_tensor = Input(shape=(128,32),name = 'input')
conv4= (Conv1D(64, kernel_size=5, strides=1,
padding = 'same',
name = 'conv4',
input_shape=(128,32)))(maxpool1)
output = Lambda(Maxout,name='maxout')(conv4)
model = Model(inputs=input_tensor, outputs=output)
print(model.summary())
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input (InputLayer) (None, 128, 32) 0
_________________________________________________________________
conv4 (Conv1D) (None, 128, 64) 20544
_________________________________________________________________
maxout (Lambda) (None, 1, 32) 0
=================================================================
where i expect the Maxout layer from input:(None,128,64) to output:(None,128,32)
How can i get the shape of output in 128,32?
python keras deep-learning
asked Nov 20 at 7:08
alan alan
62
add a comment |
I am new to DL and re-coding the lasagne CNN to Keras(TF)
the part of the layer is Conv1D then Maxout , with feature maxout
The Maxout function removed from keras2.0
I take reference to stack overflow and git-hub
to write customize lambda function
def Maxout(x, num_unit=None):
input_shape = x.get_shape().as_list()
ch = input_shape[-1]
num_unit = int(ch / 2)
assert ch is not None and ch % num_unit == 0
x = K.backend.reshape(x, (-1, ch // int(num_unit) , int(num_unit)))
x = K.backend.max(x, axis=1,keepdims=True)
input_tensor = Input(shape=(128,32),name = 'input')
conv4= (Conv1D(64, kernel_size=5, strides=1,
padding = 'same',
name = 'conv4',
input_shape=(128,32)))(maxpool1)
output = Lambda(Maxout,name='maxout')(conv4)
model = Model(inputs=input_tensor, outputs=output)
print(model.summary())
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input (InputLayer) (None, 128, 32) 0
_________________________________________________________________
conv4 (Conv1D) (None, 128, 64) 20544
_________________________________________________________________
maxout (Lambda) (None, 1, 32) 0
=================================================================
where i expect the Maxout layer from input:(None,128,64) to output:(None,128,32)
How can i get the shape of output in 128,32?
python keras deep-learning
asked Nov 20 at 7:08
alan alan
62
add a comment |
I am new to DL and re-coding the lasagne CNN to Keras(TF)
the part of the layer is Conv1D then Maxout , with feature maxout
The Maxout function removed from keras2.0
I take reference to stack overflow and git-hub
to write customize lambda function
def Maxout(x, num_unit=None):
input_shape = x.get_shape().as_list()
ch = input_shape[-1]
num_unit = int(ch / 2)
assert ch is not None and ch % num_unit == 0
x = K.backend.reshape(x, (-1, ch // int(num_unit) , int(num_unit)))
x = K.backend.max(x, axis=1,keepdims=True)
input_tensor = Input(shape=(128,32),name = 'input')
conv4= (Conv1D(64, kernel_size=5, strides=1,
padding = 'same',
name = 'conv4',
input_shape=(128,32)))(maxpool1)
output = Lambda(Maxout,name='maxout')(conv4)
model = Model(inputs=input_tensor, outputs=output)
print(model.summary())
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input (InputLayer) (None, 128, 32) 0
_________________________________________________________________
conv4 (Conv1D) (None, 128, 64) 20544
_________________________________________________________________
maxout (Lambda) (None, 1, 32) 0
=================================================================
where i expect the Maxout layer from input:(None,128,64) to output:(None,128,32)
How can i get the shape of output in 128,32?
python keras deep-learning
asked Nov 20 at 7:08
alan alan
62
I am new to DL and re-coding the lasagne CNN to Keras(TF)
the part of the layer is Conv1D then Maxout , with feature maxout
The Maxout function removed from keras2.0
I take reference to stack overflow and git-hub
to write customize lambda function
def Maxout(x, num_unit=None):
input_shape = x.get_shape().as_list()
ch = input_shape[-1]
num_unit = int(ch / 2)
assert ch is not None and ch % num_unit == 0
x = K.backend.reshape(x, (-1, ch // int(num_unit) , int(num_unit)))
x = K.backend.max(x, axis=1,keepdims=True)
input_tensor = Input(shape=(128,32),name = 'input')
conv4= (Conv1D(64, kernel_size=5, strides=1,
padding = 'same',
name = 'conv4',
input_shape=(128,32)))(maxpool1)
output = Lambda(Maxout,name='maxout')(conv4)
model = Model(inputs=input_tensor, outputs=output)
print(model.summary())
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input (InputLayer) (None, 128, 32) 0
_________________________________________________________________
conv4 (Conv1D) (None, 128, 64) 20544
_________________________________________________________________
maxout (Lambda) (None, 1, 32) 0
=================================================================
where i expect the Maxout layer from input:(None,128,64) to output:(None,128,32)
How can i get the shape of output in 128,32?
python keras deep-learning
python keras deep-learning
asked Nov 20 at 7:08
alan alan
62
asked Nov 20 at 7:08
alan alan
62
edited Nov 20 at 7:59
asked Nov 20 at 7:08
alan alan
62
asked Nov 20 at 7:08
alan alan
62
asked Nov 20 at 7:08
alan alan
62
62
add a comment |
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