我有 3 个班级（A、B 和 C）。
我有 6 个功能：
train_x = [[ 6.442 6.338 7.027 8.789 10.009 12.566]
[ 6.338 7.027 5.338 10.009 8.122 11.217]
[ 7.027 5.338 5.335 8.122 5.537 6.408]
[ 5.338 5.335 5.659 5.537 5.241 7.043]]
这些特征表示由 3 个类（例如 AABBC 等）组成的 5 个字符的字符串模式。
让，一个 5 字符的字符串模式被单热编码如下：
train_z = [[0. 0. 1. 0. 0. 1. 0. 0. 1. 0. 0. 1. 1. 0. 0.]
[0. 0. 1. 0. 0. 1. 0. 0. 1. 1. 0. 0. 1. 0. 0.]
[0. 0. 1. 0. 0. 1. 1. 0. 0. 1. 0. 0. 1. 0. 0.]
[0. 0. 1. 1. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1.]]
我认为，这是一个Multi-task learning problem。
因此，我编写了以下源代码：
# there would be 6 inputs for 6 features
inputs_tensor = keras.Input(shape=(FEATURES_COUNT,))
# there would be 2 hidden layers
hidden_layer_1 = keras.layers.Dense(LAYER_1_NEURON_COUNT, activation="relu")
hidden_layer_2 = keras.layers.Dense(LAYER_2_NEURON_COUNT, activation="relu")
# there would be 15 outputs for 15-bits
# each o/p layer will have 1 neurons for binary data
output_layer_1 = keras.layers.Dense(1, activation='sigmoid') # 1 neuraons for 1 output
output_layer_2 = keras.layers.Dense(1, activation='sigmoid') # -do-
output_layer_3 = keras.layers.Dense(1, activation='sigmoid') # -do-
output_layer_4 = keras.layers.Dense(1, activation='sigmoid') # -do-
output_layer_5 = keras.layers.Dense(1, activation='sigmoid') # -do-
output_layer_6 = keras.layers.Dense(1, activation='sigmoid') # -do-
output_layer_7 = keras.layers.Dense(1, activation='sigmoid') # 1 neuraons for 1 output
output_layer_8 = keras.layers.Dense(1, activation='sigmoid') # -do-
output_layer_9 = keras.layers.Dense(1, activation='sigmoid') # -do-
output_layer_10 = keras.layers.Dense(1, activation='sigmoid') # -do-
output_layer_11 = keras.layers.Dense(1, activation='sigmoid') # -do-
output_layer_12 = keras.layers.Dense(1, activation='sigmoid') # -do-
output_layer_13 = keras.layers.Dense(1, activation='sigmoid') # -do-
output_layer_14 = keras.layers.Dense(1, activation='sigmoid') # -do-
output_layer_15 = keras.layers.Dense(1, activation='sigmoid') # -do-
# assembling the layers.
x = hidden_layer_1(inputs_tensor)
x = hidden_layer_2(x)
# configuring the output
output1 = output_layer_1(x)
output2 = output_layer_2(x)
output3 = output_layer_3(x)
output4 = output_layer_4(x)
output5 = output_layer_5(x)
output6 = output_layer_6(x)
output7 = output_layer_7(x)
output8 = output_layer_8(x)
output9 = output_layer_9(x)
output10 = output_layer_10(x)
output11 = output_layer_11(x)
output12 = output_layer_12(x)
output13 = output_layer_13(x)
output14 = output_layer_14(x)
output15 = output_layer_15(x)
model = keras.Model(inputs=[inputs_tensor],
outputs=[output1, output2, output3, output4, output5,
output6, output7, output8, output9, output10,
output11, output12, output13, output14, output15],
name="functional_model")
model.summary()
print("Inputs count : ", model.inputs)
print("Outputs count : ", len(model.outputs))
opt_function = keras.optimizers.SGD(lr=0.01, decay=1e-1, momentum=0.9, nesterov=True)
#
model.compile(loss='binary_crossentropy',
optimizer=opt_function,
metrics=['accuracy'])
#
print(train_x,"\n",train_z)
model.fit(
train_x, train_z,
epochs=EPOCHS,
batch_size=BATCH_SIZE
)
产生错误：
Traceback (most recent call last):
File "C:/Users/pc/source/repos/OneHotEncodingLayer__test/ny_nn___k_15_outputs.py", line 117, in <module>
model.fit(
File "C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\keras\engine\training.py", line 108, in _method_wrapper
return method(self, *args, **kwargs)
File "C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1098, in fit
tmp_logs = train_function(iterator)
File "C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\eager\def_function.py", line 780, in __call__
result = self._call(*args, **kwds)
File "C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\eager\def_function.py", line 823, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\eager\def_function.py", line 696, in _initialize
self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
File "C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\eager\function.py", line 2855, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\eager\function.py", line 3213, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\eager\function.py", line 3065, in _create_graph_function
func_graph_module.func_graph_from_py_func(
File "C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\framework\func_graph.py", line 986, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\eager\def_function.py", line 600, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File "C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\framework\func_graph.py", line 973, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\keras\engine\training.py:806 train_function *
return step_function(self, iterator)
C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\keras\engine\training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\keras\engine\training.py:789 run_step **
outputs = model.train_step(data)
C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\keras\engine\training.py:748 train_step
loss = self.compiled_loss(
C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:204 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\keras\losses.py:149 __call__
losses = ag_call(y_true, y_pred)
C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\keras\losses.py:253 call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
return target(*args, **kwargs)
C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\keras\losses.py:1605 binary_crossentropy
K.binary_crossentropy(y_true, y_pred, from_logits=from_logits), axis=-1)
C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
return target(*args, **kwargs)
C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\keras\backend.py:4823 binary_crossentropy
return nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output)
C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
return target(*args, **kwargs)
C:\ProgramData\Miniconda3\envs\by_nn\lib\site-packages\tensorflow\python\ops\nn_impl.py:173 sigmoid_cross_entropy_with_logits
raise ValueError("logits and labels must have the same shape (%s vs %s)" %
ValueError: logits and labels must have the same shape ((1, 1) vs (1, 15))
Process finished with exit code 1
答案 0 :(得分：1)
出现错误是因为模型期望一个具有 15 个单位的输出层
output_layer = keras.layers.Dense(15, activation='sigmoid')
无论如何，这不是实现模型的正确方法，因为您当前的模型忽略了所有连续的 3 个输出值组都相互关联的事实，因为标签是互斥的。
例如，如果模型预测由 15 个值 [1, 1, 1, ..., 1, 1, 1] 组成的输出 1，则无法决定是否为每个字符分配 A、B 或 C 类。
您的输出应该是 5 层，并使用分类交叉熵损失训练 softmax 激活（每个输出一个）。标签应适应这种形式的输出，将标签 train_z 拆分为 5 个 (N, 3) 形矩阵的列表。
下面的代码应该比您当前的模型更有效。
inputs_tensor = keras.Input(shape=(FEATURES_COUNT,))
# there would be 2 hidden layers
hidden_layer_1 = keras.layers.Dense(LAYER_1_NEURON_COUNT, activation="relu")
hidden_layer_2 = keras.layers.Dense(LAYER_2_NEURON_COUNT, activation="relu")
output_layer_char1 = keras.layers.Dense(3, activation='softmax')
output_layer_char2 = keras.layers.Dense(3, activation='softmax')
output_layer_char3 = keras.layers.Dense(3, activation='softmax')
output_layer_char4 = keras.layers.Dense(3, activation='softmax')
output_layer_char5 = keras.layers.Dense(3, activation='softmax')
x = hidden_layer_1(inputs_tensor)
x = hidden_layer_2(x)
# configuring the output
output1 = output_layer_char1(x)
output2 = output_layer_char2(x)
output3 = output_layer_char3(x)
output4 = output_layer_char4(x)
output5 = output_layer_char5(x)
model = keras.Model(inputs=[inputs_tensor],
outputs=[output1, output2, output3, output4, output5],
name="functional_model")
opt_function = keras.optimizers.SGD(learning_rate=0.01, decay=1e-1, momentum=0.9, nesterov=True)
model.compile(loss=['categorical_crossentropy']*5,
optimizer=opt_function,
metrics=[['accuracy']]*5)
model.fit(
train_x, tf.split(train_z, 5, axis=1),
epochs=EPOCHS,
batch_size=BATCH_SIZE
)
答案 1 :(得分：0)
所以我查看了您的代码，发现 data_input_k 包含 5 个观察值。 . .你只是没有足够的数据。神经网络只有在您拥有大型数据集或数据集中的效果非常明显时才能真正发挥作用（在这种情况下，您一开始就永远不会使用神经网络）。