我正在研究用于训练的 CNN-LSTM 模型，我的数据集包含 25760 张尺寸为 (70,70,3) 的图像。在训练过程中，我遇到了这个错误:“ValueError:层 conv2d 的输入 0 与层不兼容::预期 min_ndim=4，发现 ndim=3。收到完整形状:(2240, 70, 3)”。
谁能告诉我这是什么意思以及如何解决？
训练数据的形状:(25760,70,70,3)
代码:
import tensorflow as tf
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D, LSTM, TimeDistributed
print(np.shape(X))
u=np.array(X)
v=np.array(y)
model=Sequential()
model.add(TimeDistributed(Conv2D(32,(5,5),padding='same',input_shape=(70,70,3))))
model.add(TimeDistributed(Activation('relu')))
model.add(TimeDistributed(MaxPooling2D(pool_size=(2,2))))
model.add(TimeDistributed(Conv2D(32,(5,5),padding='same')))
model.add(TimeDistributed(Activation('relu')))
model.add(TimeDistributed(MaxPooling2D(pool_size=(2,2))))
model.add(TimeDistributed(Conv2D(64,(5,5),padding='same')))
model.add(TimeDistributed(Activation('relu')))
model.add(TimeDistributed(MaxPooling2D(pool_size=(2,2))))
model.add(TimeDistributed(Conv2D(64,(5,5),padding='same')))
model.add(TimeDistributed(Activation('relu')))
model.add(TimeDistributed(MaxPooling2D(pool_size=(2,2))))
model.add(TimeDistributed(Conv2D(128,(5,5),padding='same')))
model.add(TimeDistributed(Activation('relu')))
model.add(TimeDistributed(MaxPooling2D(pool_size=(2,2))))
model.add(Flatten())
model.add(LSTM(100))
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dense(8))
model.add(Activation('softmax'))
model.compile(loss="sparse_categorical_crossentropy",optimizer="adam",metrics=['accuracy'])
model.fit(u,v,batch_size=32,epochs=20,validation_split=0.2)
model.save('cnn_lstm_1.h5')
回溯:
ValueError Traceback (most recent call last)
<ipython-input-5-c86147b70dfb> in <module>
46 model.compile(loss="sparse_categorical_crossentropy",optimizer="adam",metrics=['accuracy'])
47
---> 48 model.fit(u,v,batch_size=32,epochs=20,validation_split=0.2)
49
50 model.save('cnn_lstm_1.h5')
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1098 _r=1):
1099 callbacks.on_train_batch_begin(step)
-> 1100 tmp_logs = self.train_function(iterator)
1101 if data_handler.should_sync:
1102 context.async_wait()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
826 tracing_count = self.experimental_get_tracing_count()
827 with trace.Trace(self._name) as tm:
--> 828 result = self._call(*args, **kwds)
829 compiler = "xla" if self._experimental_compile else "nonXla"
830 new_tracing_count = self.experimental_get_tracing_count()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
869 # This is the first call of __call__, so we have to initialize.
870 initializers = []
--> 871 self._initialize(args, kwds, add_initializers_to=initializers)
872 finally:
873 # At this point we know that the initialization is complete (or less
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
724 self._concrete_stateful_fn = (
725 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 726 *args, **kwds))
727
728 def invalid_creator_scope(*unused_args, **unused_kwds):
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2967 args, kwargs = None, None
2968 with self._lock:
-> 2969 graph_function, _ = self._maybe_define_function(args, kwargs)
2970 return graph_function
2971
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3359
3360 self._function_cache.missed.add(call_context_key)
-> 3361 graph_function = self._create_graph_function(args, kwargs)
3362 self._function_cache.primary[cache_key] = graph_function
3363
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3204 arg_names=arg_names,
3205 override_flat_arg_shapes=override_flat_arg_shapes,
-> 3206 capture_by_value=self._capture_by_value),
3207 self._function_attributes,
3208 function_spec=self.function_spec,
/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
988 _, original_func = tf_decorator.unwrap(python_func)
989
--> 990 func_outputs = python_func(*func_args, **func_kwargs)
991
992 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
632 xla_context.Exit()
633 else:
--> 634 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
635 return out
636
/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
975 except Exception as e: # pylint:disable=broad-except
976 if hasattr(e, "ag_error_metadata"):
--> 977 raise e.ag_error_metadata.to_exception(e)
978 else:
979 raise
ValueError: in user code:
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:805 train_function *
return step_function(self, iterator)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:795 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica
return fn(*args, **kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:788 run_step **
outputs = model.train_step(data)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:754 train_step
y_pred = self(x, training=True)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py:1012 __call__
outputs = call_fn(inputs, *args, **kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/sequential.py:389 call
outputs = layer(inputs, **kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py:1012 __call__
outputs = call_fn(inputs, *args, **kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/layers/wrappers.py:241 call
y = self.layer(inputs, **kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py:998 __call__
input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/input_spec.py:239 assert_input_compatibility
str(tuple(shape)))
ValueError: Input 0 of layer conv2d is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: (2240, 70, 3)
最佳答案
您正在输入图像序列吗？
如果是这样，那么您的数据集应包含以下维度 批量大小 x 序列长度 x img_width x img_height x channel
例如，大小为 32 且序列长度为 5 的典型批处理将是 32x5x70x70x3
您可以使用 pad_sequences() 函数转换数据集，使数据集形状成为训练批处理形状。请参阅keras pad_sequences
关于python - ValueError : Input 0 of layer conv2d is incompatible with the layer: : expected min_ndim=4, 发现 ndim=3。收到完整形状 : (2240, 70, 3)，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/67345171/