How to fix TypeError: x and y must have the same dtype, got tf.int32 != tf.float32 with tensorflow using PyGad? - tensorflow

Question

I have defined a new layer to be put as an input layer using Keras. The code is:
class layer(tensorflow.keras.layers.Layer):
def __init__(self):
super(layer, self).__init__()
H_init = tf.random_normal_initializer()
self.H = tf.Variable(
initial_value=H_init(shape=(1,), dtype="float32"),
trainable=True,
)
b_init = tf.zeros_initializer()
self.b = tf.Variable(
initial_value=b_init(shape=(1,), dtype="float32"), trainable=True
)
n_init = tf.zeros_initializer()
self.n = tf.Variable(
initial_value=n_init(shape=(1,), dtype="float32"), trainable=True
)
def call(self, z):
return self.H**2 / (1+(1/self.b)**(2/self.n)) *(1+((1+z)/self.b)**(2/self.n))
I intend to put this layer as input in my generation callback function, which is
`def callback_generation(ga_instance):
print("Generation"= {generation}".format(generation=ga_instance.generations_completed))
print("Fitness =
{fitness}".format(fitness=ga_instance.best_solution()[1]))
inputs = tensorflow.keras.Input(shape=(1,), name="inputs")
targets = tensorflow.keras.Input(shape=(1,), name="targets")
logits = tensorflow.keras.layers.Dense(15)(inputs)
predictions = layer(name="predictions")(logits, targets)
model = keras.Model(inputs=[inputs, targets],
outputs=predictions)
data = {
"inputs": z,
"targets": H_z,
}
model = tensorflow.keras.Sequential(inputs=input_layer,
outputs=output_layer)
weights_vector =
tensorflow.pygad.kerasga.model_weights_as_vector(model=model)
keras_ga = pygad.kerasga.KerasGA(model=model,
num_solutions=12)
model.summary()`
This gives me an error reporting that the tf__call() method takes 2 positional arguments but 3 were given.
This is the detailed error traceback:
TypeError Traceback (most
recent call last)
<ipython-input-101-e09c7d4f3228> in <module>
20 targets = tensorflow.keras.Input(shape=(1,),
name="targets")
21 logits = tensorflow.keras.layers.Dense(15)(inputs)
---> 22 predictions = layer(name="predictions")(logits,
targets)
23
24 model = keras.Model(inputs=[inputs, targets],
outputs=predictions)
~/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in
__call__(self, *args, **kwargs)
950 if _in_functional_construction_mode(self, inputs,
args, kwargs, input_list):
951 return self._functional_construction_call(inputs,
args, kwargs,
--> 952
input_list)
953
954 # Maintains info about the `Layer.call` stack.
~/anaconda3/lib/python3.7/site-
packages/tensorflow/python/keras/engine/base_layer.py in
_functional_construction_call(self, inputs, args, kwargs,
input_list)
1089 # Check input assumptions set after layer
building, e.g. input shape.
1090 outputs = self._keras_tensor_symbolic_call(
-> 1091 inputs, input_masks, args, kwargs)
1092
1093 if outputs is None:
~/anaconda3/lib/python3.7/site-
packages/tensorflow/python/keras/engine/base_layer.py in
_keras_tensor_symbolic_call(self, inputs, input_masks, args,
kwargs)
820 return
nest.map_structure(keras_tensor.KerasTensor, output_signature)
821 else:
--> 822 return self._infer_output_signature(inputs, args,
kwargs, input_masks)
823
824 def _infer_output_signature(self, inputs, args,
kwargs, input_masks):
~/anaconda3/lib/python3.7/site-
packages/tensorflow/python/keras/engine/base_layer.py in
_infer_output_signature(self, inputs, args, kwargs,
input_masks)
861 # TODO(kaftan): do we maybe_build here, or
have we already done it?
862 self._maybe_build(inputs)
--> 863 outputs = call_fn(inputs, *args, **kwargs)
864
865 self._handle_activity_regularization(inputs,
outputs)
~/anaconda3/lib/python3.7/site-
packages/tensorflow/python/autograph/impl/api.py in
wrapper(*args, **kwargs)
668 except Exception as e: # pylint:disable=broad-
except
669 if hasattr(e, 'ag_error_metadata'):
--> 670 raise e.ag_error_metadata.to_exception(e)
671 else:
672 raise
TypeError: in user code:
TypeError: tf__call() takes 2 positional arguments but 3 were given