我已经使用带有分组卷积的tensorflow.keras创建了这个计算机视觉模型：
n_filters = [8, 16, 32]
img_dims = (128,572,1)
layer_dims = calculate_dims(img_dims,conv_filters=n_filters)
print('Layer dimensions')
for l in layer_dims:
print(l)
last_neurons = layer_dims[-1][0]*layer_dims[-1][1]*layer_dims[-1][2]
model = Sequential()
model.add(tf.keras.Input(shape=(img_dims)))
model.add(layers.Conv2D(n_filters[0], kernel_size=3, padding = "same", activation = "relu"))
for n in n_filters[1:]:
model.add(layers.Conv2D(n, kernel_size=3, padding = "same", activation = "relu",groups=2))
model.add(layers.MaxPool2D(2))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation="relu"))
model.add(layers.Dense(8, activation="relu"))
model.add(layers.Dense(1, activation='relu'))
return model
这是 model.summary() 输出
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type) ┃ Output Shape ┃ Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv2d (Conv2D) │ (None, 128, 572, 8) │ 80 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_1 (Conv2D) │ (None, 128, 572, 16) │ 592 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d (MaxPooling2D) │ (None, 64, 286, 16) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_2 (Conv2D) │ (None, 64, 286, 32) │ 2,336 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d_1 (MaxPooling2D) │ (None, 32, 143, 32) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ flatten (Flatten) │ (None, 146432) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense) │ (None, 64) │ 9,371,712 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense) │ (None, 8) │ 520 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense) │ (None, 1) │ 9 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
总参数：9,375,249 (35.76 MB) 可训练参数：9,375,249 (35.76 MB) 不可训练参数：0 (0.00 B)
这样训练。
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),
loss="mean_squared_error",
metrics=['root_mean_squared_error', 'mean_absolute_error'],
)
model_history = model.fit(
train_dataset,
validation_data=val_dataset,
batch_size=4,
epochs=2,
verbose=2
)
我在展平层上遇到错误：
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
Cell In[16], line 1
----> 1 model_history = model.fit(
2 train_dataset,
3 validation_data=val_dataset,
4 batch_size=4,
5 epochs=2,
6 # callbacks=callbacks,
7 verbose=2
8 )
9 completed = dt.now()
File ~/torch_cuda12_env/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs)
119 filtered_tb = _process_traceback_frames(e.__traceback__)
120 # To get the full stack trace, call:
121 # `keras.config.disable_traceback_filtering()`
--> 122 raise e.with_traceback(filtered_tb) from None
123 finally:
124 del filtered_tb
File ~/torch_cuda12_env/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:53, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
51 try:
52 ctx.ensure_initialized()
---> 53 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
54 inputs, attrs, num_outputs)
55 except core._NotOkStatusException as e:
56 if name is not None:
InvalidArgumentError: Graph execution error:
Detected at node sequential_1/flatten_1/Reshape defined at (most recent call last):
<stack traces unavailable>
only one input size may be -1, not both 0 and 1
Stack trace for op definition:
File "usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
File "usr/lib/python3.10/runpy.py", line 86, in _run_code
File "/python3.10/site-packages/ipykernel_launcher.py", line 18, in <module>
File "/python3.10/site-packages/traitlets/config/application.py", line 1075, in launch_instance
File "/python3.10/site-packages/ipykernel/kernelapp.py", line 739, in start
File "/python3.10/site-packages/tornado/platform/asyncio.py", line 205, in start
File "usr/lib/python3.10/asyncio/base_events.py", line 603, in run_forever
File "usr/lib/python3.10/asyncio/base_events.py", line 1909, in _run_once
File "usr/lib/python3.10/asyncio/events.py", line 80, in _run
File "/python3.10/site-packages/ipykernel/kernelbase.py", line 545, in dispatch_queue
File "/python3.10/site-packages/ipykernel/kernelbase.py", line 534, in process_one
File "/python3.10/site-packages/ipykernel/kernelbase.py", line 437, in dispatch_shell
File "/python3.10/site-packages/ipykernel/ipkernel.py", line 362, in execute_request
File "/python3.10/site-packages/ipykernel/kernelbase.py", line 778, in execute_request
File "/python3.10/site-packages/ipykernel/ipkernel.py", line 449, in do_execute
File "/python3.10/site-packages/ipykernel/zmqshell.py", line 549, in run_cell
File "/python3.10/site-packages/IPython/core/interactiveshell.py", line 3075, in run_cell
File "/python3.10/site-packages/IPython/core/interactiveshell.py", line 3130, in _run_cell
File "/python3.10/site-packages/IPython/core/async_helpers.py", line 129, in _pseudo_sync_runner
File "/python3.10/site-packages/IPython/core/interactiveshell.py", line 3334, in run_cell_async
File "/python3.10/site-packages/IPython/core/interactiveshell.py", line 3517, in run_ast_nodes
File "/python3.10/site-packages/IPython/core/interactiveshell.py", line 3577, in run_code
File "tmp/ipykernel_97254/668273277.py", line 1, in <module>
File "/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 117, in error_handler
File "/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py", line 329, in fit
File "/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py", line 122, in one_step_on_iterator
File "/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py", line 110, in one_step_on_data
File "/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py", line 57, in train_step
File "/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 117, in error_handler
File "/python3.10/site-packages/keras/src/layers/layer.py", line 826, in __call__
File "/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 117, in error_handler
File "/python3.10/site-packages/keras/src/ops/operation.py", line 48, in __call__
File "/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 156, in error_handler
File "/python3.10/site-packages/keras/src/models/sequential.py", line 206, in call
File "/python3.10/site-packages/keras/src/models/functional.py", line 199, in call
File "/python3.10/site-packages/keras/src/ops/function.py", line 151, in _run_through_graph
File "/python3.10/site-packages/keras/src/models/functional.py", line 583, in call
File "/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 117, in error_handler
File "/python3.10/site-packages/keras/src/layers/layer.py", line 826, in __call__
File "/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 117, in error_handler
File "/python3.10/site-packages/keras/src/ops/operation.py", line 48, in __call__
File "/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 156, in error_handler
File "/python3.10/site-packages/keras/src/layers/reshaping/flatten.py", line 54, in call
File "/python3.10/site-packages/keras/src/ops/numpy.py", line 4527, in reshape
File "/python3.10/site-packages/keras/src/backend/tensorflow/numpy.py", line 1618, in reshape
[[{{node sequential_1/flatten_1/Reshape}}]]
tf2xla conversion failed while converting __inference_one_step_on_data_2376[]. Run with TF_DUMP_GRAPH_PREFIX=/path/to/dump/dir and --vmodule=xla_compiler=2 to obtain a dump of the compiled functions.
[[StatefulPartitionedCall]] [Op:__inference_one_step_on_iterator_2471]
我尝试阅读有关 keras 层的更多信息，但没有任何迹象表明 Flatten 层中存在错误。我还尝试将 input_shape 赋予 Flatten 层为 (32, 143, 32)，但即使这样也没有解决问题。
不确定库版本是否与我第一次遇到的这个错误有关。我正在使用这些库：
这是tensorflow v2.16的问题。降级到 v2.14 后，模型可以正常编译和训练。