Issue retraining the classifier

[smeisler]

Hello,

I am trying to retrain the classifier. I have started by randomizing the weights by including the following lines in the load_weights function:

for key1 in weights_biases.keys():
        for key2 in weights_biases[key1].keys():
            weights_biases[key1][key2] = np.random.normal(loc=0.0, scale=1.0, size=np.size(weights_biases[key1][key2]))

I then try to compile and fit the network:

model = branched_network()
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
model.fit(
    np.asarray(inputs), np.asarray(targets), epochs=1, verbose=1)

Inputs is a list of numpy arrays for cochleagrams, and targets is an array of integers corresponding to word outcomes.
I get the following error when running the fit.

Tensor("branched_network/Cast:0", shape=(None, 1, 65536), dtype=float64)
Tensor("branched_network/Cast_1:0", shape=(None, 1, 65536), dtype=float64)
Tensor("branched_network/Cast_2:0", shape=(None, 256, 256, 1), dtype=float64)


TypeError                                 Traceback (most recent call last)
<ipython-input-205-6366830757a9> in <module>
      1 tf.keras.backend.clear_session()
----> 2 model.call(inputs[0],'speech')

<ipython-input-203-b351b5e9b5da> in call(self, x, type)
     73     for layer in self.commun :
     74         print(tf.dtypes.cast(x, tf.float64))
---> 75         x = layer(tf.dtypes.cast(x, tf.float32))
     76     if type == "speech":
     77       for layer in self.speech: x = layer(tf.dtypes.cast(x, tf.float32))

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
    983 
    984         with ops.enable_auto_cast_variables(self._compute_dtype_object):
--> 985           outputs = call_fn(inputs, *args, **kwargs)
    986 
    987         if self._activity_regularizer:

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
    778       else:
    779         compiler = "nonXla"
--> 780         result = self._call(*args, **kwds)
    781 
    782       new_tracing_count = self._get_tracing_count()

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    821       # This is the first call of __call__, so we have to initialize.
    822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
    824     finally:
    825       # At this point we know that the initialization is complete (or less

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
    694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
    695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
    697             *args, **kwds))
    698 

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
   2853       args, kwargs = None, None
   2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
   2856     return graph_function
   2857 

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   3211 
   3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
   3214       self._function_cache.primary[cache_key] = graph_function
   3215       return graph_function, args, kwargs

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   3063     arg_names = base_arg_names + missing_arg_names
   3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
   3066             self._name,
   3067             self._python_function,

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
    984         _, original_func = tf_decorator.unwrap(python_func)
    985 
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
    987 
    988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
    598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
    599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
    601     weak_wrapped_fn = weakref.ref(wrapped_fn)
    602 

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in bound_method_wrapper(*args, **kwargs)
   3733     # However, the replacer is still responsible for attaching self properly.
   3734     # TODO(mdan): Is it possible to do it here instead?
-> 3735     return wrapped_fn(*args, **kwargs)
   3736   weak_bound_method_wrapper = weakref.ref(bound_method_wrapper)
   3737 

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    971           except Exception as e:  # pylint:disable=broad-except
    972             if hasattr(e, "ag_error_metadata"):
--> 973               raise e.ag_error_metadata.to_exception(e)
    974             else:
    975               raise

TypeError: in user code:

    <ipython-input-6-cee54ce53363>:15 call  *
        x = tf.nn.conv2d( previous_h, self.w,
    /Users/smeisler/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:201 wrapper  **
        return target(*args, **kwargs)
    /Users/smeisler/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/nn_ops.py:2158 conv2d_v2
        return conv2d(input,  # pylint: disable=redefined-builtin
    /Users/smeisler/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
        return target(*args, **kwargs)
    /Users/smeisler/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/nn_ops.py:2264 conv2d
        return gen_nn_ops.conv2d(
    /Users/smeisler/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/gen_nn_ops.py:975 conv2d
        _, _, _op, _outputs = _op_def_library._apply_op_helper(
    /Users/smeisler/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/op_def_library.py:503 _apply_op_helper
        raise TypeError(

    TypeError: Input 'filter' of 'Conv2D' Op has type float64 that does not match type float32 of argument 'input'.

I have tried casting the tensor to different data types before passing them in to the next layer, but I continue receiving the same errors. Do you know how I may fix this?

Best,
Steven