为什么我的model.predict输出2个值而不是1？

model = Sequential()
#model.add(Flatten(input_shape=[139, 28]))
model.add(Dense(32, input_dim=x_train.shape[1], activation='relu'))  #input=number of inputs from data; 32=number of neurons in the layer
model.add(Dropout(0.25))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(8, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(1, activation='softmax'))

model.compile(loss='sparse_categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
model.fit(x_train, y_train, epochs=10, batch_size=64)

print("Generate predictions")
predictions = model.predict(x_train[:1])
print(x_train.shape)
print (predictions)
print("predictions shape:", predictions.shape)`

我得到了以下输出：

Epoch 1/10

InvalidArgumentError Traceback（most recent call last）in<cell line: 16>（）14 model.compile（loss='sparse_categorical_crossentropy'，optimizer = 'adam'，metrics = ['accuracy']）15 # fit model ---> 16 model.fit（x_train，y_train，epochs=10，batch_size=64）17 18 #print（len（model.layers））
1 frames /usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/execute.py in quick_execute（op_name，num_outputs，inputs，attrs，ctx，name）50 try：51 ctx.ensure_initialized（）---> 52 tensors = pywrap_tfe.TFE_Py_Execute（ctx._handle，device_name，op_name，53 inputs，attrs，num_outputs）54 except core._NotOkStatusException as e：
InvalidArgumentError：图形执行错误：

在节点“sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits”处检测到（最近一次调用）：文件"/usr/lib/python3.10/runpy. py "，line 196，in_run_module_as_main return_run_code（code，main_globals，None，File "/usr/lib/python3.10/runpy. py"，line 86，in_run_code exec（code，run_globals）File "/usr/local/lib/python3.10/dist-packages/ipykernel_launcher. py”，第16行，在app中。launch_new_instance（）文件"/usr/local/lib/python3.10/dist-packages/traitlets/config/application. py”，第992行，在launch_instance app中。start（）File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelapp. py”，第619行，在开始www.example.com_loop中。start（）File "/usr/local/lib/python3.10/dist-packages/tornado/platform/asyncio. py”，第195行，在start self中。异步_www.example.com_永远self.ioself._run_callback（functools. partial（callback，future））文件"/usr/local/lib/python3.10/dist-packages/tornado/ioloop. py "，line 738，in_run_callback ret = callback（）File "/usr/local/lib/python3.10/dist-packages/tornado/gen. py"，line 825，in inner self. ctx_run（self.run）文件"/usr/local/lib/python3.10/dist-packages/tornado/gen. py”，第786行，在运行中，yielded = self。gen. send（value）File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase. py”，第361行，在处理中_一个产率生成maybe_future（dispatch（* args））文件"/usr/local/lib/python3.10/dist-packages/tornado/gen. py "，line 234，in wrapper yielded = ctx_run（next，result）File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase. py”，第261行，在dispatch_shell yield gen中。maybe_future（handler（stream，idents，msg））文件"/usr/local/lib/python3.10/dist-packages/tornado/gen. py"，line 234，in wrapper yielded = ctx_run（next，result）File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase. py"，第539行，在execute_request中loop.run www.example.com_ast_nodes（code_ast. body，cell_name，File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell. py "，line 3473，in run_ast_nodes if（await www.example.com_code（code，result，async = asy））：文件"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell. py”，第3553行，在run_code exec（code_obj，self. user_global_ns，self. user_ns）文件""，第16行，在www.example.com（x_train，y_train，epochs = 10，batch_size = 64）文件"/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils. py "，line 65，in error_handler return fn（ args，kwargs）File "/usr/local/lib/python3.10/dist-packages/keras/engine/training. py”，第1685行，在fit tmp_logs = self中。train_function（iterator）文件"/usr/local/lib/python3.10/dist-packages/keras/engine/training. py"，line 1284，in train_function return step_function（self，iterator）File "/usr/local/lib/python3.10/dist-packages/keras/engine/training. py”，第1268行，在step_function outputs = model中。分布www.example.com（run_step，args =（data，））File "/usr/local/lib/python3.10/dist-packages/keras/engine/training. py”，第1249行，在run_step outputs = model中。train_step（data）文件"/usr/local/lib/python3.10/dist-packages/keras/engine/training. py”，第1051行，在train_step loss = self中。compute_loss（x，y，y_pred，sample_weight）文件"/usr/local/lib/python3.10/dist-packages/keras/engine/training. py”，第1109行，在compute_loss中返回self。compiled_loss（File "/usr/local/lib/python3.10/dist-packages/keras/engine/compile_utils. py "，line 265，incallloss_value = loss_obj（y_t，y_p，sample_weight = sw）File "/usr/local/lib/python3.10/dist-packages/keras/losses. py"，line 142，incalllosses = call_fn（y_true，y_pred）File "/usr/local/lib/python3.10/dist-packages/keras/losses. py"，第268行，在调用return ag_fn（y_true，y_pred，self._fn_kwargs）文件"/usr/local/lib/python3.10/dist-packages/keras/losses. py"，line 2078，in sparse_categorical_crossentropy return backend. sparse_categorical_crossentropy（File "/usr/local/lib/python3.10/dist-packages/keras/backend. py”，第5660行，在sparse_categorical_crossentropy res = tf中。nn. sparse_softmax_cross_entropy_with_logits（节点： www.example.com 1 1 0 1 1 0 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 shell.run_cell(code, store_history=store_history, silent=silent) File "/usr/local/lib/python3.10/dist-packages/ipykernel/zmqshell.py", line 539, in run_cell return super(ZMQInteractiveShell, self).run_cell(*args,kwargs) File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 2975, in run_cell result = self._run_cell( File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3030, in run_cell return runner(coro) File "/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py", line 78, in pseudo_sync_runner coro.send(None) File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3257, in run_cell_async has_raised = await www.example.com _ast_nodes(code_ast.body, cell_name, File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3473, in run_ast_nodes if (await self.run_code(code, result, async=asy)): File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3553, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "", line 16, in <cell line: 16>model.fit (x_train, y_train, epochs=10, batch_size=64) File "/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py", line 65, in error_handler return fn(*args,**kwargs) File "/usr/local/lib/python3.10/dist-packages/keras/engine/training.py", line 1685, in fit tmp_logs = self.train_function(iterator) File "/usr/local/lib/python3.10/dist-packages/keras/engine/training.py", line 1284, in train_function return step_function(self, iterator) File "/usr/local/lib/python3.10/dist-packages/keras/engine/training.py", line 1268, in step_function outputs = model.distribute www.example.com (run_step, args=(data,)) File "/usr/local/lib/python3.10/dist-packages/keras/engine/training.py", line 1249, in run_step outputs = model.train_step(data) File "/usr/local/lib/python3.10/dist-packages/keras/engine/training.py", line 1051, in train_step loss = self.compute_loss(x, y, y_pred, sample_weight) File "/usr/local/lib/python3.10/dist-packages/keras/engine/training.py", line 1109, in compute_loss return self.compiled_loss( File "/usr/local/lib/python3.10/dist-packages/keras/engine/compile_utils.py", line 265, incallloss_value = loss_obj(y_t, y_p, sample_weight=sw) File "/usr/local/lib/python3.10/dist-packages/keras/losses.py", line 142, incall**losses = call_fn(y_true, y_pred) File "/usr/local/lib/python3.10/dist-packages/keras/losses.py", line 268, in call return ag_fn(y_true, y_pred,**self._fn_kwargs) File "/usr/local/lib/python3.10/dist-packages/keras/losses.py", line 2078, in sparse_categorical_crossentropy return backend.sparse_categorical_crossentropy( File "/usr/local/lib/python3.10/dist-packages/keras/backend.py", line 5660, in sparse_categorical_crossentropy res = tf.nn.sparse_softmax_cross_entropy_with_logits( Node: 'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits' Received a label value of 1 which is outside the valid range of [0, 1). Label values: 1 1 0 1 0 1 1 0 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 0 1 1 0 1 1 1 0 0 1 0 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 0 {{node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits}} [Op:__inference_train_function_3244]