CODEWITHSUNDEEP
tensorflowkeraskeras-layerkeras-2
Darshi
Darshi

Reputation: 55

Error in creating custom initializer using get_variable with Keras

I created a custom initializer with Keras. Part of the code is: 

def my_init(shape):
    P = tf.get_variable("P", shape=shape,    initializer = tf.contrib.layers.xavier_initializer())
    return P

model = Sequential()
model.add(Conv2D(32, kernel_size=(5, 5),strides=(1, 1), padding='same', input_shape = input_shape, kernel_initializer = my_init))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, kernel_size=(1, 1) , strides=(1, 1) , padding='same' , kernel_initializer = my_init))

When "my_init" initializer is called for the second time in the convolution layer it throws this error: 

Variable P already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:

It is not allowing to reuse the variable P. Is there any way to create a new variable in each call?

Upvotes: 1

Views: 1413

Answers (1)

Daniel Möller
Daniel Möller

Reputation: 86600

You could try using the Xavier initializers available in Keras, under the names glorot_uniform and glorot_normal.

See them here: https://keras.io/initializers/

model.add(Conv2D(32, kernel_size=(1, 1) , strides=(1, 1) , 
          padding='same' , kernel_initializer =keras.initializers.glorot_uniform())

Upvotes: 1

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