Why my model.predict outputs 2 values instead of 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)`
And I got the following output:
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: Graph execution error:
Detected at node 'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits' defined at (most recent call last): File "/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", line 16, in app.launch_new_instance() File "/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py", line 992, in launch_instance app.start() File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelapp.py", line 619, in start self.io_loop.start() File "/usr/local/lib/python3.10/dist-packages/tornado/platform/asyncio.py", line 195, in start self.asyncio_loop.run_forever() File "/usr/lib/python3.10/asyncio/base_events.py", line 603, in run_forever self._run_once() File "/usr/lib/python3.10/asyncio/base_events.py", line 1909, in _run_once handle._run() File "/usr/lib/python3.10/asyncio/events.py", line 80, in _run self._context.run(self._callback, *self._args) File "/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py", line 685, in lambda f: self._run_callback(functools.partial(callback, future)) File "/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) File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 786, in run yielded = self.gen.send(value) File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py", line 361, in process_one yield gen.maybe_future(dispatch(*args)) File "/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", line 261, in dispatch_shell yield gen.maybe_future(handler(stream, idents, msg)) File "/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", line 539, in execute_request self.do_execute( File "/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/ipkernel.py", line 302, in do_execute res = 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 self.run_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_strategy.run(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, in call loss_value = loss_obj(y_t, y_p, sample_weight=sw) File "/usr/local/lib/python3.10/dist-packages/keras/losses.py", line 142, in call 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]
You have 2 units in last SoftMax layer. So, there will be 2 outputs.
0.36325565and0.6367443are two different outputs.