How can I deal with this code error which happens in my custom layer in Keras?
I want to make a custom layer in Keras.
In this example, I use a variable to multiply the tensor, but i get the error which is
in /keras/engine/training_arrays.py, line 304, in predict_loop
outs[i][batch_start:batch_end] = batch_out ValueError: could not broadcast input array from shape (36) into shape (2).
Actually i have check this file, but i get nothing. Is there some wrong in my custom layer?
#the definition of mylayer.
from keras import backend as K
import keras
from keras.engine.topology import Layer
class mylayer(Layer):
def __init__(self, output_dim, **kwargs):
self.output_dim = output_dim
super(mylayer, self).__init__(**kwargs)
def build(self, input_shape):
self.kernel = self.add_weight(name = 'kernel',
shape=(1,),dtype='float32',trainable=True,initializer='uniform')
super(mylayer, self).build(input_shape)
def call(self, inputs, **kwargs):
return self.kernel * inputs[0]
def compute_output_shape(self, input_shape):
return (input_shape[0], input_shape[1])
#the test of mylayer.
from mylayer import mylayer
from tensorflow import keras as K
import numpy as np
from keras.layers import Input, Dense, Flatten
from keras.models import Model
x_train = np.random.random((2, 3, 4, 3))
y_train = np.random.random((2, 36))
print(x_train)
x = Input(shape=(3, 4, 3))
y = Flatten()(x)
output = mylayer((36, ))(y)
model = Model(inputs=x, outputs=output)
model.summary()
model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])
model.fit(x_train, y_train, epochs=2)
hist = model.predict(x_train,batch_size=2)
print(hist)
print(model.get_layer(index=1).get_weights())
#So is there some wrong in my custom error?
Especially, when i train this net, it's ok,but when i try to use "prdict", it's wrong.
python keras
edited Jan 1 at 9:10
Joey Mallone
2,15141731
asked Jan 1 at 8:51
cb zhangcb zhang
82
add a comment |
I want to make a custom layer in Keras.
In this example, I use a variable to multiply the tensor, but i get the error which is
in /keras/engine/training_arrays.py, line 304, in predict_loop
outs[i][batch_start:batch_end] = batch_out ValueError: could not broadcast input array from shape (36) into shape (2).
Actually i have check this file, but i get nothing. Is there some wrong in my custom layer?
#the definition of mylayer.
from keras import backend as K
import keras
from keras.engine.topology import Layer
class mylayer(Layer):
def __init__(self, output_dim, **kwargs):
self.output_dim = output_dim
super(mylayer, self).__init__(**kwargs)
def build(self, input_shape):
self.kernel = self.add_weight(name = 'kernel',
shape=(1,),dtype='float32',trainable=True,initializer='uniform')
super(mylayer, self).build(input_shape)
def call(self, inputs, **kwargs):
return self.kernel * inputs[0]
def compute_output_shape(self, input_shape):
return (input_shape[0], input_shape[1])
#the test of mylayer.
from mylayer import mylayer
from tensorflow import keras as K
import numpy as np
from keras.layers import Input, Dense, Flatten
from keras.models import Model
x_train = np.random.random((2, 3, 4, 3))
y_train = np.random.random((2, 36))
print(x_train)
x = Input(shape=(3, 4, 3))
y = Flatten()(x)
output = mylayer((36, ))(y)
model = Model(inputs=x, outputs=output)
model.summary()
model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])
model.fit(x_train, y_train, epochs=2)
hist = model.predict(x_train,batch_size=2)
print(hist)
print(model.get_layer(index=1).get_weights())
#So is there some wrong in my custom error?
Especially, when i train this net, it's ok,but when i try to use "prdict", it's wrong.
python keras
edited Jan 1 at 9:10
Joey Mallone
2,15141731
asked Jan 1 at 8:51
cb zhangcb zhang
82
Show us the full error traceback as code-formatted text.
– Klaus D.
Jan 1 at 8:54
add a comment |
I want to make a custom layer in Keras.
In this example, I use a variable to multiply the tensor, but i get the error which is
in /keras/engine/training_arrays.py, line 304, in predict_loop
outs[i][batch_start:batch_end] = batch_out ValueError: could not broadcast input array from shape (36) into shape (2).
Actually i have check this file, but i get nothing. Is there some wrong in my custom layer?
#the definition of mylayer.
from keras import backend as K
import keras
from keras.engine.topology import Layer
class mylayer(Layer):
def __init__(self, output_dim, **kwargs):
self.output_dim = output_dim
super(mylayer, self).__init__(**kwargs)
def build(self, input_shape):
self.kernel = self.add_weight(name = 'kernel',
shape=(1,),dtype='float32',trainable=True,initializer='uniform')
super(mylayer, self).build(input_shape)
def call(self, inputs, **kwargs):
return self.kernel * inputs[0]
def compute_output_shape(self, input_shape):
return (input_shape[0], input_shape[1])
#the test of mylayer.
from mylayer import mylayer
from tensorflow import keras as K
import numpy as np
from keras.layers import Input, Dense, Flatten
from keras.models import Model
x_train = np.random.random((2, 3, 4, 3))
y_train = np.random.random((2, 36))
print(x_train)
x = Input(shape=(3, 4, 3))
y = Flatten()(x)
output = mylayer((36, ))(y)
model = Model(inputs=x, outputs=output)
model.summary()
model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])
model.fit(x_train, y_train, epochs=2)
hist = model.predict(x_train,batch_size=2)
print(hist)
print(model.get_layer(index=1).get_weights())
#So is there some wrong in my custom error?
Especially, when i train this net, it's ok,but when i try to use "prdict", it's wrong.
python keras
edited Jan 1 at 9:10
Joey Mallone
2,15141731
asked Jan 1 at 8:51
cb zhangcb zhang
82
I want to make a custom layer in Keras.
In this example, I use a variable to multiply the tensor, but i get the error which is
in /keras/engine/training_arrays.py, line 304, in predict_loop
outs[i][batch_start:batch_end] = batch_out ValueError: could not broadcast input array from shape (36) into shape (2).
Actually i have check this file, but i get nothing. Is there some wrong in my custom layer?
#the definition of mylayer.
from keras import backend as K
import keras
from keras.engine.topology import Layer
class mylayer(Layer):
def __init__(self, output_dim, **kwargs):
self.output_dim = output_dim
super(mylayer, self).__init__(**kwargs)
def build(self, input_shape):
self.kernel = self.add_weight(name = 'kernel',
shape=(1,),dtype='float32',trainable=True,initializer='uniform')
super(mylayer, self).build(input_shape)
def call(self, inputs, **kwargs):
return self.kernel * inputs[0]
def compute_output_shape(self, input_shape):
return (input_shape[0], input_shape[1])
#the test of mylayer.
from mylayer import mylayer
from tensorflow import keras as K
import numpy as np
from keras.layers import Input, Dense, Flatten
from keras.models import Model
x_train = np.random.random((2, 3, 4, 3))
y_train = np.random.random((2, 36))
print(x_train)
x = Input(shape=(3, 4, 3))
y = Flatten()(x)
output = mylayer((36, ))(y)
model = Model(inputs=x, outputs=output)
model.summary()
model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])
model.fit(x_train, y_train, epochs=2)
hist = model.predict(x_train,batch_size=2)
print(hist)
print(model.get_layer(index=1).get_weights())
#So is there some wrong in my custom error?
Especially, when i train this net, it's ok,but when i try to use "prdict", it's wrong.
python keras
python keras
edited Jan 1 at 9:10
Joey Mallone
2,15141731
asked Jan 1 at 8:51
cb zhangcb zhang
82
edited Jan 1 at 9:10
Joey Mallone
2,15141731
asked Jan 1 at 8:51
cb zhangcb zhang
82
edited Jan 1 at 9:10
Joey Mallone
2,15141731
edited Jan 1 at 9:10
Joey Mallone
2,15141731
edited Jan 1 at 9:10
Joey Mallone
2,15141731
2,15141731
asked Jan 1 at 8:51
cb zhangcb zhang
82
asked Jan 1 at 8:51
cb zhangcb zhang
82
asked Jan 1 at 8:51
cb zhangcb zhang
82
82
Show us the full error traceback as code-formatted text.
– Klaus D.
Jan 1 at 8:54
add a comment |
Show us the full error traceback as code-formatted text.
– Klaus D.
Jan 1 at 8:54
Show us the full error traceback as code-formatted text.
– Klaus D.
Jan 1 at 8:54
Show us the full error traceback as code-formatted text.
– Klaus D.
Jan 1 at 8:54
add a comment |
1 Answer
1
active
oldest
votes
Your shape of self.kernel * inputs[0] is (36,), but your expectation is (?,36). Change it:
def call(self, inputs, **kwargs):
return self.kernel * inputs
If you want to output the weight of mylayer, you should set index=2.
answered Jan 1 at 9:14
giser_yuganggiser_yugang
1,7701419
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Your shape of self.kernel * inputs[0] is (36,), but your expectation is (?,36). Change it:
def call(self, inputs, **kwargs):
return self.kernel * inputs
If you want to output the weight of mylayer, you should set index=2.
answered Jan 1 at 9:14
giser_yuganggiser_yugang
1,7701419
add a comment |
Your shape of self.kernel * inputs[0] is (36,), but your expectation is (?,36). Change it:
def call(self, inputs, **kwargs):
return self.kernel * inputs
If you want to output the weight of mylayer, you should set index=2.
answered Jan 1 at 9:14
giser_yuganggiser_yugang
1,7701419
add a comment |
Your shape of self.kernel * inputs[0] is (36,), but your expectation is (?,36). Change it:
def call(self, inputs, **kwargs):
return self.kernel * inputs
If you want to output the weight of mylayer, you should set index=2.
answered Jan 1 at 9:14
giser_yuganggiser_yugang
1,7701419
Your shape of self.kernel * inputs[0] is (36,), but your expectation is (?,36). Change it:
def call(self, inputs, **kwargs):
return self.kernel * inputs
If you want to output the weight of mylayer, you should set index=2.
answered Jan 1 at 9:14
giser_yuganggiser_yugang
1,7701419
answered Jan 1 at 9:14
giser_yuganggiser_yugang
1,7701419
answered Jan 1 at 9:14
giser_yuganggiser_yugang
1,7701419
answered Jan 1 at 9:14
giser_yuganggiser_yugang
1,7701419
1,7701419
add a comment |
add a comment |
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Show us the full error traceback as code-formatted text.
– Klaus D.
Jan 1 at 8:54