我正在使用 Tensorflow 进行ML项目，该项目使用学生数据预测被学校录取的机会。我遇到了一个我无法理解的问题（我相信这与输入形状有关）。这是我的代码和我正在使用的数据集。
数据集： https://www.kaggle.com/mohansacharya/graduate-admissions
代码：
import numpy as np
import tensorflow as tf
import keras
import pandas as pd
data = pd.read_csv("Admission_Predict.csv")
df = pd.DataFrame(data)
X = df.drop(df.columns[[0, -1]], axis=1).values
y = df[df.columns[-1]].values
model = keras.Sequential([
keras.layers.Flatten(input_shape=(500, 7)),
keras.layers.Dense(32),
keras.layers.Dense(1)
])
model.compile(optimizer="adam",
loss="mse",
metrics=["accuracy"])
model.fit(X, y, epochs=10)
错误：
Epoch 1/10
WARNING:tensorflow:Model was constructed with shape (None, 500, 7) for input Tensor("flatten_14_input:0", shape=(None, 500, 7), dtype=float32), but it was called on an input with incompatible shape (None, 7).
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-160-e60f87ea5845> in <module>
----> 1 model.fit(X, y, epochs=10)
~\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
64 def _method_wrapper(self, *args, **kwargs):
65 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
---> 66 return method(self, *args, **kwargs)
67
68 # Running inside `run_distribute_coordinator` already.
~\AppData\Local\Programs\Python\Python38\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)
846 batch_size=batch_size):
847 callbacks.on_train_batch_begin(step)
--> 848 tmp_logs = train_function(iterator)
849 # Catch OutOfRangeError for Datasets of unknown size.
850 # This blocks until the batch has finished executing.
~\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
578 xla_context.Exit()
579 else:
--> 580 result = self._call(*args, **kwds)
581
582 if tracing_count == self._get_tracing_count():
~\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
609 # In this case we have created variables on the first call, so we run the
610 # defunned version which is guaranteed to never create variables.
--> 611 return self._stateless_fn(*args, **kwds) # pylint: disable=not-callable
612 elif self._stateful_fn is not None:
613 # Release the lock early so that multiple threads can perform the call
~\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\function.py in __call__(self, *args, **kwargs)
2417 """Calls a graph function specialized to the inputs."""
2418 with self._lock:
-> 2419 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
2420 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
2421
~\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs)
2772 and self.input_signature is None
2773 and call_context_key in self._function_cache.missed):
-> 2774 return self._define_function_with_shape_relaxation(args, kwargs)
2775
2776 self._function_cache.missed.add(call_context_key)
~\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\function.py in _define_function_with_shape_relaxation(self, args, kwargs)
2703 self._function_cache.arg_relaxed_shapes[rank_only_cache_key] = (
2704 relaxed_arg_shapes)
-> 2705 graph_function = self._create_graph_function(
2706 args, kwargs, override_flat_arg_shapes=relaxed_arg_shapes)
2707 self._function_cache.arg_relaxed[rank_only_cache_key] = graph_function
~\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2655 arg_names = base_arg_names + missing_arg_names
2656 graph_function = ConcreteFunction(
-> 2657 func_graph_module.func_graph_from_py_func(
2658 self._name,
2659 self._python_function,
~\AppData\Local\Programs\Python\Python38\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)
979 _, original_func = tf_decorator.unwrap(python_func)
980
--> 981 func_outputs = python_func(*func_args, **func_kwargs)
982
983 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
~\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds)
439 # __wrapped__ allows AutoGraph to swap in a converted function. We give
440 # the function a weak reference to itself to avoid a reference cycle.
--> 441 return weak_wrapped_fn().__wrapped__(*args, **kwds)
442 weak_wrapped_fn = weakref.ref(wrapped_fn)
443
~\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs)
966 except Exception as e: # pylint:disable=broad-except
967 if hasattr(e, "ag_error_metadata"):
--> 968 raise e.ag_error_metadata.to_exception(e)
969 else:
970 raise
ValueError: in user code:
C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py:571 train_function *
outputs = self.distribute_strategy.run(
C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:951 run **
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2290 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2649 _call_for_each_replica
return fn(*args, **kwargs)
C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py:531 train_step **
y_pred = self(x, training=True)
C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:927 __call__
outputs = call_fn(cast_inputs, *args, **kwargs)
C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\engine\sequential.py:277 call
return super(Sequential, self).call(inputs, training=training, mask=mask)
C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\engine\network.py:717 call
return self._run_internal_graph(
C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\engine\network.py:888 _run_internal_graph
output_tensors = layer(computed_tensors, **kwargs)
C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:885 __call__
input_spec.assert_input_compatibility(self.input_spec, inputs,
C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\engine\input_spec.py:212 assert_input_compatibility
raise ValueError(
ValueError: Input 0 of layer dense_26 is incompatible with the layer: expected axis -1 of input shape to have value 3500 but received input with shape [None, 7]
答案 0 :(得分：0)
您的数据仍为2D，您无需使用平坦的图层。您的模型必须是：
print(list(db.mycollection.find()))
还要注意您指定的输入维度...仅传递要素维度，而不传递样本维度
最后一点：如果您的问题是回归任务，那么准确度就不是合适的指标