If I try to predict with my model with 3 inputs with this code:
predicted_seaLevel = []
prediction = model2.predict(
[[np.array(X[0][0])],[np.array(X[1][0])],[np.array(X[2][0])]],
batch_size=None,
verbose='auto',
steps=None,
callbacks=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False
)
predicted_seaLevel.append(prediction)
I get this error:
IndexError Traceback (most recent call last)
Input In [119], in <cell line: 3>()
1 predicted_seaLevel = []
----> 3 prediction = model2.predict(
4 [[np.array(X[0][0])],[np.array(X[1][0])],[np.array(X[2][0])]],
5 batch_size=None,
6 verbose='auto',
7 steps=None,
8 callbacks=None,
9 max_queue_size=10,
10 workers=1,
11 use_multiprocessing=False
12 )
13 predicted_seaLevel.append(prediction)
File ~\.conda\envs\handwritingNumbersAI\lib\site-packages\keras\engine\training.py:1720, in Model.predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
1714 except ValueError:
1715 warnings.warn('Using Model.predict with '
1716 'MultiWorkerDistributionStrategy or TPUStrategy and '
1717 'AutoShardPolicy.FILE might lead to out-of-order result'
1718 '. Consider setting it to AutoShardPolicy.DATA.')
-> 1720 data_handler = data_adapter.get_data_handler(
1721 x=x,
1722 batch_size=batch_size,
1723 steps_per_epoch=steps,
1724 initial_epoch=0,
1725 epochs=1,
1726 max_queue_size=max_queue_size,
1727 workers=workers,
1728 use_multiprocessing=use_multiprocessing,
1729 model=self,
1730 steps_per_execution=self._steps_per_execution)
1732 # Container that configures and calls `tf.keras.Callback`s.
1733 if not isinstance(callbacks, callbacks_module.CallbackList):
File ~\.conda\envs\handwritingNumbersAI\lib\site-packages\keras\engine\data_adapter.py:1383, in get_data_handler(*args, **kwargs)
1381 if getattr(kwargs["model"], "_cluster_coordinator", None):
1382 return _ClusterCoordinatorDataHandler(*args, **kwargs)
-> 1383 return DataHandler(*args, **kwargs)
File ~\.conda\envs\handwritingNumbersAI\lib\site-packages\keras\engine\data_adapter.py:1138, in DataHandler.__init__(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution, distribute)
1135 self._steps_per_execution_value = steps_per_execution.numpy().item()
1137 adapter_cls = select_data_adapter(x, y)
-> 1138 self._adapter = adapter_cls(
1139 x,
1140 y,
1141 batch_size=batch_size,
1142 steps=steps_per_epoch,
1143 epochs=epochs - initial_epoch,
1144 sample_weights=sample_weight,
1145 shuffle=shuffle,
1146 max_queue_size=max_queue_size,
1147 workers=workers,
1148 use_multiprocessing=use_multiprocessing,
1149 distribution_strategy=tf.distribute.get_strategy(),
1150 model=model)
1152 strategy = tf.distribute.get_strategy()
1154 self._current_step = 0
File ~\.conda\envs\handwritingNumbersAI\lib\site-packages\keras\engine\data_adapter.py:240, in TensorLikeDataAdapter.__init__(self, x, y, sample_weights, sample_weight_modes, batch_size, epochs, steps, shuffle, **kwargs)
235 (sample_weights, _, _) = training_utils.handle_partial_sample_weights(
236 y, sample_weights, sample_weight_modes, check_all_flat=True)
238 inputs = pack_x_y_sample_weight(x, y, sample_weights)
--> 240 num_samples = set(int(i.shape[0]) for i in tf.nest.flatten(inputs)).pop()
241 _check_data_cardinality(inputs)
243 # If batch_size is not passed but steps is, calculate from the input data.
244 # Default to 32 for backwards compat.
File ~\.conda\envs\handwritingNumbersAI\lib\site-packages\keras\engine\data_adapter.py:240, in <genexpr>(.0)
235 (sample_weights, _, _) = training_utils.handle_partial_sample_weights(
236 y, sample_weights, sample_weight_modes, check_all_flat=True)
238 inputs = pack_x_y_sample_weight(x, y, sample_weights)
--> 240 num_samples = set(int(i.shape[0]) for i in tf.nest.flatten(inputs)).pop()
241 _check_data_cardinality(inputs)
243 # If batch_size is not passed but steps is, calculate from the input data.
244 # Default to 32 for backwards compat.
File ~\.conda\envs\handwritingNumbersAI\lib\site-packages\tensorflow\python\framework\tensor_shape.py:896, in TensorShape.__getitem__(self, key)
894 else:
895 if self._v2_behavior:
--> 896 return self._dims[key].value
897 else:
898 return self._dims[key]
IndexError: list index out of range
This my Model:
def simpleFunctional():
input1 = keras.layers.Input(shape=(1,))
input2 = keras.layers.Input(shape=(1,))
input3 = keras.layers.Input(shape=(1,))
merged = keras.layers.Concatenate(axis=1)([input1, input2, input3])
dense1 = keras.layers.Dense(16, input_dim=2, activation=keras.activations.relu, use_bias=True)(merged)
dense2 = keras.layers.Dense(32, input_dim=2, activation=keras.activations.relu, use_bias=True)(dense1)
dense3 = keras.layers.Dense(64, input_dim=2, activation=keras.activations.relu, use_bias=True)(dense2)
dense4 = keras.layers.Dense(32, input_dim=2, activation=keras.activations.relu, use_bias=True)(dense3)
dense5 = keras.layers.Dense(32, input_dim=2, activation=keras.activations.relu, use_bias=True)(dense4)
output = keras.layers.Dense(1, activation=keras.activations.relu, use_bias=True)(dense5)
model = keras.models.Model(inputs=[input1, input2, input3], outputs=output)
#opt = SGD(learning_rate=0.001)
model.compile(
loss='binary_crossentropy',
optimizer='adam',
metrics=["accuracy"],)
history = model.fit(X_train, Y_train, batch_size=45, epochs=900)
test_scores = model.evaluate(X_test, Y_test, verbose=2)
print("Test loss:", test_scores[0])
print("Test accuracy:", test_scores[1])
model.save("../AI_Lilly")
return model
But this works just fine:
predicted_seaLevel = []
prediction = model2.predict(
[[np.array([0.21468531468531468])],[np.array([0.4228191581323462])],[np.array([0.17076839761718698])]],
batch_size=None,
verbose='auto',
steps=None,
callbacks=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False
)
predicted_seaLevel.append(prediction)
While:
[[np.array(X[0][0])],[np.array(X[1][0])],[np.array(X[2][0])]] == [[np.array([0.21468531468531468])],[np.array([0.4228191581323462])],[np.array([0.17076839761718698])]]
is true.
I also tried:
X_0 = []
X_1 = []
X_2 = []
for i in range(54):
X_0.append(np.array(X[0][i]))
X_1.append(np.array(X[1][i]))
X_2.append(np.array(X[2][i]))
predicted_seaLevel = []
prediction = model2.predict(
[[X_0[0]],[X_1[0]],[X_2[0]]],
batch_size=None,
verbose='auto',
steps=None,
callbacks=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False
)
predicted_seaLevel.append(prediction)
But it gets me the same error.
I also tried:
predicted_seaLevel = []
prediction = model2.predict(
[X[0][0],X[1][0],X[2][0]],
batch_size=None,
verbose='auto',
steps=None,
callbacks=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False
)
predicted_seaLevel.append(prediction)
But it gets me this error:
Input In [146], in <cell line: 3>()
1 predicted_seaLevel = []
----> 3 prediction = model2.predict(
4 [X[0][0],X[1][0],X[2][0]],
5 batch_size=None,
6 verbose='auto',
7 steps=None,
8 callbacks=None,
9 max_queue_size=10,
10 workers=1,
11 use_multiprocessing=False
12 )
13 predicted_seaLevel.append(prediction)
File ~\.conda\envs\handwritingNumbersAI\lib\site-packages\keras\engine\training.py:1751, in Model.predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
1749 for step in data_handler.steps():
1750 callbacks.on_predict_batch_begin(step)
-> 1751 tmp_batch_outputs = self.predict_function(iterator)
1752 if data_handler.should_sync:
1753 context.async_wait()
File ~\.conda\envs\handwritingNumbersAI\lib\site-packages\tensorflow\python\eager\def_function.py:885, in Function.__call__(self, *args, **kwds)
882 compiler = "xla" if self._jit_compile else "nonXla"
884 with OptionalXlaContext(self._jit_compile):
--> 885 result = self._call(*args, **kwds)
887 new_tracing_count = self.experimental_get_tracing_count()
888 without_tracing = (tracing_count == new_tracing_count)
File ~\.conda\envs\handwritingNumbersAI\lib\site-packages\tensorflow\python\eager\def_function.py:924, in Function._call(self, *args, **kwds)
921 self._lock.release()
922 # In this case we have not created variables on the first call. So we can
923 # run the first trace but we should fail if variables are created.
--> 924 results = self._stateful_fn(*args, **kwds)
925 if self._created_variables and not ALLOW_DYNAMIC_VARIABLE_CREATION:
926 raise ValueError("Creating variables on a non-first call to a function"
927 " decorated with tf.function.")
File ~\.conda\envs\handwritingNumbersAI\lib\site-packages\tensorflow\python\eager\function.py:3038, in Function.__call__(self, *args, **kwargs)
3035 """Calls a graph function specialized to the inputs."""
3036 with self._lock:
3037 (graph_function,
-> 3038 filtered_flat_args) = self._maybe_define_function(args, kwargs)
3039 return graph_function._call_flat(
3040 filtered_flat_args, captured_inputs=graph_function.captured_inputs)
File ~\.conda\envs\handwritingNumbersAI\lib\site-packages\tensorflow\python\eager\function.py:3459, in Function._maybe_define_function(self, args, kwargs)
3449 with ag_ctx.ControlStatusCtx(
3450 status=ag_status, options=self._autograph_options):
3451
(...)
3454 # and 2. there's no provided input signature
3455 # and 3. there's been a cache miss for this calling context
3456 if (self._experimental_relax_shapes and
3457 self.input_signature is None and
3458 call_context_key in self._function_cache.missed):
-> 3459 return self._define_function_with_shape_relaxation(
3460 args, kwargs, flat_args, filtered_flat_args, cache_key_context)
3462 self._function_cache.missed.add(call_context_key)
3463 graph_function = self._create_graph_function(args, kwargs)
File ~\.conda\envs\handwritingNumbersAI\lib\site-packages\tensorflow\python\eager\function.py:3381, in Function._define_function_with_shape_relaxation(self, args, kwargs, flat_args, filtered_flat_args, cache_key_context)
3374 (relaxed_arg_specs, relaxed_kwarg_specs) = nest.pack_sequence_as(
3375 (args, kwargs), relaxed_arg_specs, expand_composites=False)
3376 (args, kwargs) = nest.pack_sequence_as(
3377 (relaxed_arg_specs, relaxed_kwarg_specs),
3378 flat_args,
3379 expand_composites=True)
-> 3381 graph_function = self._create_graph_function(
3382 args, kwargs, override_flat_arg_shapes=relaxed_arg_shapes)
3383 self._function_cache.arg_relaxed[rank_only_cache_key] = graph_function
3385 return (graph_function, [
3386 t for t in nest.flatten((args, kwargs), expand_composites=True)
3387 if isinstance(t, (ops.Tensor,
3388 resource_variable_ops.BaseResourceVariable))
3389 ])
File ~\.conda\envs\handwritingNumbersAI\lib\site-packages\tensorflow\python\eager\function.py:3298, in Function._create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3293 missing_arg_names = [
3294 "%s_%d" % (arg, i) for i, arg in enumerate(missing_arg_names)
3295 ]
3296 arg_names = base_arg_names missing_arg_names
3297 graph_function = ConcreteFunction(
-> 3298 func_graph_module.func_graph_from_py_func(
3299 self._name,
3300 self._python_function,
3301 args,
3302 kwargs,
3303 self.input_signature,
3304 autograph=self._autograph,
3305 autograph_options=self._autograph_options,
3306 arg_names=arg_names,
3307 override_flat_arg_shapes=override_flat_arg_shapes,
3308 capture_by_value=self._capture_by_value),
3309 self._function_attributes,
3310 function_spec=self.function_spec,
3311 # Tell the ConcreteFunction to clean up its graph once it goes out of
3312 # scope. This is not the default behavior since it gets used in some
3313 # places (like Keras) where the FuncGraph lives longer than the
3314 # ConcreteFunction.
3315 shared_func_graph=False)
3316 return graph_function
File ~\.conda\envs\handwritingNumbersAI\lib\site-packages\tensorflow\python\framework\func_graph.py:1007, 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, acd_record_initial_resource_uses)
1004 else:
1005 _, original_func = tf_decorator.unwrap(python_func)
-> 1007 func_outputs = python_func(*func_args, **func_kwargs)
1009 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
1010 # TensorArrays and `None`s.
1011 func_outputs = nest.map_structure(convert, func_outputs,
1012 expand_composites=True)
File ~\.conda\envs\handwritingNumbersAI\lib\site-packages\tensorflow\python\eager\def_function.py:668, in Function._defun_with_scope.<locals>.wrapped_fn(*args, **kwds)
664 with default_graph._variable_creator_scope(scope, priority=50): # pylint: disable=protected-access
665 # __wrapped__ allows AutoGraph to swap in a converted function. We give
666 # the function a weak reference to itself to avoid a reference cycle.
667 with OptionalXlaContext(compile_with_xla):
--> 668 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
669 return out
File ~\.conda\envs\handwritingNumbersAI\lib\site-packages\tensorflow\python\framework\func_graph.py:994, in func_graph_from_py_func.<locals>.wrapper(*args, **kwargs)
992 except Exception as e: # pylint:disable=broad-except
993 if hasattr(e, "ag_error_metadata"):
--> 994 raise e.ag_error_metadata.to_exception(e)
995 else:
996 raise
ValueError: in user code:
C:\Users\FutureSpace\.conda\envs\handwritingNumbersAI\lib\site-packages\keras\engine\training.py:1586 predict_function *
return step_function(self, iterator)
C:\Users\FutureSpace\.conda\envs\handwritingNumbersAI\lib\site-packages\keras\engine\training.py:1576 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\Users\FutureSpace\.conda\envs\handwritingNumbersAI\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1286 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Users\FutureSpace\.conda\envs\handwritingNumbersAI\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2849 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\Users\FutureSpace\.conda\envs\handwritingNumbersAI\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3632 _call_for_each_replica
return fn(*args, **kwargs)
C:\Users\FutureSpace\.conda\envs\handwritingNumbersAI\lib\site-packages\keras\engine\training.py:1569 run_step **
outputs = model.predict_step(data)
C:\Users\FutureSpace\.conda\envs\handwritingNumbersAI\lib\site-packages\keras\engine\training.py:1537 predict_step
return self(x, training=False)
C:\Users\FutureSpace\.conda\envs\handwritingNumbersAI\lib\site-packages\keras\engine\base_layer.py:1020 __call__
input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
C:\Users\FutureSpace\.conda\envs\handwritingNumbersAI\lib\site-packages\keras\engine\input_spec.py:199 assert_input_compatibility
raise ValueError('Layer ' layer_name ' expects '
ValueError: Layer model_18 expects 3 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor 'ExpandDims:0' shape=(None, 1) dtype=float32>]
I really have no idea how to solve and can't explaine it to myself that one thing works but another thing which is equal to the first thing doesn't work.
Please help!
CodePudding user response：
You need to provide 3 inputs to your model, thus your input to predict has to have a shape like:
[(None, 1), (None, 1), (None, 1)]
to solve your problem, just use this:
model.predict(
[
np.array([[X[0][0]],]), # inputs of input1
np.array([[X[1][0]],]), # inputs of input2
np.array([[X[2][0]],]), # inputs of input3
],
...
)