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model.fit AttributeError: 'tuple' object has no attribute 'shape'

I have a problem with training my multi input model. I have built it with the following code piece:
def create_covn_layers(input_layer):
input = layers.Conv2D(32, (3,3), input_shape=get_img_input_shape(True))(input_layer)
covn01 = layers.Conv2D(32, (3, 3))(input)
acti01 = layers.Activation('relu')(covn01)
pool01 = layers.MaxPooling2D((2, 2))(acti01)
covn02 = layers.Conv2D(64, (3, 3))(pool01)
acti02 = layers.Activation('relu')(covn02)
pool02 = layers.MaxPooling2D(2, 2)(acti02)
covn03 = layers.Conv2D(128, (3, 3))(pool02)
acti02 = layers.Activation('relu')(covn03)
pool02 = layers.MaxPooling2D(pool_size=(2,2), padding='same')(acti02)
covn_base = layers.Dropout(0.2)(pool02)
return covn_base
#flat = layers.Flatten()(pool03)
model_one_input = layers.Input(shape=get_img_input_shape(True))
model_one = create_covn_layers(model_one_input)
model_two_input = layers.Input(shape=get_img_input_shape(True))
model_two = create_covn_layers(model_two_input)
concat_feature_layer = layers.concatenate([model_one, model_two])
flatten_layer = layers.Flatten()(concat_feature_layer)
fully_connected_dense_big = layers.Dense(256, activation='relu')(flatten_layer)
dropout_one = layers.Dropout(0.3)(fully_connected_dense_big)
fully_connected_dense_small = layers.Dense(128, activation='relu')(dropout_one)
dropout_two = layers.Dropout(0.3)(fully_connected_dense_small)
output = layers.Dense(3, activation='softmax')(dropout_two)
model = Model(
inputs=[model_one_input, model_two_input],
outputs=output
)
The input layers accepts the following shape:
batch_size = 18
def get_img_input_shape(for_model=False):
if for_model:
return(299,299,3)
return (299, 299)
[![image shape layer][1]][1]
The model structure:
https://imgur.com/eNtPnjA
I have built a custom generator thats takes two generators with flowfromdataframe and output two input and one label.
train_generator_one = ImageDataGenerator(
rescale = 1./255,
validation_split=0.2
)
train_generator_two = ImageDataGenerator(
rescale = 1./255,
validation_split=0.2
)
input_1_train_gen = train_generator_one.flow_from_dataframe(
balanced_eeg_data,
batch_size=batch_size,
target_size=get_img_input_shape(),
shuffle=False,
color_mode="rgb",
class_mode="categorical",
subset="training")
input_2_train_gen = train_generator_two.flow_from_dataframe(
balanced_ecg_data,
batch_size=batch_size,
target_size=get_img_input_shape(),
shuffle=False,
color_mode="rgb",
class_mode="categorical",
subset="training")
input_1_validation_gen = train_generator_one.flow_from_dataframe(
balanced_eeg_data,
batch_size=batch_size,
target_size=get_img_input_shape(),
shuffle=False,
color_mode="rgb",
class_mode="categorical",
subset="validation")
input_2_validation_gen = train_generator_two.flow_from_dataframe(
balanced_ecg_data,
batch_size=batch_size,
target_size=get_img_input_shape(),
shuffle=False,
color_mode="rgb",
class_mode="categorical",
subset="validation")
def create_data_generator(data_gen_one, data_gen_two):
while(True):
_gen1, _gen1_l = next(data_gen_one)
_gen2, _gen2_l = next(data_gen_two)
yield [_gen1, _gen2], [_gen1_l]
multi_train_generator = create_data_generator(
input_1_train_gen,
input_2_train_gen
)
multi_validation_generator = create_data_generator(
input_1_validation_gen,
input_2_validation_gen
)
When i call the model.fit however it gives an attribute error:
history = model.fit(
multi_train_generator,
epochs=2,
steps_per_epoch = input_1_train_gen.samples//batch_size,
validation_data=multi_validation_generator,
validation_steps = input_1_validation_gen.samples//batch_size,
)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/var/folders/0v/m6wt8rqj7s1dcljdyjrdfxmw0000gn/T/ipykernel_84306/4129641024.py in <module>
----> 1 history = model.fit(
2 multi_train_generator,
3 epochs=2,
4 steps_per_epoch = input_1_train_gen.samples//batch_size,
5 validation_data=multi_validation_generator,
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1181 _r=1):
1182 callbacks.on_train_batch_begin(step)
-> 1183 tmp_logs = self.train_function(iterator)
1184 if data_handler.should_sync:
1185 context.async_wait()
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
887
888 with OptionalXlaContext(self._jit_compile):
--> 889 result = self._call(*args, **kwds)
890
891 new_tracing_count = self.experimental_get_tracing_count()
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
931 # This is the first call of __call__, so we have to initialize.
932 initializers = []
--> 933 self._initialize(args, kwds, add_initializers_to=initializers)
934 finally:
935 # At this point we know that the initialization is complete (or less
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
761 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
762 self._concrete_stateful_fn = (
--> 763 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
764 *args, **kwds))
765
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
3048 args, kwargs = None, None
3049 with self._lock:
-> 3050 graph_function, _ = self._maybe_define_function(args, kwargs)
3051 return graph_function
3052
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3442
3443 self._function_cache.missed.add(call_context_key)
-> 3444 graph_function = self._create_graph_function(args, kwargs)
3445 self._function_cache.primary[cache_key] = graph_function
3446
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3277 arg_names = base_arg_names + missing_arg_names
3278 graph_function = ConcreteFunction(
-> 3279 func_graph_module.func_graph_from_py_func(
3280 self._name,
3281 self._python_function,
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/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)
997 _, original_func = tf_decorator.unwrap(python_func)
998
--> 999 func_outputs = python_func(*func_args, **func_kwargs)
1000
1001 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
670 # the function a weak reference to itself to avoid a reference cycle.
671 with OptionalXlaContext(compile_with_xla):
--> 672 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
673 return out
674
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
984 except Exception as e: # pylint:disable=broad-except
985 if hasattr(e, "ag_error_metadata"):
--> 986 raise e.ag_error_metadata.to_exception(e)
987 else:
988 raise
AttributeError: in user code:
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/training.py:855 train_function *
return step_function(self, iterator)
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/training.py:845 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/distribute/distribute_lib.py:1285 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/distribute/distribute_lib.py:2833 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/distribute/distribute_lib.py:3608 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/training.py:838 run_step **
outputs = model.train_step(data)
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/training.py:800 train_step
self.compiled_metrics.update_state(y, y_pred, sample_weight)
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/compile_utils.py:439 update_state
self.build(y_pred, y_true)
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/compile_utils.py:361 build
self._metrics = nest.map_structure_up_to(y_pred, self._get_metric_objects,
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/util/nest.py:1374 map_structure_up_to
return map_structure_with_tuple_paths_up_to(
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/util/nest.py:1472 map_structure_with_tuple_paths_up_to
results = [
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/util/nest.py:1473 <listcomp>
func(*args, **kwargs) for args in zip(flat_path_gen, *flat_value_gen)
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/util/nest.py:1376 <lambda>
lambda _, *values: func(*values), # Discards the path arg.
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/compile_utils.py:485 _get_metric_objects
return [self._get_metric_object(m, y_t, y_p) for m in metrics]
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/compile_utils.py:485 <listcomp>
return [self._get_metric_object(m, y_t, y_p) for m in metrics]
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/compile_utils.py:506 _get_metric_object
y_t_rank = len(y_t.shape.as_list())
AttributeError: 'tuple' object has no attribute 'shape'
Can anyone help or point me to where the issue is?
The dataframe are identical except for the paths.
UPDATE:
I found out that metrics['acc] is coursing this problem... very annoying...
However why i fails i haven't found out yet.
[1]: https://i.stack.imgur.com/AU6HU.png

So for anyone else who runs into this i have found the problem.
OBS: This problem doesn't occur when using a Sequential model... don't know why.
However when you're hot encoding the labels like below as i did:
And using a multi input model with generators like:
Then don't use metrics=['acc'] this does not work and you will get attribute error.
See the following: https://www.tensorflow.org/api_docs/python/tf/keras/metrics/CategoricalAccuracy
Use the tf.keras.metrics.CategoricalAccuracy
This works with hot encoded labels.

ValueError: No gradients provided for any variable while doing regression for integer values, which include negatives using keras

I have a problem where I need to predict some integers from an image. The problem is that this includes some negative integers too. I have done some reasearch and came accross Poisson which does count regression, however this does not work due to me also needing to predict some negative integers too, resulting in Poisson output nan as its loss. I was thinking of using Lambda to round the output of my model however this resulted in this error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/var/folders/nc/c4mgwn897qbg8g52tp3mhbjr0000gp/T/ipykernel_8618/1788039059.py in <module>
----> 1 model.fit(x_train, y_train,callbacks=[callback], epochs = 999)
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1181 _r=1):
1182 callbacks.on_train_batch_begin(step)
-> 1183 tmp_logs = self.train_function(iterator)
1184 if data_handler.should_sync:
1185 context.async_wait()
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
887
888 with OptionalXlaContext(self._jit_compile):
--> 889 result = self._call(*args, **kwds)
890
891 new_tracing_count = self.experimental_get_tracing_count()
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
931 # This is the first call of __call__, so we have to initialize.
932 initializers = []
--> 933 self._initialize(args, kwds, add_initializers_to=initializers)
934 finally:
935 # 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)
761 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
762 self._concrete_stateful_fn = (
--> 763 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
764 *args, **kwds))
765
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
3048 args, kwargs = None, None
3049 with self._lock:
-> 3050 graph_function, _ = self._maybe_define_function(args, kwargs)
3051 return graph_function
3052
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3442
3443 self._function_cache.missed.add(call_context_key)
-> 3444 graph_function = self._create_graph_function(args, kwargs)
3445 self._function_cache.primary[cache_key] = graph_function
3446
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3277 arg_names = base_arg_names + missing_arg_names
3278 graph_function = ConcreteFunction(
-> 3279 func_graph_module.func_graph_from_py_func(
3280 self._name,
3281 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)
997 _, original_func = tf_decorator.unwrap(python_func)
998
--> 999 func_outputs = python_func(*func_args, **func_kwargs)
1000
1001 # 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)
670 # the function a weak reference to itself to avoid a reference cycle.
671 with OptionalXlaContext(compile_with_xla):
--> 672 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
673 return out
674
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
984 except Exception as e: # pylint:disable=broad-except
985 if hasattr(e, "ag_error_metadata"):
--> 986 raise e.ag_error_metadata.to_exception(e)
987 else:
988 raise
ValueError: in user code:
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:855 train_function *
return step_function(self, iterator)
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:845 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:1285 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2833 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:3608 _call_for_each_replica
return fn(*args, **kwargs)
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:838 run_step **
outputs = model.train_step(data)
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:799 train_step
self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:530 minimize
return self.apply_gradients(grads_and_vars, name=name)
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:630 apply_gradients
grads_and_vars = optimizer_utils.filter_empty_gradients(grads_and_vars)
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/utils.py:75 filter_empty_gradients
raise ValueError("No gradients provided for any variable: %s." %
ValueError: No gradients provided for any variable: ['conv2d_2/kernel:0', 'conv2d_2/bias:0', 'conv2d_3/kernel:0', 'conv2d_3/bias:0', 'dense_3/kernel:0', 'dense_3/bias:0', 'dense_4/kernel:0', 'dense_4/bias:0', 'dense_5/kernel:0', 'dense_5/bias:0'].
Here is my implimentation of the Lambda layer thing:
filter_size = (3,3)
filters = 32
pool = 2
input_layer = keras.Input(shape=(100,300,1))
conv_extractor = layers.Conv2D(filters,filter_size, activation='relu')(input_layer)
conv_extractor = layers.MaxPooling2D(pool_size=(pool, pool))(conv_extractor)
conv_extractor = layers.Conv2D(filters,filter_size, activation='relu')(conv_extractor)
conv_extractor = layers.MaxPooling2D(pool_size=(pool, pool))(conv_extractor)
#conv_extractor = layers.Reshape(target_shape=(100 // (pool ** 2), (100 // (pool ** 2)) * filters))(conv_extractor)
shape = ((100 // 4), (300 // 4) * 32)
#conv_extractor = layers.Dense(512, activation='relu')(conv_extractor)
conv_extractor = layers.Reshape(target_shape=(23,2336))(conv_extractor)
gru_1 = GRU(512, return_sequences=True)(conv_extractor)
gru_1b = GRU(512, return_sequences=True, go_backwards=True)(conv_extractor)
gru1_merged = add([gru_1, gru_1b])
gru_2 = GRU(512, return_sequences=True)(gru1_merged)
gru_2b = GRU(512, return_sequences=True, go_backwards=True)(gru1_merged)
x = layers.concatenate([gru_2, gru_2b]) # move concatenate layer aside
x = layers.Flatten()(x)
inner = layers.Dense(30, activation='LeakyReLU')(x)
inner = layers.Dense(10, activation='LeakyReLU')(inner)
inner = layers.Dense(3, activation='LeakyReLU')(inner)
inner layers.Lambda(keras.backend.round)(inner)
model = Model(input_layer,inner)
model.compile(loss = "MeanSquaredError", optimizer = optimizers.Adam(2e-4), metrics=['accuracy'])
model.fit(x_train, y_train, epochs = 999)
Why did I get this error? And how can I fix it? If it's not fixable, is there another way of solving my problem (e.g by modifying the poisson loss function)?

Add the smallest value (in this case is negative) so that everything is >= 0. Then use Poisson.

Localisation CNN in Keras

I have a machine learning CNN which is supposed to classify and localise objects in present in a image. The input is a RGB image of dimensions (448, 448, 3), and the output is a 24 (for the bounding box coordinates, as there are multiple objects in a image, a maximum of 6, and 6 id's of the image present. Here is how I defined the model:
def define_model():
input = Input(shape = (448,448,3)
x = Conv2D(32, (3,3), activation = 'relu')(input)
x = MaxPooling2D((3,3))(x)
x = Conv2D(64, (3,3), activation = 'relu')(x)
x = MaxPooling2D((3,3))(x)
x = Conv2D(64, (3,3), activation = 'relu')(x)
x = Conv2D(32, (3,3), activation = 'relu')(x)
x = GlobalAveragePooling2D()(x)
classification_output_head = Flatten()(x)
classification_output_head = Dropout(0.1)(classification_output_head)
classification_output_head = Dense(6, activation = 'sigmoid', name = 'class_of_objs')(classification_output_head)
regression_for_bounding_box_output_head = Flatten()(x)
regression_for_bounding_box_output_head = Dense(64, activation = 'relu')((regression_for_bounding_box_output_head))
regression_for_bounding_box_output_head = Dense(32, activation = 'relu')(regression_for_bounding_box_output_head)
regression_for_bounding_box_output_head = Dense(24, activation = 'sigmoid', name = 'bounding_box')(regression_for_bounding_box_output_head)
return Model(inputs=[input], outputs=[classification_output_head, regression_for_bounding_box_output_head])
I then define the model:
model = define_model()
Heres the rest:
losses = {'class_of_objs': 'sparse_categorical_crossentropy',
'bounding_box': 'mse'}
model.compile('adam', loss=losses, metrics=['mae'])
tloader = tf.data.Dataset.from_tensor_slices((x, y_class, y_bbox))
model.fit(tloader,epochs=100)
When I run model.fit I get this error:
Epoch 1/100
WARNING:tensorflow:Model was constructed with shape (None, 448, 448, 3) for input Tensor("input_25:0", shape=(None, 448, 448, 3), dtype=float32), but it was called on an input with incompatible shape (448, 448, 3).
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-308-70dc0cc6919c> in <module>
----> 1 model.fit(tloader,epochs=100)
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
106 def _method_wrapper(self, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self, *args, **kwargs)
109
110 # Running inside `run_distribute_coordinator` already.
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing) 1096 batch_size=batch_size): 1097 callbacks.on_train_batch_begin(step)
-> 1098 tmp_logs = train_function(iterator) 1099 if data_handler.should_sync: 1100 context.async_wait()
~/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/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
How do I fix it

Before calling model.fit, you have to compile the model with model.complile on which you set the losse and the optimizer chosen like this:model.compile(optimizer="Adam", loss="mse", metrics=["mae"])

How to use 'Recall' as metric in keras classifier?

How can use use the 'Recall' and other metrics in keras classifier. The following code only works for accuracy but if I change the metric to recall it fails.
Versions
"""
[('numpy', '1.19.1'),
('pandas', '1.1.1'),
('sklearn', '0.23.2'),
('tensorflow', '2.3.0'),
('keras', '2.4.3')]
"""
MWE
import numpy as np
import pandas as pd
import sklearn
import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers.core import Dense
SEED = 100
np.random.seed(100)
X = np.random.random((20, 3))
y = np.random.randint(0,2,size=20)
print(x.shape, y.shape) # (20, 3) (20,)
n_inputs = X.shape[1]
model = Sequential([
Dense(n_inputs, input_shape=(n_inputs, ), activation='relu'),
Dense(32, activation='relu'),
Dense(2, activation='softmax')
])
METRIC = 'Recall' # Recall fails
# METRIC = 'accuracy' # accuracy works
model.compile('adam',loss='binary_crossentropy',metrics=[METRIC])
model.fit(X, y,validation_split=0.2,epochs=1)
Error
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-17-b372311b0ed4> in <module>
38 epochs=10,
39 shuffle=True,
---> 40 verbose=5)
~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
106 def _method_wrapper(self, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self, *args, **kwargs)
109
110 # Running inside `run_distribute_coordinator` already.
~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1096 batch_size=batch_size):
1097 callbacks.on_train_batch_begin(step)
-> 1098 tmp_logs = train_function(iterator)
1099 if data_handler.should_sync:
1100 context.async_wait()
~/opt/miniconda3/envs/tf2/lib/python3.7/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/miniconda3/envs/tf2/lib/python3.7/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/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
695 self._concrete_stateful_fn = (
696 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 697 *args, **kwds))
698
699 def invalid_creator_scope(*unused_args, **unused_kwds):
~/opt/miniconda3/envs/tf2/lib/python3.7/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/miniconda3/envs/tf2/lib/python3.7/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/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3073 arg_names=arg_names,
3074 override_flat_arg_shapes=override_flat_arg_shapes,
-> 3075 capture_by_value=self._capture_by_value),
3076 self._function_attributes,
3077 function_spec=self.function_spec,
~/opt/miniconda3/envs/tf2/lib/python3.7/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/miniconda3/envs/tf2/lib/python3.7/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/miniconda3/envs/tf2/lib/python3.7/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
ValueError: in user code:
/Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:806 train_function *
return step_function(self, iterator)
/Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
/Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:789 run_step **
outputs = model.train_step(data)
/Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:759 train_step
self.compiled_metrics.update_state(y, y_pred, sample_weight)
/Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/compile_utils.py:409 update_state
metric_obj.update_state(y_t, y_p, sample_weight=mask)
/Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/utils/metrics_utils.py:90 decorated
update_op = update_state_fn(*args, **kwargs)
/Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/metrics.py:176 update_state_fn
return ag_update_state(*args, **kwargs)
/Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/metrics.py:1410 update_state **
sample_weight=sample_weight)
/Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/utils/metrics_utils.py:353 update_confusion_matrix_variables
y_pred.shape.assert_is_compatible_with(y_true.shape)
/Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/framework/tensor_shape.py:1134 assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (None, 2) and (None, 1) are incompatible

Change the final layer output 2 to 1.
import numpy as np
import pandas as pd
import sklearn
import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers.core import Dense
SEED = 100
np.random.seed(100)
X = np.random.random((20, 3))
y = np.random.randint(0,2,size=20)
print(X.shape, y.shape) # (20, 3) (20,)
n_inputs = X.shape[1]
model = Sequential([
Dense(n_inputs, input_shape=(n_inputs, ), activation='relu'),
Dense(32, activation='relu'),
Dense(1, activation='relu')
])
METRIC = 'Recall' # Recall fails
# METRIC = 'accuracy' # accuracy works
model.compile('adam',loss='binary_crossentropy',metrics=[METRIC])
model.fit(X, y,validation_split=0.2,epochs=1)

ValueError: Shapes (1, 4) and (1, 3) are incompatible

I use a simple neural learning to classify iris.csv on Jupyter notebook.
I set 1 Hidden layer include 10 hidden unit.
'iris.csv' has 4 features and 1 results, and the results have 3 Species.
So I set 4 input_shape and 3 out in Dense layer.
Code:
import seaborn
import numpy
import pandas
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.utils import np_utils
data = pandas.read_csv('./data/iris.csv')
data.head()
class_as_one_hot_encoding = pandas.get_dummies(data['Species'])
del data['Species']
data = pandas.concat([data, class_as_one_hot_encoding], axis=1)
data.head()
x = data.values[:,:4]
y = data.values[:,4:]
train_x, test_x, train_y, test_y = train_test_split(x, y, train_size=0.7)
model = Sequential()
model.add(Dense(10, input_shape=(4,)))
model.add(Activation('sigmoid'))
model.add(Dense(3))
model.add(Activation('softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=["accuracy"])
model_info = model.fit(train_x, train_y, epochs=100, batch_size=1 )
ValueError: Shapes (1, 4) and (1, 3) are incompatible full error message as below.
I do not know how to fit it.
Error:
Epoch 1/100
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-23-b5e5622d976c> in <module>
8 model.add(Activation('softmax'))
9 model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=["accuracy"])
---> 10 model_info = model.fit(train_x, train_y, epochs=100, batch_size=1 )
~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
106 def _method_wrapper(self, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self, *args, **kwargs)
109
110 # Running inside `run_distribute_coordinator` already.
~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1096 batch_size=batch_size):
1097 callbacks.on_train_batch_begin(step)
-> 1098 tmp_logs = train_function(iterator)
1099 if data_handler.should_sync:
1100 context.async_wait()
~\anaconda3\lib\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()
~\anaconda3\lib\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
~\anaconda3\lib\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
~\anaconda3\lib\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
~\anaconda3\lib\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
~\anaconda3\lib\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,
~\anaconda3\lib\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,
~\anaconda3\lib\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
~\anaconda3\lib\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
ValueError: in user code:
C:\Users\Admin\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:806 train_function *
return step_function(self, iterator)
C:\Users\Admin\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\Users\Admin\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Users\Admin\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\Users\Admin\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
C:\Users\Admin\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:789 run_step **
outputs = model.train_step(data)
C:\Users\Admin\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:748 train_step
loss = self.compiled_loss(
C:\Users\Admin\anaconda3\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:204 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
C:\Users\Admin\anaconda3\lib\site-packages\tensorflow\python\keras\losses.py:149 __call__
losses = ag_call(y_true, y_pred)
C:\Users\Admin\anaconda3\lib\site-packages\tensorflow\python\keras\losses.py:253 call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
C:\Users\Admin\anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
return target(*args, **kwargs)
C:\Users\Admin\anaconda3\lib\site-packages\tensorflow\python\keras\losses.py:1535 categorical_crossentropy
return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
C:\Users\Admin\anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
return target(*args, **kwargs)
C:\Users\Admin\anaconda3\lib\site-packages\tensorflow\python\keras\backend.py:4687 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
C:\Users\Admin\anaconda3\lib\site-packages\tensorflow\python\framework\tensor_shape.py:1134 assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (1, 4) and (1, 3) are incompatible