TypeError

[carlotolopes]

Hi! While running this part of code:

model = get_siamese_model((105, 105, 1))
model.summary()

I got this error:

TypeError Traceback (most recent call last)
in
----> 1 model = get_siamese_model((105, 105, 1))
2 model.summary()

in get_siamese_model(input_shape)
11 model = Sequential()
12 model.add(Conv2D(64, (10,10), activation='relu', input_shape=input_shape,
---> 13 kernel_initializer=initialize_weights, kernel_regularizer=l2(2e-4)))
14 model.add(MaxPooling2D())
15 model.add(Conv2D(128, (7,7), activation='relu',

~\Anaconda3\lib\site-packages\keras\engine\sequential.py in add(self, layer)
164 # and create the node connecting the current layer
165 # to the input layer we just created.
--> 166 layer(x)
167 set_inputs = True
168 else:

~\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py in symbolic_fn_wrapper(*args, **kwargs)
73 if _SYMBOLIC_SCOPE.value:
74 with get_graph().as_default():
---> 75 return func(*args, **kwargs)
76 else:
77 return func(*args, **kwargs)

~\Anaconda3\lib\site-packages\keras\engine\base_layer.py in call(self, inputs, **kwargs)
461 'You can build it manually via: '
462 'layer.build(batch_input_shape)')
--> 463 self.build(unpack_singleton(input_shapes))
464 self.built = True
465

~\Anaconda3\lib\site-packages\keras\layers\convolutional.py in build(self, input_shape)
139 name='kernel',
140 regularizer=self.kernel_regularizer,
--> 141 constraint=self.kernel_constraint)
142 if self.use_bias:
143 self.bias = self.add_weight(shape=(self.filters,),

~\Anaconda3\lib\site-packages\keras\engine\base_layer.py in add_weight(self, name, shape, dtype, initializer, regularizer, trainable, constraint)
277 if dtype is None:
278 dtype = self.dtype
--> 279 weight = K.variable(initializer(shape, dtype=dtype),
280 dtype=dtype,
281 name=name,

TypeError: initialize_weights() got an unexpected keyword argument 'dtype'

Is it related to my tf version?
My current tf version is 2.0 gpu

[aifromphytsai]

You may try this way. Rename the second argument of the following functions as "dtype":
def initialize_weights(shape, dtype=None):
return np.random.normal(loc = 0.0, scale = 1e-2, size = shape)
def initialize_bias(shape, dtype=None):
return np.random.normal(loc = 0.5, scale = 1e-2, size = shape)