In Keras, how to apply softmax function on each row of the weight matrix?
from keras.models import Model
from keras.models import Input
from keras.layers import Dense
a = Input(shape=(3,))
b = Dense(2, use_bias=False)(a)
model = Model(inputs=a, outputs=b)
Suppose that the weights of the Dense layer in the above code is [[2, 3], [3, 1], [-1, 1]]. If we give [[2, 1, 3]] as an input to the model, then the output will be:
But I want to apply the softmax function to each row of the Dense layer, so that the output will be:
How can I do this?
python machine-learning keras keras-layer softmax
asked Nov 13 at 3:41
zxcv
285
add a comment |
from keras.models import Model
from keras.models import Input
from keras.layers import Dense
a = Input(shape=(3,))
b = Dense(2, use_bias=False)(a)
model = Model(inputs=a, outputs=b)
Suppose that the weights of the Dense layer in the above code is [[2, 3], [3, 1], [-1, 1]]. If we give [[2, 1, 3]] as an input to the model, then the output will be:
But I want to apply the softmax function to each row of the Dense layer, so that the output will be:
How can I do this?
python machine-learning keras keras-layer softmax
asked Nov 13 at 3:41
zxcv
285
You mean you want the softmax to be applied on the weights of Dense layer and not on its output, right?
– today
Nov 13 at 7:29
@today Yes, exactly.
– zxcv
Nov 13 at 9:59
add a comment |
from keras.models import Model
from keras.models import Input
from keras.layers import Dense
a = Input(shape=(3,))
b = Dense(2, use_bias=False)(a)
model = Model(inputs=a, outputs=b)
Suppose that the weights of the Dense layer in the above code is [[2, 3], [3, 1], [-1, 1]]. If we give [[2, 1, 3]] as an input to the model, then the output will be:
But I want to apply the softmax function to each row of the Dense layer, so that the output will be:
How can I do this?
python machine-learning keras keras-layer softmax
asked Nov 13 at 3:41
zxcv
285
from keras.models import Model
from keras.models import Input
from keras.layers import Dense
a = Input(shape=(3,))
b = Dense(2, use_bias=False)(a)
model = Model(inputs=a, outputs=b)
Suppose that the weights of the Dense layer in the above code is [[2, 3], [3, 1], [-1, 1]]. If we give [[2, 1, 3]] as an input to the model, then the output will be:
But I want to apply the softmax function to each row of the Dense layer, so that the output will be:
How can I do this?
python machine-learning keras keras-layer softmax
python machine-learning keras keras-layer softmax
asked Nov 13 at 3:41
zxcv
285
asked Nov 13 at 3:41
zxcv
285
edited Nov 14 at 5:04
asked Nov 13 at 3:41
zxcv
285
asked Nov 13 at 3:41
zxcv
285
asked Nov 13 at 3:41
zxcv
285
285
You mean you want the softmax to be applied on the weights of Dense layer and not on its output, right?
– today
Nov 13 at 7:29
@today Yes, exactly.
– zxcv
Nov 13 at 9:59
add a comment |
You mean you want the softmax to be applied on the weights of Dense layer and not on its output, right?
– today
Nov 13 at 7:29
@today Yes, exactly.
– zxcv
Nov 13 at 9:59
You mean you want the softmax to be applied on the weights of Dense layer and not on its output, right?
– today
Nov 13 at 7:29
You mean you want the softmax to be applied on the weights of Dense layer and not on its output, right?
– today
Nov 13 at 7:29
@today Yes, exactly.
– zxcv
Nov 13 at 9:59
@today Yes, exactly.
– zxcv
Nov 13 at 9:59
add a comment |
1 Answer
1
active
oldest
votes
One way to achieve what you are looking for is to define a custom layer by subclassing the Dense layer and overriding its call method:
from keras import backend as K
class CustomDense(Dense):
def __init__(self, units, **kwargs):
super(CustomDense, self).__init__(units, **kwargs)
def call(self, inputs):
output = K.dot(inputs, K.softmax(self.kernel, axis=-1))
if self.use_bias:
output = K.bias_add(output, self.bias, data_format='channels_last')
if self.activation is not None:
output = self.activation(output)
return output
Test to make sure it works:
model = Sequential()
model.add(CustomDense(2, use_bias=False, input_shape=(3,)))
model.compile(loss='mse', optimizer='adam')
import numpy as np
w = np.array([[2,3], [3,1], [1,-1]])
inp = np.array([[2,1,3]])
model.layers[0].set_weights([w])
print(model.predict(inp))
# output
[[4.0610714 1.9389288]]
Verify it using numpy:
soft_w = np.exp(w) / np.sum(np.exp(w), axis=-1, keepdims=True)
print(np.dot(inp, soft_w))
[[4.06107115 1.93892885]]
answered Nov 13 at 10:58
today
9,28621435
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
One way to achieve what you are looking for is to define a custom layer by subclassing the Dense layer and overriding its call method:
from keras import backend as K
class CustomDense(Dense):
def __init__(self, units, **kwargs):
super(CustomDense, self).__init__(units, **kwargs)
def call(self, inputs):
output = K.dot(inputs, K.softmax(self.kernel, axis=-1))
if self.use_bias:
output = K.bias_add(output, self.bias, data_format='channels_last')
if self.activation is not None:
output = self.activation(output)
return output
Test to make sure it works:
model = Sequential()
model.add(CustomDense(2, use_bias=False, input_shape=(3,)))
model.compile(loss='mse', optimizer='adam')
import numpy as np
w = np.array([[2,3], [3,1], [1,-1]])
inp = np.array([[2,1,3]])
model.layers[0].set_weights([w])
print(model.predict(inp))
# output
[[4.0610714 1.9389288]]
Verify it using numpy:
soft_w = np.exp(w) / np.sum(np.exp(w), axis=-1, keepdims=True)
print(np.dot(inp, soft_w))
[[4.06107115 1.93892885]]
answered Nov 13 at 10:58
today
9,28621435
add a comment |
One way to achieve what you are looking for is to define a custom layer by subclassing the Dense layer and overriding its call method:
from keras import backend as K
class CustomDense(Dense):
def __init__(self, units, **kwargs):
super(CustomDense, self).__init__(units, **kwargs)
def call(self, inputs):
output = K.dot(inputs, K.softmax(self.kernel, axis=-1))
if self.use_bias:
output = K.bias_add(output, self.bias, data_format='channels_last')
if self.activation is not None:
output = self.activation(output)
return output
Test to make sure it works:
model = Sequential()
model.add(CustomDense(2, use_bias=False, input_shape=(3,)))
model.compile(loss='mse', optimizer='adam')
import numpy as np
w = np.array([[2,3], [3,1], [1,-1]])
inp = np.array([[2,1,3]])
model.layers[0].set_weights([w])
print(model.predict(inp))
# output
[[4.0610714 1.9389288]]
Verify it using numpy:
soft_w = np.exp(w) / np.sum(np.exp(w), axis=-1, keepdims=True)
print(np.dot(inp, soft_w))
[[4.06107115 1.93892885]]
answered Nov 13 at 10:58
today
9,28621435
add a comment |
One way to achieve what you are looking for is to define a custom layer by subclassing the Dense layer and overriding its call method:
from keras import backend as K
class CustomDense(Dense):
def __init__(self, units, **kwargs):
super(CustomDense, self).__init__(units, **kwargs)
def call(self, inputs):
output = K.dot(inputs, K.softmax(self.kernel, axis=-1))
if self.use_bias:
output = K.bias_add(output, self.bias, data_format='channels_last')
if self.activation is not None:
output = self.activation(output)
return output
Test to make sure it works:
model = Sequential()
model.add(CustomDense(2, use_bias=False, input_shape=(3,)))
model.compile(loss='mse', optimizer='adam')
import numpy as np
w = np.array([[2,3], [3,1], [1,-1]])
inp = np.array([[2,1,3]])
model.layers[0].set_weights([w])
print(model.predict(inp))
# output
[[4.0610714 1.9389288]]
Verify it using numpy:
soft_w = np.exp(w) / np.sum(np.exp(w), axis=-1, keepdims=True)
print(np.dot(inp, soft_w))
[[4.06107115 1.93892885]]
answered Nov 13 at 10:58
today
9,28621435
One way to achieve what you are looking for is to define a custom layer by subclassing the Dense layer and overriding its call method:
from keras import backend as K
class CustomDense(Dense):
def __init__(self, units, **kwargs):
super(CustomDense, self).__init__(units, **kwargs)
def call(self, inputs):
output = K.dot(inputs, K.softmax(self.kernel, axis=-1))
if self.use_bias:
output = K.bias_add(output, self.bias, data_format='channels_last')
if self.activation is not None:
output = self.activation(output)
return output
Test to make sure it works:
model = Sequential()
model.add(CustomDense(2, use_bias=False, input_shape=(3,)))
model.compile(loss='mse', optimizer='adam')
import numpy as np
w = np.array([[2,3], [3,1], [1,-1]])
inp = np.array([[2,1,3]])
model.layers[0].set_weights([w])
print(model.predict(inp))
# output
[[4.0610714 1.9389288]]
Verify it using numpy:
soft_w = np.exp(w) / np.sum(np.exp(w), axis=-1, keepdims=True)
print(np.dot(inp, soft_w))
[[4.06107115 1.93892885]]
answered Nov 13 at 10:58
today
9,28621435
answered Nov 13 at 10:58
today
9,28621435
answered Nov 13 at 10:58
today
9,28621435
answered Nov 13 at 10:58
today
9,28621435
9,28621435
add a comment |
add a comment |
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You mean you want the softmax to be applied on the weights of Dense layer and not on its output, right?
– today
Nov 13 at 7:29
@today Yes, exactly.
– zxcv
Nov 13 at 9:59