这里是深度学习新手。如果 Mango 和 Guava 是否患病（4 类），我正在尝试在 google collab 上创建一个用于多类分类的迁移学习模型。我正在使用 Resnetv50。我也做了一些数据扩充。
代码如下：
train_datagen = ImageDataGenerator(
rescale=1./255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
val_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(224, 224),
batch_size=20,
class_mode='categorical'
)
val_generator = val_datagen.flow_from_directory(
val_dir,
target_size=(224, 224),
batch_size=20,
class_mode='categorical'
)
pretrained_model = ResNet50(include_top = False,
input_shape = (224, 224, 3),
pooling = 'max',
classes = 4,
weights = 'imagenet')
for layer in pretrained_model.layers:
layers.trainable = False
model = Sequential()
model.add(pretrained_model)
model.add(Flatten())
model.add(layers.Dropout(0.5))
model.add(Dense(256, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(Dense(4, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',
optimizer=optimizers.Adam(learning_rate=1e-3),
metrics=['acc'])
cp_cb = keras.callbacks.ModelCheckpoint("model.h5", save_best_only= True)
然后当我执行这个
history = model.fit(
train_generator,
validation_data=val_generator,
epochs=32,
callbacks=[cp_cb],
)
我得到这个错误：
Epoch 1/32
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-104-ab26bc76f2cb> in <module>
----> 1 history = model.fit(
2 train_generator,
3 validation_data=val_generator,
4 epochs=32,
5 callbacks=[cp_cb],
1 frames
/usr/local/lib/python3.8/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/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.8/dist-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/local/lib/python3.8/dist-packages/traitlets/config/application.py", line 992, in launch_instance
app.start()
File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelapp.py", line 612, in start
self.io_loop.start()
File "/usr/local/lib/python3.8/dist-packages/tornado/platform/asyncio.py", line 215, in start
self.asyncio_loop.run_forever()
File "/usr/lib/python3.8/asyncio/base_events.py", line 570, in run_forever
self._run_once()
File "/usr/lib/python3.8/asyncio/base_events.py", line 1859, in _run_once
handle._run()
File "/usr/lib/python3.8/asyncio/events.py", line 81, in _run
self._context.run(self._callback, *self._args)
File "/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py", line 687, in <lambda>
lambda f: self._run_callback(functools.partial(callback, future))
File "/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py", line 740, in _run_callback
ret = callback()
File "/usr/local/lib/python3.8/dist-packages/tornado/gen.py", line 821, in inner
self.ctx_run(self.run)
File "/usr/local/lib/python3.8/dist-packages/tornado/gen.py", line 782, in run
yielded = self.gen.send(value)
File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 365, in process_one
yield gen.maybe_future(dispatch(*args))
File "/usr/local/lib/python3.8/dist-packages/tornado/gen.py", line 234, in wrapper
yielded = ctx_run(next, result)
File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 268, in dispatch_shell
yield gen.maybe_future(handler(stream, idents, msg))
File "/usr/local/lib/python3.8/dist-packages/tornado/gen.py", line 234, in wrapper
yielded = ctx_run(next, result)
File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 543, in execute_request
self.do_execute(
File "/usr/local/lib/python3.8/dist-packages/tornado/gen.py", line 234, in wrapper
yielded = ctx_run(next, result)
File "/usr/local/lib/python3.8/dist-packages/ipykernel/ipkernel.py", line 306, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.8/dist-packages/ipykernel/zmqshell.py", line 536, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2854, in run_cell
result = self._run_cell(
File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2881, in _run_cell
return runner(coro)
File "/usr/local/lib/python3.8/dist-packages/IPython/core/async_helpers.py", line 68, in _pseudo_sync_runner
coro.send(None)
File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3057, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3249, in run_ast_nodes
if (await self.run_code(code, result, async_=asy)):
File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3326, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-104-ab26bc76f2cb>", line 1, in <module>
history = model.fit(
File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1650, in fit
tmp_logs = self.train_function(iterator)
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1249, in train_function
return step_function(self, iterator)
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1233, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1222, in run_step
outputs = model.train_step(data)
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1024, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1082, in compute_loss
return self.compiled_loss(
File "/usr/local/lib/python3.8/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/python3.8/dist-packages/keras/losses.py", line 152, in __call__
losses = call_fn(y_true, y_pred)
File "/usr/local/lib/python3.8/dist-packages/keras/losses.py", line 284, in call
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "/usr/local/lib/python3.8/dist-packages/keras/losses.py", line 2098, in sparse_categorical_crossentropy
return backend.sparse_categorical_crossentropy(
File "/usr/local/lib/python3.8/dist-packages/keras/backend.py", line 5633, in sparse_categorical_crossentropy
res = tf.nn.sparse_softmax_cross_entropy_with_logits(
Node: 'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits'
logits and labels must have the same first dimension, got logits shape [20,4] and labels shape [80]
[[{{node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits}}]] [Op:__inference_train_function_371261]
我尝试改变很多东西但仍然面临这个错误。我看到其他堆栈溢出帖子但没有帮助。
另外，当我在 train_generator 和 val_generator 中将 class_mode 更改为 binary 时，我的模型运行但验证精度低。但我不认为将 class_mode 更改为 Binary 是正确的，因为我正在进行多类分类。